iafor

journal of education

technology in education
Volume 12 – Issue 2 – 2024
Editor: Michael P. Menchaca

ISSN: 2187-0594

iafor
IAFOR Journal of Education: Language Learning in Education
Volume 12 – Issue 2 – 2024

IAFOR Publications
The International Academic ForumIAFOR Journal of Education: Technology in Education
EditorMichael P. Menchaca
University of Hawai‘i at Mānoa
Associate Editor
Daniel L. Hoffman
University of Hawai‘i at Mānoa
Associate Editor
Devayani Tirthali
Research Associate, India

Published by The International Academic Forum (IAFOR), Japan
IAFOR Publications, Sakae 1-16-26-201, Naka-ward, Aichi, Japan 460-0008

IAFOR Publications Assistant: Mark Kenneth Camiling
Publications Manager: Nick Potts
IAFOR Journal of Education: Technology in Education
Volume 12– Issue 2 – 2024
Published August, 2024
IAFOR Publications © Copyright 2024
ISSN: 2187-0594
ije.iafor.org

Cover Image: Andy Kelly, Unsplash

IAFOR Journal of Education: Technology in Education
Volume 12 – Issue 2 – 2024
Edited by Michael P. Menchaca
Associate Editor: Daniel L. Hoffman
Associate Editor: Devayani Tirthali

Table of Contents
From the Editors
Devayani Tirthali, Associate Editor
Daniel L. Hoffman, Associate Editor
Michael P. Menchaca, Editor

1

Notes on Contributors

4

Factors Influencing Teachers’ Use of Artificial Intelligence for
Instructional Purposes
Mukhammadfoik Bakhadirov
Rena Alasgarova
Jeyhun Rzayev

9

Perception of ChatGPT Usage for Homework Assignments: Students’ andProfessors’ Perspectives
Irena Miljkovic Krecar
Maja Kolega
Lana Jurcec

33

Developing a Course on Human-Centered Machine Learning-Based
Product Design
Eui-Chul Jung
Meile Le

61

Internationalized Students: Examining Students’ Experiences in Virtual
Study Abroad Programs in Japan
Ariunaa Enkhtur
Ming Li

83

Using AI-Powered Speech Recognition Technology to Improve English
Pronunciation and Speaking Skills
Nutprapha K. Dennis

107

Academic and Social Adjustment to Post-Pandemic Hybrid Learning:
A Phenomenological Study of Filipino First-Year Engineering Students’
Experiences
Maria Cristina M. Firmante

127

The Influence of E-Comics on English Lexical Competence in Virtual
Higher Education
Norma Flores-González
Vianey Castelán Flores
Mónica Zamora Hernández

149

Reviewers

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IAFOR Journal of Education: Technology in Education

Volume 12 – Issue 2 – 2024

From the Editors
The editors of IAFOR’s Journal of Education: Technology in Education welcome you to peruse
this issue’s collection of articles exploring technology’s continued influence of education! Most
notably, a post-pandemic increased comfort with technology-mediated virtual educationalsettings coupled with the rapid development of AI has moved the field of education to a newnormal. The articles in this special issue engage with the challenges and opportunities broughton by this shift as well as the need for newer conceptualisations.
Four of the articles underscore the evolving role of AI and Machine Learning (ML) in
education, the opportunities and challenges such evolution poses to traditional teaching andassessment, and the need to equip students and educators with skills necessary to navigate thecomplexities of AI-driven innovation. The remaining three articles discuss implications of “thevirtual,” ranging from the experience of first year students in a hybrid course to technopedagogical models for virtual Language learning classrooms to shifting conceptualisations ofinternational students in the age of virtual student exchange programs.
The articles examine these aspects of technology in education using a diverse array of methods
including experimental design with intervention, exploratory qualitative inquiry,
phenomenological investigation, surveys, and design research. Continuing the diversity of
global engagement, this year we offer articles written by authors spanning countries from east
to west including South Korea, Japan, Thailand, The Philippines, Uzbekistan, Azerbaijan,
Croatia, and Mexico. Thus, supporting IAFOR’s commitment encouraging interdisciplinary
discussion, facilitating intercultural awareness, and promoting international exchange, this year
we present the following seven thought provoking articles.
The first article integrates two theoretical models to provide a nuanced analysis of factors
influencing AI adoption in instructional practices by private school teachers in Azerbaijan. The
study will be of interest to educators, school administrators, and policymakers as it explores
factors such as individual teacher characteristics, perceived usefulness, ease of use, and thebroader institutional environment in shaping educators’ willingness to integrate AI in theirpractice. Bakhadirov, Alasgarova, and Rzayev argue that a supportive policy environment anda culture promoting AI usage among colleagues are pivotal in driving adoption, often more sothan individual innovativeness or openness to new experiences.
Article two examines the ethical conundrum of using ChatGPT for writing homeworkassignments in university settings. Krecar, Kolega, and Jurcec first investigate Croatianstudents’ attitudes, usage patterns, and ethical considerations about using ChatGPT for writing
homework assignments. Second, they assess professors’ abilities to distinguish between
student-written and AI-generated content, juxtaposing that with their perception of the efficacy
to do so. Although the study found most students’ ethical compass intact, professors’ lack ofefficacy to differentiate between student and AI-generated content was concerning. The authors
provide a substantive discussion about the way forward for upholding academic integrity giventhese realities.

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Focused on instructional design, the third article delves into how future designers can be
educated to effectively utilize ML technologies while maintaining a strong focus on usercentric design principles. ML applications touted to enhance user experience (UX) acrossvarious platforms have failed in reality, often due to a misalignment between technologicalcapabilities and actual user needs. The seven-week course developed by Jung and Lee seeks tobridge this gap by fostering a more holistic approach to ML in UX design, where the humanelement is not just considered but prioritized. The authors provide details of the methodology
and course structure, offering valuable insights for educators looking to incorporate criticalthinking and the cultivation of innovation in their design curricula.
The fourth article focuses on the significant transformation in the concept of an “international
student” brought on by the advent of virtual study abroad programs. Enkhtur and Li highlightthe tension between the traditional definitions of mobility, often linked to physical movement
across borders, and the new realities of virtual student exchanges that allow for cross-culturalcommunication and knowledge sharing without the need for physical travel. With the backdropof the policy pivot from the Japanese government for internationalization of education, theauthors, based on student narratives, urge to re-frame discourse in policy circles and university
spaces to “internationalization at home.”
The next article investigates the use of AI-powered speech recognition technology to improve
English pronunciation and speaking skills of EFL learners. By examining both the measurable
impact on language skills and students’ subjective experiences, this study provides valuable
insights into the potential of AI-assisted language learning. Dennis situates the study withincurrent debates around the role of AI in language acquisition and pedagogy with implications
for language educators, curriculum designers, and developers.
Article six explores the timely topic of how first-year engineering students in The Philippines
adjust academically and socially to hybrid learning environments in the post-pandemic era. As
universities transition back to more in-person instruction while retaining some online
components, understanding students’ experiences with this new modality is crucial for
supporting their success. Firmante uses a phenomenological approach to give primacy tostudent voices through focus groups, observations, and student essays. Although the hybrid
modality seems inevitable for flexible and inclusive learning, the author, as a practicing school
counsellor, highlights the need for universities to better structure hybrid programs and provide
targeted support services to ease first-year students’ transition.
The final article focuses on e-comic strips as a discursive technique for English language
learning in virtual classrooms, specifically to enhance lexical competence. Flores-González,Flores, and Hernández describe their techno-pedagogical model mediated by three applications
coupled with various individual and collaborative activities, designed for the experimental
study. The authors assess the impact of their model based on standardised tests of lexicalcompetency as well as student perceptions of how well the applications fulfil 18 criteria ofTechnique Feature Analysis. The study found that use of e-comics enhanced creativity,

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language involvement, and confidence, facilitating a transition from memorization of word lists
to extension of lexicon and application in different communicative contexts.
Overall, given the quality and diversity of articles, we hope you find them thought provoking
and inspiring as we navigate the new realities of technology in education.
Devayani Tirthali, Associate Editor
Daniel L. Hoffman, Associate Editor, andMichael P. Menchaca, Editor
IAFOR Journal of Education: Technology in Educationtech.editor.joe@iafor.org

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Notes on Contributors
Article 1
Factors Influencing Teachers’ Use of Artificial Intelligence for Instructional Purposes
Dr Mukhammadfoik Bakhadirov
Mukhammadfoik Bakhadirov is an Associate Professor at School of Business and Economics,
Westminster International University in Tashkent, Uzbekistan. He is an experienced education
professional with a demonstrated history of working in higher education in CIS countries.
Mukhammadfoik Bakhadirov has been a lead for multiple programs in Business andManagement at graduate and undergraduate levels, and managed projects relating toEntrepreneurship and SME development. Mukhammadfoik Bakhadirov obtained his terminal
degree from Maastricht School of Management, Maastricht University, graduate degree from
University of Westminster and undergraduate degree from Cairo University. The scope of his
publications and research interests include SME, Entrepreneurship, Leadership, Management,
Organizational Behavior, HR Management and Education.
Email: foiqxon@gmail.com
Dr Rena Alasgarova
Rena Alasgarova is the Deputy Academic Principal for Secondary School at Baku Oxford
School, Azerbaijan. She is currently pursuing her second PhD at Charles University in Prague,
where she investigates first language attrition in academic settings. Dr Alasgarova graduatedfrom the Azerbaijan University of Languages with a BA in English Language Philology andsubsequently earned her MA in TESOL and PhD in International Leadership from Horizons
University, Paris. Throughout her career, she has received numerous international teaching
diplomas and certificates and has presented at various educational conferences worldwide.
Rena is also an active member of the twelfth European Digital Education Hub squad, which is
focused on the exploration of explainable artificial intelligence in education.
Email: rena.alasgarova@bakuoxfordschool.net
Dr Jeyhun Rzayev
Jeyhun Rzayev is a cultural anthropologist with a historical background. He earned hisbachelor's and master’s degrees from Baku State University and completed his PhD in 2014.
Currently, he is an Assistant Professor at ADA University in Baku, Azerbaijan, where heteaches the course “Civilizations and Cultures”. Dr. Rzayev is deeply engaged in academic
research, focusing on AI in educational settings. He is the author of two guidelines and 25
scientific articles, and co-author of four books. Additionally, Dr. Rzayev serves as a reviewer
for the IAFOR Journal of Education: Studies in Education and The International Journal ofTechnology in Education and Science.
Email: jrzayev@ada.edu.az

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Article 2
Perception of ChatGPT Usage for Homework Assignments: Students' and Professors'
Perspectives
Dr Irena Miljkovic Krecar
Irena Miljkovic Krecar graduated in psychology, gained a master’s degree in organizational
psychology and a doctorate in psychology, all at Faculty of Humanities and Social Sciences in
Zagreb. Since graduation, she has been working as personal counsellor and job selection
professional, but mostly as a psychology lecturer and trainer. She is employed at the VERN’
University as a psychology professor, teaching eight different courses. She has published over
30 scientific and expert articles, course books in business psychology and psychology of
tourism and presented papers at many international conferences.
E-mail: irena.miljkovic@vern.hr
Dr Maja Kolega
Maja Kolega graduated in Psychology at the Faculty of Philosophy, completed postgraduate
study, Supervision in psychosocial work and gained PhD at the Faculty of Law, University ofZagreb, Croatia. She is currently employed at VERN’ University as a psychology lecturer,
teaching and developing different courses. Her main interests are education and organizational
development.
Email: maja.kolega@vern.hr
Dr Lana Jurcec
Lana Jurcec is an Associate Professor at the Faculty of Teacher Education. Her main researchinterest is in the fields of educational psychology and positive psychology. She published about
twenty-five scientific papers, two book chapters and one professional book.
Email: lana.jurcec@ufzg.hr
Article 3
Developing a Course on Human-Centered Machine Learning-Based Product Design
Eui-Chul Jung
Seoul National University, South Korea
Email: jech@snu.ac.kr
Meile Le
Hanyang University, South Korea

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Article 4Internationalized Students: Examining Students’ Experiences in Virtual Study Abroad
Programs in JapanDr Ariunaa Enkhtur
Ariunaa Enkhtur (PhD) is a specially appointed assistant professor at the Center for Global
Initiatives, Osaka University, Japan. Her research interests include internationalization ofhigher education, academic mobility, virtual student mobility, transnational higher education,international education and development, teaching and learning. For more information about
Ariunaa Enkhtur and her research works, please visit: https://researchmap.jp/ariunaa_enkhtur.
Email: enkhtur.ariunaa.cgin@osaka-u.ac.jp
Dr Ming Li
Ming Li (PhD) is an Associate Professor at the Institute for Transdisciplinary Graduate Degree
Programs, Osaka University, Japan. Her major academic fields include the internationalizationof higher education, international student mobility, and interdisciplinary education. For more
information about Dr. Ming Li and her research works, please visit:https://researchmap.jp/liming
Email: li.ming.itgp@osaka-u.ac.jp
Article 5
Using AI-Powered Speech Recognition Technology to Improve English Pronunciationand Speaking Skills
Dr Nutprapha K. Dennis
Nutprapha K. Dennis holds a PhD in English Language Studies from Suranaree University ofTechnology, Thailand, and MA in Teaching English as a Foreign Language (TEFL) from Ubon
Ratchathani University, Thailand. She is currently a Senior English Lecturer at Ubon
Ratchathani Rajabhat University, where she has over 15 years of experience teaching English
at the university level. Her research interests include language teaching, material and course
design, English communication in cross-cultural contexts, and educational technologies.
E-mail: nootprapa.k@ubru.ac.th
Article 6
Academic and Social Adjustment to Post-Pandemic Hybrid Learning:
Phenomenological Study of Filipino First-Year Engineering Students’ Experiences

A

Maria Cristina M. Firmante
Maria Cristina M. Firmante is a registered guidance counselor with over 18 years of experience.
She works as a University Counselor at De La Salle University. She completed a BS in
Psychology, Master of Arts in Education, major in Guidance and Counseling, and is currently
a candidate for a doctorate in Counseling Psychology, major in Clinical Counseling at De La
Salle University. Cris has been a speaker at various seminars on guidance and counseling,mental health, and positive psychology. She is also passionate about research and has presented
and published her work locally and internationally. She serves as a board member of the6

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Educational Counselors of the Asia-Pacific (ECPA-APECA, Philippines) and an active
member of the Philippine Guidance and Counseling Association (PGCA, INC.).
Email: maria.cristina.firmante@dlsu.edu.phArticle 7
The Influence of E-Comics on English Lexical Competence in Virtual Higher Education
Dr Norma Flores-González
Norma Flores-González is a professor-researcher at the Faculty of Languages of the
Benemérita Universidad Autónoma de Puebla, Mexico. She has a degree in Modern
Languages, a Master in Educational Sciences degree, and a PhD in Education. Her areas ofinterest are face-to-face, virtual, and distance education, evaluation, tutoring, and teachingpractice. She has presented papers and research in national and international contexts andpublished in national and international journals. She belongs to the National System of
Researchers (SNI).
Email: norma-fg@hotmail.com
Dr Vianey Castelán Flores
Vianey Castelán Flores is a professor-researcher of the French Teaching Bachelor at the
Benemérita Universidad Autónoma Puebla. She has a degree in Modern Languages, a Masterin Education degree, and a PhD in Education. She is a Coordinator of Academic Evaluationand the Scholarships Department. Her areas of interest are teaching, research, and evaluation.
Email: fcvianey@hotmail.com
Dr Mónica Zamora Hernández
Mónica Zamora Hernández is a research professor of the French-Teaching Bachelor at theFaculty of Languages of the Benemérita Universidad Autónoma de Puebla. She has a degreein Modern Languages, a Master in Educational Sciences degree, and a PhD in Education. Her
field of study is tutoring and teaching-learning processes of foreign languages.
Email: monica_zahdz@gmail.com

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Factors Influencing Teachers’ Use of Artificial Intelligence for
Instructional Purposes

Mukhammadfoik Bakhadirov
Westminster International University, Uzbekistan
Rena Alasgarova
Baku Oxford School, Azerbaijan
Jeyhun Rzayev
ADA University, Azerbaijan

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Abstract
The current paper examined the impact of a set of individual, technological, and institutionalvariables on the adoption of artificial intelligence (AI) among teachers at private schools. The
rationale for this study lies in its contribution to the understanding of how teacher
characteristics, institutional support, and technological perceptions affect AI adoption in
educational settings. The study used data collected from teachers (n=306) from seven schools
located in Azerbaijan in 2024. The study suggested that perceived usefulness of AI increases
teachers’ use of AI for educational purposes, while perceived ease of use of AI has no
statistically significant impact. The study also documented a statistically significant link
between institutional policy and the use of AI by colleagues on the one hand, and AI adoption
among schoolteachers on the other. Finally, the study found evidence relating to the link
between AI adoption and the age of the teacher, such that teachers who are younger were more
likely to adopt this technology. Surprisingly, personal innovativeness and level of openness to
new experiences did not stimulate teachers to adopt AI for teaching. The findings contribute toimproving the field’s understanding of teachers’ attitudes and motivations for using AI for
instructional purposes. The study findings also highlight the role of administrative regulationand school policies in stimulating the adoption of new technologies. These findings contributeto relatively novel literature relating to the application of AI in education and provide usefulrecommendations for administrators of educational institutions.
Keywords: artificial intelligence, education, personality, technology acceptance, technology
adoption innovativeness10

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Whilst the rapid advancement of technology continues to change the educational landscape,artificial intelligence (AI) is becoming a major influence on the methods and outcomes of
education. The introduction of AI in teaching and learning processes presents numerous
opportunities for improving efficiency in content delivery; however, AI should be implemented
based on consideration of both technological and human aspects. The Technology Acceptance
Model (TAM), designed by Davis (1986), provides an effective model for predicting the extent
to which technology is accepted by educators. This model envisions perceived usefulness andperceived ease of use as key components influencing technology acceptability, which areparticularly relevant when discussing the incorporation of AI in education (Davis & Granić,
2024).
The acceptability and application of AI in educational contexts are influenced by personality
qualities articulated in the Big 5 model (Kaya et al., 2022; Sánchez-Prieto et al., 2019; Seibert
et al., 2021; Sindermann et al., 2022; Stein et al., 2024). The Big 5 model categorizes
personality traits into five broad dimensions: openness to experience, conscientiousness,extraversion, agreeableness, and neuroticism. These attributes define how a person would react
to change and new technologies, and, therefore, how they would approach AI tools when thelatter are introduced into their teaching environment. According to Kaya and colleagues (2022),
characteristics such as openness to experience and conscientiousness can have a substantial
impact on a teacher’s readiness to incorporate new technologies, such as AI, into their teachingpractices. These characteristics influence how educators see the potential benefits andusefulness of AI tools, determining their readiness to adopt such technologies. The Big 5 traits
provide comprehensive knowledge of the psychological aspects that might support or hinder
technological transition in education, meaning that individual personality differences must be
considered when devising targeted interventions to boost AI adoption (Stein et al., 2024). This
psychological preparedness is vital since it complements the practical needs and pressures that
educators face in their professional orientation.
In the context of education, teacher burnout has persisted as a major issue affecting teachers in
the course of time and poses several implications for their willingness to accept and incorporate
technological teaching aids into their teaching process (Sindermann et al., 2022). Researchers
argue that teachers often work under pressure and stress originated from administrative tasks,
as well as the urge to address students’ individual learning needs, which can be aggravated bylimited administrative support systems (Arvidsson et al., 2019). These practices can result in ahigh level of stress and burnout among teachers, therefore inhibiting their ability to perform
effectively and foster a positive and constructive learning atmosphere. AI technologies mayhelp reduce some of these stressors due to the adoption of new technologies and automation ofprocesses which would improve the experience and work satisfaction of teachers, thereby
promoting job retention, improved quality of teaching, and improved outcomes for students(Haleem et al., 2022). For instance, AI can aid in effective performance and individual
assessments, thus minimizing the amount of work in this area of teachers’ day-to-day activities
and helping them focus on the teaching process and its effectiveness.

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Thus, schools should prioritize the development of clear policies and support frameworks that
address the technical and pedagogical aspects of AI integration (Ding et al., 2024; Karakose &Tulubas, 2024). In turn, by creating conditions for positive attitudes towards and usage oftechnologies in general and AI in particular, school leaders and administrators may enhanceteachers’ professional practice and expertise, hence, improving their students’ outcomes.
The need to integrate these technological innovations in educational settings underlines theurge for proper policies at schools regarding the application of the technologies. Such aproactive approach will address several of the previously discussed concerns surrounding how
AI can potentially reduce the workload in teaching without ever coming to fruition in practicefor many teachers. For any policy to be effective, it should not only encapsulate the resolutionof compatibility problems but also ensure that AI implementations are in line with education
goals and aims, as well as consider teachers’ needs to enable a harmonized adoption of AI toboost the teaching and learning environment (Fullan et al., 2023; Chan, 2023). This study aims
to explore a range of factors affecting AI use in teaching. It does this by addressing thefollowing research questions:
1. What is the impact of the perceived usefulness of AI technology on its adoption among
teachers?
2. How does perceived ease of use influence teachers' willingness to employ AI tools intheir instruction?
3. What role does teachers’ innovativeness play in the acceptance and integration of AI
into teaching?
4. How does openness to new experiences influence teachers' attitudes towards andengagement with AI technologies?
5. What influence do school policies have on the adoption and effective use of AI byteachers?
The significance of this research is underscored by the comprehensive analysis provided by theEuropean Digital Education Hub's briefing reports (Le Borgne et al., 2024; Obae et al., 2024).
These reports elaborate on the necessity of equipping educators with AI literacy and adapting
curricula to include AI competencies as integral components of modern education. Specifically,
Briefing Report 1 highlights the urgency of continuous professional development that
addresses both the technological and pedagogical aspects of AI integration, ensuring thatteachers are not only users but also informed implementers of AI technologies in theirclassrooms (Le Borgne et al., 2024). Briefing Report 4 discusses the crucial role of institutional
support systems in facilitating the effective use of AI within educational frameworks. Itemphasizes the importance of school policies that are flexible yet robust enough to support the
dynamic nature of AI technologies, advocating for policies that can solve compatibility issues
and foster an environment conducive to technological advancements and teacher acceptance(Obae et al., 2024).
The current study expands upon these premises by considering a wider range of factors
affecting AI use in teaching, including teachers’ traits like innovativeness and willingness to12

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experiment, characteristics of the learning environment, policies, and administrative support.
By discussing these dimensions, this study attempted to create a bigger picture of the role thatdifferent factors are playing in the process of adopting new AI technologies in education.
Thereby, it offers valuable insights for administrators and policymakers aiming to foster a more
effective integration of AI in schools.
The remainder of the paper is structured as follows. ‘Literature Review’ develops the
hypotheses. ‘Data and Methodology’ describes the methods of the study and data collection
procedures. ‘Results’ presents the findings of the analysis. ‘Discussion’ describes the findingsof the study, its theoretical and practical implications. The paper concludes with ‘Conclusions
and Recommendations’.
Literature Review and Hypothesis Development
Technology Adoption and the Use of AI in Education: The Role of Perceived Usefulness
and Perceived Ease of Use
Introduced by Davis in 1986, TAM is a framework that provides a strong foundation forstudying the adoption of technology in the educational landscape. This model revolves around
two principal concepts that determine the attitude of educators towards accepting newtechnologies: perceived usefulness and perceived ease of use (Davis & Granić, 2024). In thecontext of AI for education, these constructs guide the evaluation of educators’ judgments
about what aid AI tools can bring and how they fit into current pedagogical practices (Kelly etal., 2023).
The perceived usefulness of AI in education is said to be connected to its capacity to
revolutionize the pedagogical context. AI technologies, which are viewed as “an auxiliary
system for education” (Uygun, 2024, p. 938), allow the education process to get more personaland optimal for achieving the utmost learning outcomes and providing space for adaptation to
the individual psychological needs of students as related to “their autonomy, competence andsocial relatedness” (Ofosu-Ampong et al., 2023, p. 45). This also relates to the personalizationof activities to reflect the specific pace and style of learning, as well as tailoring relevant
constructive feedback and assessment that considers the strengths and weaknesses of individual
students. It is argued that the qualities of AI, which directly support learning and teachingprocesses in improving student outcomes and developing “their creative ability to shape theirthoughts”, are useful for facilitating a more dynamic and interactive classroom environment(García-Martínez et al., 2023, p.188). Additionally, the ability of AI to reduce the amount oftime spent on administrative workload related to tasks like grading or evaluating students’assignments is beneficial to the educator as it increases the amount of time that the educatorspends on teaching and interacting with the student (Owan et al., 2023). The cumulative effectof these AI applications can help teachers ensure a challenging, however engaging, and positive
learning environment in which both teachers and students can thrive. Through reducing
burdens and enriching a positive teaching experience, AI can promote more sustainable andsatisfying teaching practices.

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The above arguments suggest that the perceived usefulness of AI motivates teachers to improvetheir teaching practices by introducing AI into the educational process. Therefore, wehypothesize that:
Hypothesis 1:

a. Perceived usefulness of AI stimulates the use of AI for educational
purposes

Teacher burnout is a challenging issue in today’s educational settings and is a condition ofphysical, emotional, and mental exhaustion that leads to negative and sometimes inappropriate
responses towards students, their jobs, and colleagues in general. This occurs due to the myriadof demands that fall on teachers, which include but are not limited to administrative paperwork,
constant assessment and evaluation of students’ assignments, and the efforts to accommodate
diverse learning types and preferences (Arvidsson et al., 2019). The increased emphasis ontechnology and especially on the use of AI provides the space for diminishing, or at least
preventing, some of these stressors through the automation of menial tasks and simplifying
complicated work processes, thus eliminating the risk of burnout (Bauwens et al., 2020). This
supports the claim that AI not only empowers teaching practices but also enhances teachers’
job satisfaction and their career sustainability within the teaching field. In this context, thetransformation implies the introduction of AI in education by simplifying tasks and reducing
workload, which naturally leads to the concept of perceived ease of use of the TAM framework,emphasizing the necessity of creating AI tools that are simple and easily adapted into theeducational system.
Perceived ease of use represents the idea that teachers consider learning to use AI in theirclassroom practices to require minimal effort. Teachers can be more inclined to accept toolsthat they can easily understand or that do not require sophisticated technical skills and arecompatible with the existent educational frameworks (Al Darayseh, 2023; Ofosu-Ampong,2024). The lack of integration and noticeable gaps in how these technologies align with daily
teaching activities are crucial factors in teachers’ low acceptance of AI tools. The above
arguments suggest that teachers are more interested in adopting AI for teaching purposes when
their perception of its ease of use is high. Therefore, we hypothesize that:
Hypothesis 1:

b. Perceived ease of use of AI stimulates the use of AI for educational
purposes.

Acceptance of the AI by Teachers: The Role of Personality and Individual Factors
The adoption of AI in educational environments is largely determined by personal
characteristics (Kaya et al., 2022; Sánchez-Prieto et al., 2019; Sindermann et al., 2022).
Historical shifts in education types, for example, the introduction of the internet, the rise of
edtech, and the rapid pivot to online learning during COVID-19, have revealed differences inthe degree of adaptability among educators (Haleem et al., 2022; Ng et al., 2023). Thesetransformations show that some educators approach and master new technologies with ease,whilst others still have some hesitation due to their risk averse or conservative nature. Such

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fluctuations in adaptation can be envisaged through the lens of the Big 5 personality
dimensions. For instance, openness to experience encompasses such traits as curiosity and a
predisposition to explore new things, which relates to teachers’ innovativeness (Bauwens etal., 2020). Those who are high in this trait are more likely to incorporate AI technologies into
their teaching practices since they are more open to change, possibly leading to more improvedand efficient teaching experiences in schools.
Empirical research studies emphasize individual differences in technology adoption. Teacherswith high levels of openness and innovativeness are the ones who usually experiment with newtools and are most successful in integrating these platforms into their teaching practices. As
stated by Kaya and colleagues (2022) “openness to experience may increase the perceived
practicality and ease of use of technology” (p. 508). Innovativeness in education is not only
about technology but also about creativity in pedagogy and curriculum design that determines
the direction of the evolution of educational practices (Seibert, et al., 2021).
In ever-evolving education, with advancements in technology, the importance of such
personality characteristics as innovativeness in technology acceptance needs to be appreciated(Haleem et al., 2022; Sánchez-Prieto et al., 2019). This individual orientation is useful not onlyin understanding why some educators are better at technology-enhanced learning but also inproviding a more finely tuned view of technology integration in the educational world.
Therefore, we suggest that:
Hypothesis 2:

a. Teacher’s innovativeness is positively associated with the adoptionof AI for teaching purposes.

In examining the nature of technology adoption by educators, the Big 5 personality model,
particularly the trait of openness to experiences, is rather revealing (Kaya et al., 2022; Stein etal., 2024). This characteristic encompasses the person’s inclination towards innovation andtheir open-mindedness to the demonstration of fresh concepts and strategies that reflect theirreadiness to assimilate and apply AI tools in their teaching practice. (Sánchez-Prieto et al.,2019).
A high level of openness in teachers highlights their curiosity, imagination, and being broadly
keen on incorporating technologies into their pedagogical repertoire (Kaya et al., 2022;
Sánchez-Prieto et al., 2019; Sindermann et al., 2022). Teachers, high in openness, are often theearly adopters of new technologies who explore their potential to improve learning andteaching outcomes with much more enthusiasm than their less open colleagues. The correlationof openness to technology adoption is backed by recent empirical studies indicating that theteacher’s personality traits play a significant role in the process of technology integration intoclassrooms. One of the examples is that educational professionals with a high degree of
openness are not only quick to try out new tools but are also experts in incorporating these
innovations in ways that promote student engagement and outcomes (Sánchez-Prieto et al.,2019).

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The openness of teachers to new experiences allows for the customization of professional
development programs. Such professional development programs should be developed to caterfor the needs and wants of teachers regardless of their inherent passion for new technologies
and to ensure a more effective and inclusive adoption of educational innovations.
Apart from openness, the Big 5 model incorporates other personality traits such as
conscientiousness, extraversion, agreeableness, and neuroticism. The conjunction of thesecharacteristics creates a complex image of the person that influences his or her conduct invarious areas of life, including professional activities (Seibert et al., 2021). Comprehending
these personality-driven aspects can help in implementing focused plans for technology
integration in schools. Therefore, we hypothesize that:
Hypothesis 2:

b. Teacher’s openness to new experience is positively associated withthe adoption of AI for teaching purposes.

AI Adoption and Institutional Policies: The Role of Administrative Support
The incorporation of AI into educational settings is not a mere technological innovation but an
involving process that needs strong administrative support. The adoption landscape of
educational technology is quite diverse, with some schools being pioneer adopters ofinnovative tools and methods, while others are conservative and diligent, often slowing down
the technological spirit. Such differences are mostly caused by the various levels of support
and promotion that educational institutions offer (Chan, 2023; Rahman & Watanobe, 2023).
Schools that are inclined to promote the implementation of innovative technologies are
generally progressive organizations that consider technological innovation as a fundamental
tool to improve educational outcomes. These schools invest not only in the equipment andsoftware needed but also in a culture of continuous learning and adaptability (Fullan et al.,2023; Karakose & Tulubas, 2024). Support services in these schools include continued
professional development designed to help teachers integrate new technologies effectively into
their pedagogy.
On the other hand, the difficulty that schools face in implementing technology is largely due
to a lack of institutional support. Under these conditions, teachers become isolated in theirattempt to introduce new tools and unsure of the possible consequences of failures (Sindermann
et al., 2022). Fear of failure is a detrimental factor in educational settings where innovation
does not have institutional recognition. Teachers in such environments are usually unwilling tostray from the accepted norms and practices owing to job security fears, criticism, and a lackof benefit from changing their teaching styles (Roczniewska et al., 2020).
Administrative support profoundly affects this by creating an environment in which technology
experiments are viewed more as learning experiences than as a threat. As long as school leaders
endorse the use of AI and other technologies as part of the school vision aimed at providing

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future-ready education, it creates a setting that can erase fears and excite the staff (Fullan et al.,2023; Karakose & Tulubas, 2024).
However, this supportive orientation should also entail the provision of needed infrastructure– reliable internet access, up-to-date computing equipment, and technical support – that will
make the adoption of new technologies a possibility and a less intimidating process for an
educator. “This attempt not only requires enabling a more digitally enhanced learning
environment but also integrating these technologies to practice effective management andleadership in contemporary schools” (Karakose & Tulubas, 2024). The implementation of AI
technologies into teaching and learning environments demands “school leaders to constantly
adapt and expand their technological knowledge and skills simply to remain ahead of the AI
curve” (Fullan et al., 2023, p. 4). Additionally, incentives and reward systems that recognize
innovative teaching related practices, can motivate teachers to experiment with and embrace
the use of new technologies.
Such policies help educators understand how AI tools should be integrated into the learning
process and how these tools can bring about better learning outcomes among students. Clear
policies aid in creating the yardsticks for success and a framework within which teachers may
innovate with confidence. Administrative assistance also provides continued support throughtraining programs that enable educators to keep up to date with technological advancements
and pedagogical strategies that utilize these technologies appropriately (Bauwens et al., 2020;
Ding et al., 2024; Sindermann et al., 2022). For successful AI adoption, teachers should betaught not only how to use new tools but also why these tools will improve their students’
educational experiences and outcomes. Therefore, we claim that:
Hypothesis 3:

Presence of school policy supporting the use of AI increases the use of AI
for teaching purposes.

Conceptual Model
A review of the existing literature on the application of AI technology in education has
identified a considerable gap for further investigation. Our study aims to contribute to thisexamination, particularly in relation to the sets of (H1) technology adoption factors (i.e.,
perceived usefulness and perceived ease of use), (H2) personality and individual variables (i.e.,
innovativeness and openness to new experiences), and (H3) institutional support (the school’s
AI policies). A conceptual model of the proposed study is presented in the following page.

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Figure 1
Conceptual Model of the Study

Data and Methodology
Data and Its Collection
The study utilized a survey methodology, collecting data from teachers working at the sevenlargest private schools in Baku, Azerbaijan. The study’s questionnaire was piloted among 12
teachers to improve its face and content validity. Then, anonymized copies of the questionnaire
were distributed among all secondary teachers. The population of the study encompassed 956
teachers. The response rate comprised 32% with 310 teachers voluntarily agreeing toparticipate in the study. Four questionnaires were removed from the pool as they failed toanswer the attention check questions correctly. Responses collected from 306 teachers
constituted the dataset. Table 1 summarizes the descriptive statistics for the study sample.
Ethical issues were closely followed throughout the investigation in accordance with APA 7
principles. Every participant received comprehensive details regarding the research, guaranteesof their privacy and confidentiality, and knowledge of their freedom to discontinue
participation at any moment without repercussions (American Psychological Association,2017).
To further ensure the validity of the data, the following measures have been taken. Several
Likert scale items have been reversed. A reliability test was conducted to measure the internalconsistency of the collected data. Moreover, a factor analysis was performed to estimate
whether the variables heavily loaded around a common factor.

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Table 1
Descriptive Statistics (n=306)
Variable
Gender of the respondent (GEN)Age of the respondent (in full years) (AGE)
Highest education level (STAG)
Level of computer proficiency/skills (COM)
School policy that supports the use of AI (POL)
Use of AI by a colleague(s) (CUSE)
Use of AI by the respondent (USE)
Perceived usefulness (PU)
Perceived ease of use (PEU)
Attitude toward AI (ATT)Internal Locus of Control (ILC)
Innovativeness (INN)
HopeOptimism
Self-efficacy
Resilience
Openness (OP)Psychological Capital (PSYCAP)

Range
1
46
2
2
1
1
1
17
21
8
7
17
11
11
9
11
9
42

Min
1
21
1
1
0.00
0.00
0.00
4
7
6
8
18
10
10
12
10
19
42

Max
2
67
3
3
1
1
1
21
28
14
15
35
21
21
21
21
28
84

Mean
1.58
37.56
1.98
2.76
0.31
0.73
0.57
17.97
24.03
11.56
11.60
29.34
18.63
19.25
19.06
17.81
25.67
74.75

SD
0.49
8.79
0.645
0.44
0.46
0.45
0.5
3.05
3.36
1.92
1.44
4.04
1.93
1.58
1.52
2.75
1.75
5.98

Note. The table documents the descriptive statistics for the variables used in this paper. All variables
are discussed in section 3.2. The sample period is from April to May 2024.

Measurement and Analysis
The purpose of the study was to measure the impact of individual, institutional, andtechnological factors on the use of AI in educational ecosystems as posed in our main researchquestion. Specifically, it was hypothesized that the adoption of AI technology is stimulated by(H1) the components of the technology adoption, i.e., perceived usefulness and the perceived
ease of use of AI technology, (H2) the presence of an AI support policy, and (H3) individuallevel variables such as openness to new experiences, innovativeness. The ordinary least squares
regression equation presented below was employed to test these hypotheses.
USE = α + β! (PU) + β" (PEU) + β# (POL) + β$ (OP) + β% (INN) + β& (SCHL)+ β' (AGE) + β( (GEN) + β) (SUBJ) + β!* (STAG) + β!! (CUSE)+ β!" (COM) + β!# (ATT) + β!$ (ILC) + β!% (PSYCAP) + ε(1)

In the above regression equation, the dependent variable USE represents the use of artificial
intelligence technology usage by teacher for educational purposes. For (H1), we used a PU
variable to measure the degree of usefulness of AI and a PEU variable to measure the degreeof ease of use of AI as perceived by teachers. For (H2), we used an INN variable to measure
the degree of innovativeness and an OP variable to measure the openness to new experiences

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by schoolteachers. For (H3), we used a POL variable to measure whether schools have policies
supporting the use of AI for teaching and learning purposes in place.
•

•

•

•

•

PU – perceived usefulness factor – component of TAM (Davis, 1989). It is suggested
that perceived usefulness, which can be defined as a person’s perception of the degree
to which technology use can improve performance, should stimulate the use of AI
technology. This factor is generated using the principal component analysis andcalculated as a sum of answers to three out of four 7-degree Likert scale items e.g. I
would find AI technology useful in my work. One item was dropped as a result of thereliability test, and the calculated Cronbach’s alpha for perceived usefulness was equal
to 0.832.
PEU – perceived ease of use factor – another component of the technology acceptancemodel (TAM) measures P the level to which a person believes that using a particularsystem is free of effort (Davis, 1989). This construct originates from the concept ofself-efficacy, which pertains to an individual’s specific belief in their ability tosuccessfully perform actions required for a future task. Similar to perceived usefulness,a higher degree of perceived ease of use should also lead to a higher likelihood of
adopting AI technology according to the TAM model. The 7-degree Likert scale items,
such as I would find it easy to get AI technology to do what I want to do was used tomeasure PEU. The latent variable was generated using the principal component analysis
from four 7-degree Likert scales items. The calculated Cronbach’s alpha for PEU scale
is equal to 0.795.
INN – innovativeness variable was measured using an adopted scale by De Jong and
Den Hartog (2008). It contained 7-degree Likert scale items such as I systematicallyintroduce innovative ideas into work practices. These were grouped into a single latentvariable using the principal components analysis and calculated as a sum of fivevariables. Cronbach’s alpha for this factor is equal to 0.812.
OP – openness to new experience variable measured using the Big 5 Personality traits
scale items. It included eight regular and two reversed 7-degree Likert scale items, suchas I see myself as someone who is curious about many different things. These weregrouped into a single Openness laten variable using the sum of four variables that weregrouped around the common factor as a result of principal component analysis.
Reliability analysis
The other independent variable of interest is POL. It is a dummy variable measuring
the school’s support for the use of AI at a policy level, where 1=represents the presenceof policy and 0=absence of school policy supporting the use of AI.

The differences across the schools were taken into account by controlling for the school(SCHL) variable. Moreover, the above regression equation also contains multiple personality
and individual-level characteristics as control variables. These are:••

AGE – age of the respondent,
GEN – gender of the respondent,

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•
•
•
•
•
•
•

Volume 12 – Issue 2 – 2024

SUBJ – subject teachers teach,
STAG – stage of education,
COM – degree of computer proficiency,
ATT – general attitude toward artificial intelligence,
PSYCAP – psychological capital of a teacher (calculated as a sum of for items: hope,
optimism, self-efficacy, and resilience), and
ILC – locus of control, which stands for the perceived degree of control over the things
happening in one’s life, including success and failure.
Finally, we also believe that teachers are more likely to adopt AI should their workcolleagues use AI for teaching purposes on a regular basis and measure it using the
CUSE dummy variable where 1=colleague(s) are using AI for instructional purposes
and 0=otherwise.
Results

Results of the Regression Analysis
Table 2 displays the correlations between the independent variables. The modest correlation
between these variables suggests that the analysis is not subject to a problem ofmulticollinearity.
Main regression analysis results are presented in Table 3. As anticipated, statisticallysignificant positive estimates are documented for multiple variables of interest. First, in relation
to Hypothesis 1 (H1), the perceived usefulness of AI technology is found to be statisticallyvery significant, suggesting that teachers who view AI as a beneficial tool are more likely toincorporate it into their teaching. Perceived ease of use of AI is found to be significant only at
p-value=0.1 level. Concerning Hypothesis 2 (H2), teacher innovativeness was found to be asignificant predictor of AI adoption, confirming the hypothesis that more innovative teachers
are inclined to use AI in their educational practices. Contrary to expectations, openness to newexperiences did not significantly influence AI adoption, indicating that teachers will use AI
regardless of their degree of openness to new experiences. For Hypothesis 3 (H3), the presenceof a school AI policy was significantly associated with AI usage, underscoring that the schoolthat have AI policies in place better stimulate the use of AI for educational purposes byteachers. Additionally, the study suggests that adoption of AI among the teachers is also (i)negatively related to their age (younger teachers are more likely to use AI), and (ii) positively
related to the use of AI technology by their colleagues.

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Table 2
Correlation Matrix
No.

Variables

1

2

3

4

5

6

7

8

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16

Use Of AI
Perceived Usefulness
Perceived Ease of Use
School Policy
OpennessInnovativeness
SchoolGenderAge
Teaching Area
Stage
Computer Skills
AI Use by a Colleague
Attitude Toward AI
Locus Of Control
Psychological Capital

1.000
.262
.158
.194
.071
.160
-.092
-.009
-.135
-.082
.061
.061
.270
.080
.095
.081

1.000
.717
.165
.188
.147
.084
.158
.066
-.224
.178
.079
.213
.294
.024
.225

1.000
.038
.200
.219
.041
.146
-.016
-.145
.201
.008
.223
.163
.043
.202

1.000
.112
.076
-.073
-.034
.072
-.138
.033
.124
.151
-.025
-.053
.050

1.000
.271
.196
.026
.152
-.102
-.101
.088
-.047
.259
.151
.640

1.000
.024
.047
.016
-.083
.098
-.162
-.009
.318
.139
.279

1.000
-.105
.059
.051
-.197
-.028
-.160
.225
.023
.428

1.000
-.119
-.046
.071
.012
.060
-.040
-.032
-.049

(cont.)
No.

Variables

9

10

11

12

13

14

15

16

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16

Use Of AI
Perceived Usefulness
Perceived Ease of Use
School Policy
OpennessInnovativeness
SchoolGenderAge
Teaching Area
Stage
Computer Skills
AI Use by a Colleague
Attitude Toward AI
Locus Of Control
Psychological Capital

1.000
-.066
-.040
.001
.014
.031
-.035
.177

1.000
-.018
.004
-.193
-.086
.013
-.092

1.000
-.097
.066
.086
.003
-.092

1.000
.148
-.102
-.042
.069

1.000
-.059
.021
-.003

1.000
.114
.373

1.000
.270

1.000
Note. The table documents the correlation between variables used in this paper. All variables are as discussed in
section 3.2. The sample period is from April to May 2024.

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Table 3
Relationship Between the use of AI by Teacher, Individual-level Factors, Technology
Acceptance Components, and Institutional Policies
Variables
Perceived Usefulness
Perceived Ease of Use
School Policy
OpennessInnovativeness

Model (1)0.269***(3.360)-0.065(-0.812)0.144**(2.57)-0.017(-0.302)0.129**(2.232)Model (2)0.313***(3.845)-0.091(-1.141)0.130**(2.305)0.026(0.442)0.133**(2.291)-0.113**(-1.982)-0.80(-1.444)-0.176***(-3.189)-0.004(-0.066)-0.011(-0.187)
0.037(0.660)-0.371(-0.878)
306
7.214
0.107

-0.216(-0.454)
306
4.812
0.153

SchoolGenderAge
Teaching Area
Stage
Computer Skills
Ai Use by a Colleague
Attitude Toward AI
Locus Of Control
Psychological Capital(Constant)Observations
F-Value
R-SquareModel (3)0.304***(3.660)-0.136*(-1.705)
0.111**(1.993)0.018(0.254)0.130**(2.191)-0.095(-1.538)-0.079(-1.443)-0.181***(-3.323)0.027(0.491)-0.008(-0.142)0.007(0.123)0.216***(3.780)-0.014(-0.227)0.057(1.021)
0.047(0.585)-0.556(-1.124)
306
4.825
0.200

Note. This table documents the relationship between the use of AI by teacher, individual-level factors, technology
acceptance components, and institutional policies. The t-values based on the heteroscedasticity-robust standard
errors are presented in parentheses. The outcome variable is USE (the use of AI technology for teaching purposes).
The sample period is April to May 2024. OLS regression model is used. The symbols ∗, ∗∗, ∗∗∗ correspond to p
< 0.1, p < 0.05, p < 0.01, respectively. All variables are as defined in the ‘Measurement and Analysis’ section.

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Additional Test
To examine the robustness of our results, the model was re-estimated by using an alternative
proxy for the USE variable. The regression analysis was run to measure the impact of examined
predictors on the Intention to use AI tech – INT as indicated in equation 2.
INT = α + β! (PU) + β" (PEU) + β# (POL) + β$ (OP) + β% (INN) + β& (SCHL)+ β' (AGE) + β( (GEN) + β) (SUBJ) + β!* (STAG) + β!! (CUSE)+ β!" (COM) + β!# (ATT) + β!$ (ILC) + β!% (PSYCAP) + ε(2)

Results of the alternative regression analysis were consistent with the previous findings withregards to the impact of the perceived usefulness. All three models suggested statistically verysignificant impact of perceived usefulness of AI on teachers’ intention to use it for educational
purposes. Similar to the previous model, the impact of colleagues’ practices on the intention to
use AI remains statistically significant whereas the individua-level variables, and institutional
factors did not sustain their predictive value (Table 4).
The summary of this analysis is summarized in Table 5. As can be seen, all hypotheses except
for H2b are supported according to the results of Test 1 and only H1a found support based on
the results of Test 2.
Table 4
Relationship Between the Intention to Use AI by Teacher, Individual-Level Factors,
Technology Acceptance Components, and Institutional Policies
Variables
Perceived Usefulness
Perceived Ease of Use
School Policy
OpennessInnovativeness
SchoolGenderAge
Teaching Area
Stage
Computer Skills

Model (1)0.310***(3.868)-0.072(-0.900)
0.089(1.579)0.067(1.155)0.042(0.723)Model (2).330***(3.982)-0.062(-0.760)
0.090(1.582)
0.051
0.845
0.061
(1.034)0.038(0.654)-0.072(-1.276)-0.059(-1.058)
0.032(0.561)-0.067(-1.157)
0.048(0.850)24

Model (3)0.351***(4.098)-0.110(-1.331)0.069(1.209)0.040(0.545)0.075(1.230)0.050(0.777)-0.077(-1.366)-0.070(-1.242)0.053(0.934)-0.059(-1.020)0.019(0.331)IAFOR Journal of Education: Technology in Education

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AI Use by a Colleague(Constant)-0.353(-0.979)-0.297(-0.719)0.148**(2.523)-0.087(-1.357)
0.018(0.316)0.074(0.883)-0.438(-1.009)

Observations
F-Value
R-Square

306
6.939
0.104

306
3.721
0.122

306
3.428
0.151

Attitude Toward AI
Locus Of Control
Psychological Capital

Note. This table documents the relationship between the intention to use AI by teacher, individual-level factors,
technology acceptance components, and institutional policies. The t-values based on the heteroscedasticity-robust
standard errors are presented in parentheses. The outcome variable is USE (the use of AI technology for teaching
purposes). The sample period is April to May 2024. OLS regression model is used. The symbols ∗, ∗∗, ∗∗∗correspond to p < 0.1, p < 0.05, p < 0.01, respectively. All variables are as defined in the ‘Measurement andAnalysis’ section.

Table 5
Summary of the Analysis Results#
Hypothesis
1 a. Perceived usefulness of AI stimulates the useof AI for educational purposesb. Perceived ease of use of AI stimulates the useof AI for educational purposes
2 a. Teacher’s innovativeness is positively
associated with the adoption of AI for
teaching purposes.
b. Teacher’s openness to new experience ispositively associated with the adoption of AI
for teaching purposes.
3 Presence of school policy supporting the use of
AI increases the use of AI for teaching purposes.

Test 1 Results
Supported***

Test 2 Results
Supported***Supported*

Not supported

Supported**

Not supported

Not supported

Not supported

Supported**

Not supported

Note. The symbols ∗, ∗∗, ∗∗∗ correspond to p < 0.1, p < 0.05, p < 0.01, respectively. All variables
are as defined in the ‘Measurement and Analysis’ section.

Discussion
This research centers on examining the impact of individual, technological, and institutional
factors on the use of AI for instructional purposes. Through an examination of these factors
and their impact on the adoption of AI by instructors in private schools in Azerbaijan, the study
finds important components that either help or impede the real-world implementation of AI
technology in education.

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The results of the current research posit perceived usefulness and supportive institutional
policies as key factors influencing the incorporation of AI technologies into learning
environments. According to recent studies, the perceived usefulness of AI plays a major rolein adopting these tools in academic settings. This implies that the more the teachers understandthe benefits of AI regarding improving teaching efficiency and students’ performance, the more
likely they are to incorporate the technologies in their teaching practice (Kelly et al., 2023).
School policies are also a significant factor in the implementation of AI in classrooms. Schools
need to develop clear policies and framework support for AI integration so that teachers feel
more secure about using novice technologies and avoid rejection of AI tools (Chan, 2023;
Fullan et al., 2023). These findings shed light on the importance of teachers, heads of schools,
and other educational managers to support innovative approaches, which include leveraging
AI teaching methods.
Another important factor that impacts the use of AI is peer influence: teachers are more likely
to use AI if they observe other teachers using it. This peer effect highlights the need fordeveloping a community of practice where innovative teachers and their colleagues can learn
from each other and inspire others to test the advantages of AI (Sánchez-Prieto et al., 2019;
Stein et al., 2024). Thus, school leaders should ensure that they support and encourage the early
adopters of AI to prompt further development of the technology in their schools. These findings
have theoretical and practical implications for the use of AI in an educational landscape,suggesting that understanding the integration of these technologies requires consideration of
pertinent school polices, teachers’ perceptions, and educational contexts.
The significance of this study lies in its contribution to a deeper understanding of the way theinstitutional support, technology capabilities, and teacher traits interact to enable the adoptionof AI in educational ecosystems. Through the identification of the critical roles of perceived
utility, policy support, and peer influence, the study offers practical insights that might assist
educational institutions in formulating policies for more successful integration of AI into thecurriculum.
Theoretical Implications
In line with previous research, the present study has posited the factor of perceived usefulness
as a major predictor for adopting AI technologies among educators (Davis & Granić, 2024;
Kelly et al., 2023). The research synthesizes TAM and the Big 5 model, which enables tracing
a deeper picture of the factors that may facilitate or hinder the acceptance of technologies(Sánchez-Prieto et al., 2019; Stein et al., 2024). Personality characteristics may play a crucialrole in influencing teachers’ receptiveness to AI and their willingness to adopt it in theirclassrooms, thus calling for the development of targeted professional learning interventions
that take into account the diverse personality profiles.
The research also establishes that institutional policies are crucial in the integration of AI inlearning institutions, making it a critical area that requires policy support and effective policy
formulation in learning institutions (Chan, 2023; Fullan et al., 2023). In other words, the more

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a school advocates for and encourages the utilization of AI, the higher the levels of AI usage
among the staff will be. This requires deliberate planning and measures where educational
leaders consider not only the technology resources but also the culture of risk-taking.
Practical Implications
There are several practical implications of this study for educators, school administrators, andpolicymakers involved in the implementation of AI in education. These implications present
recommendations to be applied towards improving the utilization of AI tools in educational
environments.
The results of this study highlight the need for designing and delivering administrative support
and professional development opportunities that consider the personality traits of teachers.
Since such characteristics as openness to experience and innovativeness strongly relate to AI
use (Sánchez-Prieto et al. 2019; Stein et al. 2024), professional development programs should
account for existing diversity in teachers’ technological literacy and openness for change. For
example, the teachers with lower levels of openness might need the AI technologies to be
introduced to them in step-by-step manner through the professional development sessions andthrough the hands-on training that addresses their concerns (Ding et al., 2024). For thoseeducators who are ready for innovations, advanced workshops focusing on innovative uses of
AI in pedagogy could be more appropriate.
In this context, school policies have paramount importance in promoting the use of AI. Schools
are recommended not only to establish strong supportive policies that go beyond providing theappropriate resources, including access to AI tools and technical support, but also to foster а
culture of innovation (Chan, 2023; Fullan et al., 2023). Policies should also consider thephysical and social aspects of deploying AI in schools. This involves creating an environmentthat encourages experimentation and diminishes the fear of failure among teachers. Effective
communication of the benefits of AI can further enhance teacher buy-in and support for these
initiatives. To mitigate the bias inherent in AI systems, schools should develop policies on dataprotection, consent, and data usage.
Limitations of the Study
This study also has the following limitations, which might have an impact on thegeneralizability of the findings. Initially, the study is based on self-reported data; it should be
acknowledged that this type of data is subjected to interpretation errors. Although several
techniques were used to minimize these biases, it cannot be claimed that they have not been
completely eradicated. Second, the sample of educators participating in the study was limited
to teachers from private schools in Azerbaijan, which may hinder the generalization of thefindings to the population of teachers in other countries and various educational environments.
There are also other contextual factors related to AI that likely affect the extent to whichteachers use AI, including the internet connection or the school’s technological infrastructure
as well as IT assistance. Expanding the range of methodological approaches, involving more

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participants, and considering the circumstances in which teachers integrate AI technologies
into their practice will contribute to a better understanding of the field.
Conclusion and Recommendations
This research has established that perceived usefulness and supportive institutional policies are
crucial in contributing to the adoption of AI technologies in education. The study emphasizes
how crucial it is that educators comprehend and acknowledge the real-world advantages of AI,
such as increased pedagogical effectiveness and customized learning opportunities for students(Kelly et al., 2023). These insights facilitate the easier incorporation of AI tools into
educational settings. Educational institutions are encouraged to foster an environment that not
only supports but actively promotes the integration of AI through clear policies andframeworks. This support is essential for educators to feel secure and competent in utilizing AI
technologies, thus reducing the resistance associated with adopting new technologies in theclassroom (Obae et al., 2024).
Notably, institutional support is one of the significant factors that emerged in the analysis of
the findings. To support AI adoption, educational institutions need to enhance policies thatcreate a favorable environment that stipulates AI’s usage and provide extensive training forsuch purposes (Chan, 2023; Fullan et al., 2023). These policies should also guarantee theprovision of the underlying technologies, as well as support the culture of innovative use of AI
in the class and remove the culture of fear of trying new ideas from educators by embracing
the failure attempts as a positive step towards achieving the necessary integration of AI in thelearning process. The research findings advocate for educational reforms that will assist the
integration of AI literacy into existing curricula, suggesting that such integration should be
informed by an evidence-based approach to enhance the content and effectiveness of AI
education. It is crucial that these educational strategies encompass both the technological andpedagogical aspects of AI to ensure that teachers are not only users but proficient implementersof AI tools (Le Borgne et al., 2024).
In addition, the results of this study indicated that there is a need for continuing professional
development in conjunction with the identified strategies. This would assist educators inappropriately adapting to further advancements in AI technology for use in teaching. Suchtraining should be more nuanced to reflect the differences in readiness and perceptions towards
technology among teachers, in order to meet a range of needs starting from a fundamental
introduction to AI technology up to the methodological applications of this concept (Ding etal., 2024).
Looking ahead, there are some ideas that are worth exploring in more detail in the future. Morein-depth quantitative research may help to describe long-term changes in teachers’
technological beliefs as well as examine the various conditions that may have an impact on theuse of AI among teachers. This approach can encompass one-on-one interviews or focus group
discussions to obtain detailed information in regards to the psychosocial effects of AI inlearning environments.

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Furthermore, a longitudinal study could explore how the incorporation of AI into teaching andlearning would impact teaching practices and learners’ performance in the long-run, which
would be instrumental in tackling the question of the sustainability of technology integrationin learning and development as well as its benefits and drawbacks over time. A further
investigation of AI adoption in different education settings, like public schools or institutions
from different geographical regions, will also add to the knowledge of the factors that affectthe implementation of AI in an educational arena.

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References
Al Darayseh, A. (2023). Acceptance of Artificial Intelligence in teaching science: Scienceteachers’ perspective. Computers and Education: Artificial Intelligence, 4.
https://doi.org/10.1016/j.caeai.2023.100132
American Psychological Association. (2017). Ethical principles of psychologists and code of
conduct. American Psychological Association. Retrieved fromhttps://www.apa.org/ethics/code
Arvidsson, I., Leo, U., Larsson, A., Håkansson, C., Persson, R., & Björk, J. (2019). Burnout
among school teachers: Quantitative and qualitative results from a follow-up study inSouthern Sweden. BMC Public Health, 19(1). https://doi.org/10.1186/s12889-0196972-1
Bauwens, R., Muylaert, J., Clarysse, E., Audenaert, M., & Decramer, A. (2020). Teachers’
acceptance and use of digital learning environments after hours: Implications forwork-life balance and the role of Integration Preference. Computers in Human
Behavior, 112. https://doi.org/10.1016/j.chb.2020.106479
Chan, C. K. (2023). A comprehensive AI Policy Education Framework for university
teaching and learning. International Journal of Educational Technology in Higher
Education, 20(1). https://doi.org/10.1186/s41239-023-00408-3
Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance ofInformation Technology. MIS Quarterly, (13)3, 319–340.
https://doi.org/10.2307/249008
Davis, F. D., & Granić, A. (2024). The Technology Acceptance Model: 30 years of Tam.
Springer Nature. https://doi.org/10.1007/978-3-030-45274-2
De Jong, J.P.J. and Hartog, D.N.D. (2008) Measuring Innovative Work Behavior. Scales
Research Reports. EIM Business and Policy Research.
Ding, A.-C. E., Shi, L., Yang, H., & Choi, I. (2024). Enhancing teacher AI literacy andintegration through different types of cases in teacher professional development.
Computers and Education Open, 6. https://doi.org/10.1016/j.caeo.2024.100178
Fullan, M., Azorín, C., Harris, A., & Jones, M. (2023). Artificial Intelligence and schoolleadership: Challenges, opportunities and implications. School Leadership andManagement, 1–8. https://doi.org/10.1080/13632434.2023.2246856
García-Martínez, I., Fernández-Batanero, J. M., Fernández-Cerero, J., & León, S. P. (2023).
Analysing the impact of Artificial Intelligence and Computational Sciences on student
performance: Systematic review and meta-analysis. Journal of New Approaches in
Educational Research, 12(1), 171–197. https://doi.org/10.7821/naer.2023.1.1240
Haleem, A., Javaid, M., Qadri, M. A., & Suman, R. (2022). Understanding the role of digitaltechnologies in education: A Review. Sustainable Operations and Computers, 3, 275–
285. https://doi.org/10.1016/j.susoc.2022.05.004

30

IAFOR Journal of Education: Technology in Education

Volume 12 – Issue 2 – 2024

Karakose, T., & Tulubas, T. (2024). School leadership and management in the age ofArtificial Intelligence (AI): Recent developments and future prospects. Educational
Process International Journal, 13(1). https://doi.org/10.22521/edupij.2024.131.1
Kaya, F., Aydin, F., Schepman, A., Rodway, P., Yetişensoy, O., & Demir Kaya, M. (2022).
The roles of personality traits, AI anxiety, and demographic factors in Attitudes
Toward Artificial Intelligence. International Journal of Human–Computer
Interaction, 40(2), 497–514. https://doi.org/10.1080/10447318.2022.2151730
Kelly, S., Kaye, S.-A., & Oviedo-Trespalacios, O. (2023). What factors contribute to theacceptance of artificial intelligence? A systematic review. Telematics and Informatics,
77. https://doi.org/10.1016/j.tele.2022.101925
Le Borgne, Y.-A., Bellas, F., Cassidy, D., Vuorikari, R., & Kralj, L. (2024). (rep.). Teachers’
competences. European Digital Education Hub.
Ng, D. T., Leung, J. K., Su, J., Ng, R. C., & Chu, S. K. (2023). Teachers’ AI digitalcompetencies and twenty-first century skills in the post-pandemic world. Educational
Technology Research and Development, 71(1), 137–161.
https://doi.org/10.1007/s11423-023-10203-6
Obae, C., Niewint-Gori, J., Bellas, F., Pasichnyk, O., Janakievska, G., Gilleran, A., Le
Borgne, Y.-A., Vuorikari, R., Sharma, M., & Kralj, L. (2024). (rep.). Education about
AI. European Digital Education Hub.
Ofosu-Ampong, K. (2024). Beyond the hype: Exploring faculty perceptions and acceptabilityof AI in teaching practices. Discover Education, 3(1).
https://doi.org/10.1007/s44217-024-00128-4
Ofosu-Ampong, K., Acheampong, B., Kevor, M.-O., & Amankwah-Sarfo, F. (2023).
Acceptance of Artificial Intelligence (ChatGPT) in education: Trust, innovativenessand psychological need of students. Information and Knowledge Management, 13(4),37–47. https://doi.org/10.7176/ikm/13-4-03
Owan, V. J., Abang, K. B., Idika, D. O., Etta, E. O., & Bassey, B. A. (2023). Exploring thepotential of artificial intelligence tools in educational measurement and assessment.
Eurasia Journal of Mathematics, Science and Technology Education, 19(8).
https://doi.org/10.29333/ejmste/13428
Rahman, M., & Watanobe, Y. (2023). ChatGPT for education and research: Opportunities,
threats, and strategies. Applied Sciences, 13(9), 5783.
https://doi.org/10.3390/app13095783
Roczniewska, M., Puchalska-Kamińska, M., & Łądka-Barańska, A. (2020). Facing education
reform: Change-related self-efficacy is linked to job insecurity via appraisal.
Economic and Industrial Democracy, 43(3), 1164–1188.
https://doi.org/10.1177/0143831x20975479

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Sánchez-Prieto, J. C., Cruz-Benito, J., Therón, R., & García-Peñalvo, F. J. (2019). How tomeasure teachers’ acceptance of AI-driven assessment in eLearning. Proceedings of
the Seventh International Conference on Technological Ecosystems for Enhancing
Multiculturality. https://doi.org/10.1145/3362789.3362918
Seibert, D., Godulla, A., & Wolf, C. (2021). Understanding how personality affects theacceptance of technology: A literature review. Leipzig. Retrieved fromhttps://nbn-resolving.org/urn:nbn:de:0168-ssoar-75164-7
Sindermann, C., Yang, H., Elhai, J. D., Yang, S., Quan, L., Li, M., & Montag, C. (2022).
Acceptance and fear of Artificial Intelligence: Associations with personality in a
German and a Chinese sample. Discover Psychology, 2(1).
https://doi.org/10.1007/s44202-022-00020-y
Stein, J.-P., Messingschlager, T., Gnambs, T., Hutmacher, F., & Appel, M. (2024). Attitudes
towards AI: Measurement and associations with personality. Scientific Reports, 14(1).
https://doi.org/10.1038/s41598-024-53335-2
Uygun, D. (2024). Teachers’ perspectives on Artificial Intelligence in education. Advances in
Mobile Learning Educational Research, 4(1), 931–939.
https://doi.org/10.25082/amler.2024.01.005
Corresponding author: Rena Alasgarova
Email: rena.alasgarova@mtk.edu.az

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Perception of ChatGPT Usage for Homework Assignments: Students’ andProfessors’ Perspectives

Irena Miljkovic Krecar
VERN’ University, Croatia
Maja Kolega
VERN’ University, Croatia
Lana Jurcec
University of Zagreb, Croatia

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Abstract
In the context of education, the issues of integrating artificial intelligence (AI) into teaching
and maintaining academic integrity in students’ use of AI are particularly relevant. This paper
empirically examined the issue of ChatGPT usage for writing homework from the perspectivesof students and professors. Study research methods included both quantitative and qualitative
approaches. In Study 1, an anonymous questionnaire was administered to 350 Croatianstudents, users of ChatGPT, to investigate their perceptions, attitudes, habits, and intentions
regarding ChatGPT usage for homework assignments. In Study 2, twelve faculty members
were tested on their accuracy of distinguishing between original students’ papers and
ChatGPT-generated papers. For this purpose, 25 different versions of papers for 8 different
courses were prepared. The results of the students’ survey showed that most students still do
not use ChatGPT regularly and have neutral attitudes about its usefulness, ease of use, risks,
and intentions for future use. In addition, they were moderately concerned about ethical issues
around its usage. Differences across gender and field of study were found. Professors, on theother hand, reported having average self-efficacy in appraising authorship, which is in line with
their low average accuracy of 53%. Accuracy in distinguishing was lowest when ChatGPT wasinstructed to write a paper as a student. These results strongly suggest the necessity for clear
guidelines, plagiarism detection tools, and educational initiatives to promote ethical use of AI
technology.
Keywords: academic integrity, attitudes, ChatGPT ethical considerations, higher education,
students’ and professors’ perspectives

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In the dynamic development of higher education, implementing Artificial Intelligence (AI) in
the classroom introduces many different opportunities and even more complex challenges. Oneof the main challenges is how to balance the indisputable advantages of technology withmaintaining academic integrity. Academic integrity is the foundation of higher education,encompassing principles such as responsibility, honesty, trust, and fairness. Kiralj (2020)
emphasizes that students are the driving force behind the future strength of society. Student
honesty is essential, not only for academic success but also for their future work ethics, which
is a prerequisite for economic and social development. The question of authorship andoriginality becomes increasingly complex in an academic environment where AI could
significantly contribute to content creation.
Educators play a crucial role in fostering a culture of academic integrity. By adapting
pedagogical approaches that emphasize critical thinking and originality, educators canempower students to use technology without compromising academic integrity. However, italso means they should adapt to new circumstances and accept new challenges, continuously
improving their digital competencies.
This paper aims to address two important questions:
1. What are the students’ habits and attitudes towards the use of ChatGPT in writing
homework assignments, with an emphasis on the ethical component of their use?
2. Can educators distinguish between the papers generated by ChatGPT and the ones
written by their students?
In the following section, an overview of previous research on the use of AI in education isprovided. Subsequently, the methodology of the empirical study conducted with a group of
students and professors is described. The results are then presented alongside the discussion,
followed by the conclusion with practical guidelines and recommendations.
Literature Review
Many scholars have highlighted multiple benefits of using ChatGPT for students and educatorsfor educational purposes: it could serve as a starting point for personalized learning and provide
personalized feedback; support individual and group research, provide ideas and guidance on
designing and adapting educational content to make it more appealing and accessible tostudents; improve writing skills, especially in English; offer various possibilities for disabledstudents; and improve time efficiency (Dwivedi et al., 2023; Kasneci et al., 2023; Memarian &Doleck, 2023; Rahman et al., 2023; Rasul et al., 2023; Sok & Heng, 2023). This innovative
way of learning could improve student’s motivation and engagement in acquiring new
knowledge and skills, providing educators time to focus on more demanding tasks such as
developing students’ critical thinking, complex problem-solving, responsible decision-making,communication skills, and so on, and give students emotional support in their progress.

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Rasul and colleagues (2023) summarized five main challenges of using GPT in an academic
context: academic integrity, reliability, inability to evaluate and reinforce graduate skill sets,
limitations in assessing learning outcomes, and potential biases and falsified information in
information processing. Further, using GPT could lead to an unfair disadvantage for students
who do not have access to it (Dwivedi et al., 2023).
Previous research shows that both professors and students have a positive attitude towards
using ChatGPT for educational purposes, albeit with concerns, predominantly centered on theaccuracy of the generated data (Chan & Hu, 2023; Kiryakova & Angelova, 2024; Lozano &Blanco Fontao, 2023). Simultaneously, professors have a more negative attitude toward using
ChatGPT, considering cheating and plagiarism as a major challenge (Iqbal et al., 2022;
Nguyen, 2023; Waltzer et al., 2023). The assessment of students’ work in the form of essays,
projects, research papers, or similar tasks, often conducted remotely, is a pivotal component ofhigher education. These assignments serve to evaluate a broad spectrum of learning outcomes,
including the ability to locate, summarize, and paraphrase relevant literature, critical analysis
skills, creativity, innovation, and attitudes. In achieving educational outcomes maintaining
academic integrity despite the easily accessible online tools that can complete most of the work
becomes a great challenge. Although research indicates that online cheating is more prevalentthan traditional offline cheating, studies on online academic dishonesty are still in the earlystage (Chiang et al., 2022). Despite the prevailing belief among most students that cheating is
ethically wrong, they frequently rationalize their engagement in academic cheating(Majstorović, 2016; Waltzer & Dahl, 2023). Prior research suggests that various motivational
factors significantly influence students’ decision to cheat (Miles et al., 2022; Sozon et al., 2024;
Waltzer & Dahl, 2023). For example, lack of understanding of what academic misconduct is,increased pressure on students, fear of failure, time pressure, lack of motivation, lack ofinstitutional policy, perceived risk and penalties, and peer influence. According to Zhao andcolleagues (2022) perceived peer cheating is significantly stronger than other factors and plays
a crucial role in students’ academic cheating behavior. Considering all these factors that
influence attitudes toward academic dishonesty, it is essential to also consider perspectives ontechnology use to gain a more comprehensive understanding of attitudes toward the use of AI
in academic settings. Measuring attitudes toward ChatGPT usage for homework assignments
serves as the initial step towards understanding present behaviors and forecasting future
conduct.
In this paper the Technology Acceptance Model (TAM; Davis, 1989) was used as thefundamental theoretical framework. TAM emerged as a modification of the Theory of
Reasoned Action (TRA; Ajzen & Fishbein, 1980) and the Theory of Planned Behavior (TPB;
Ajzen, 1985), both cited in Ajzen 1991 and is frequently employed to examine how users
interact with various technologies. According to TAM, two factors determine whether a newtechnology will be embraced by its potential users: (1) perceived usefulness and (2) perceived
ease of use. Perceived usefulness is defined as "the degree to which a person believes that using
a particular system would enhance his or her job performance" while perceived ease of use
refers to "the degree to which a person believes that using a particular system would be free ofeffort" (Davis, 1989, p. 320). If a new technology is considered easy to use, there is an increased

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likelihood that it will also be perceived as useful, thereby enhancing its acceptance. Over time,
TAM has evolved to become the key model in understanding the potential acceptance orrejection of technology, which is of special importance for educational challenges in a digital
environment. Although the application of TAM has confirmed its robustness, the model’ssimplicity and the limited understanding of the antecedents of technology acceptance led to thedevelopment of extended versions such as TAM 2 (Venkatesh & Davis, 2000) and TAM 3(Venkatesh & Bala, 2008). Despite these models, numerous scholars continue to use theoriginal TAM, extending it with variables and theories relevant to their specific study contexts(Sukackė, 2019).
Numerous studies have confirmed TAM in the field of education (Abdullah & Ward, 2016;
Granić & Maragunić, 2019; Sherer et al., 2019) showing the significance of perceived
usefulness and perceived ease of use in understanding students’ attitudes towards using
ChatGPT for learning (Obenza et al. 2024; Rahman et al., 2023; Shaengchart, 2023). Also,
several studies have shown that AI can produce high-quality materials in different disciplines(Herbold et al., 2023; Susnjak, 2022; Yeadon et al., 2023) and the educators can hardly
distinguish students’ work from the work written by ChatGPT (Busch & Hausvik, 2023;
Fleckenstein et al., 2024; Liu et al., 2023; Waltzer et al., 2023). Bašić and colleagues (2023)
highlighted that ChatGPT-assisted writing quality is highly connected with previous
knowledge and skills, and inexperienced students could show poorer essay writing
performance with its usage. To make use of all the advantages of AI, students need a solid baseof knowledge to ask proper and relevant questions.
However, there is still a lack of empirical examination of attitudes toward using ChatGPT as ashortcut for writing homework assignments. Most investigations have been conducted within
the domain of English as a Second Language (ESL) instructional essays, with comparatively
fewer studies in other disciplinary contexts. This opens numerous possibilities for further
research into tailoring home essay instructions across diverse subjects, particularly in the social
sciences and humanities, where essay writing constitutes a significant component of thepedagogical process. Additionally, it is crucial to explore the materials produced by ChatGPTin languages other than English, to understand its suitability and potential limitations in
multilingual educational settings. This topic is especially important to educational institutions
considering the importance of academic integrity preservation. Furthermore, while previous
literature strongly suggests that AI (namely ChatGPT) can produce convincing “student
works”, there is a lack of objective empirical evidence on how proficient professors are in
distinguishing between student work from AI-generated content in various courses.
Methodology
Goals
This research has two main goals, first, to empirically examine students’ attitudinalcomponents of cognition, emotions, and behaviors related to the use of ChatGPT in homework

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assignments. Second, to determine professors’ level of accuracy in distinguishing students’
own writing from AI generated homework assignments.
The following research questions were formulated for this purpose:
1. What attitudes, habits, and intentions do students have when utilizing ChatGPT for
writing homework assignments, with a particular emphasis on the ethical dimensionof their usage?
2. Are there significant differences in ChatGPT usage perceptions, attitudes, andhabits between students of different gender and field of study?
3. Can professors accurately discriminate between homework assignments written by
students and those generated by ChatGPT?
An online survey of 350 students from various Croatian universities (Study 1) and a workshop
with 12 professors from VERN’ University1 (Study 2) were conducted in December 2023 toanswer the research questions. This included both quantitative and mixed (quantitativequalitative) approaches. The studies adhered to key ethical considerations, including voluntary
participation, informed consent, and the maintenance of anonymity and confidentiality.
Participants were fully informed about the research purpose, procedures, and their right towithdraw at any time without negative consequences. Ethical approval was obtained from theinstitution’s ethics committee.
To ensure clarity, we first present the method, findings, and discussion of the results of thestudents’ survey, followed by the same information for the sample of professors. Finally, thepaper addresses limitations of the research and provides a common conclusion.
Study 1: Students’ Survey
MethodThe questionnaire for students about their perceptions, attitudes, habits, and ethical issues
related to ChatGPT usage was constructed based on the TAM model (perceived usefulness,
perceived ease of use and intentions) and research questions. Some items were created resulting
from student-led debate (organized for this purpose with 40 students) on the use of ChatGPT
in homework assignments2. Clarity of the items was pilot tested on another group of 15
students. Besides sociodemographic questions, the final version of the questionnaire contained
items about ChatGPT usage in completing homework assignments: (1) habits, (2) level of1

Two of the three authors are employed at VERN’, which was chosen for the research due to practical reasons.
Most of the initial students’ sample are from VERN’ (N=227). When the desired sample size was not achieved,
the call for participants was extended to other Croatian students through the private and professional networks of
the authors.
2
The debate was conducted as part of regular psychology classes with two groups of students from the VERN’
University (Cyber communication and network sciences (N=25) and Business IT (N=15)). The professor who
conducted the debate is also an author of this paper.

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acceptability, (3) attitudes, (4) ethical issues, and (5) perception of professors’ knowledge of
ChatGPT.
Habits were measured by frequency of ChatGPT usage for completing homework assignmentson the scale of five (1 - few times only, 2 - few times a month, 3 - once or twice a week, 4 several times a week, 5 - every day), and a multiple choice-multiple select question about the
purpose of using ChatGPT, offering 16 purposes.
Level of acceptability to use ChatGPT when writing homework was measured by one item on
a 10-point scale, ranging from 1 (to give me some initial information or serve as inspiration)
through 5 and 6 (to take over half of the written content, and shape and complete the rest
myself) to 10 (to completely write a paper or task for me, which I will then hand over to theteacher without changes).
Attitude Towards ChatGPT Usage in Completing Homework Assignments scale contained 16
items rated on a 5-point Likert-type scale, ranging from 1 (absolutely disagree) to 5 (absolutely
agree). Exploratory factor analysis (EFA) extracted a four-factor solution and accounted for
63.5% of the total variance. Each factor consisted of four statements about ChatGPT usage for
writing homework assignments. Intention (F1) measured planned behavior of utilizing
ChatGPT for tasks deemed too challenging, tedious, time-consuming, and so on, for the
students; Risk of Use (F2) referred to students’ diminishing learning adaptability, criticalthinking abilities, and academic skills; Easy and Reliable Use (F3) measured trust in theaccuracy of the given information and the ease of ChatGPT use; Usefulness (F4) was about
fostering the development of students’ computer skills, creativity, interest, and enjoyment (SeeAppendix A). The obtained factors supported both TAM (Shaengchart, 2023; perceived
usefulness, ease of use and intentions to use ChatGPT) and TAME‑ChatGPT (Abdaljaleel etal., 2024; perceived usefulness, behavioral/cognitive factors, perceived risk of use, perceived
ease of use) models and scales. Subscales showed adequate reliability (α) as follows: .85, .80,
.76, .80.
Ethical Issues of ChatGPT Usage in Completing Homework Assignments Scale consists offour-items describing the potential implications ChatGPT usage may have on academic
integrity. Items were rated on a 5-point Likert-type scale, ranging from 1 (absolutely disagree)
to 5 (absolutely agree). EFA extracted a one-factor solution (Appendix B) and accounted for
63.5% of the total variance. Ethical Issues as separate scale is in line with Farhi and colleagues.
(2023) who highlighted concerns regarding ethics of the potential over-reliance on ChatGPT
for educational tasks. The scale showed adequate reliability (α=.77).
Perception of Professors’ Knowledge of ChatGPT were measured with four items focusing on
how students perceive professors’ abilities to detect ChatGPT papers. Items were rated on a 5point Likert-type scale, ranging from 1 (absolutely disagree) to 5 (absolutely agree).
Snowball sampling was used to recruit Croatian students. Along with a link to thequestionnaire, participants were informed about research’s core components and that their

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involvement was voluntary and anonymous, with no credits awarded for participation. The
final sample, consisting of 350 students, was selected based on the criteria of having personal
experience in using ChatGPT for study purposes. 61.1% were females and the total sample
ranged in age from 18 to 46 with a mean age of 23 years (M=22.75, SD=5.52). Students wereat the undergraduate (68.3%) and graduate (31.7%) levels of education from different fields ofstudy: 58% of students were majoring in social sciences, 34.3% in science and technology,
6.3% in humanities, 1.1% in medical sciences, and 0.3% in arts.
Results
Data were analyzed using the Statistical Package for Social Sciences, version 26.0 (SPSS Inc.,
IL). For most variables, the values of skewness and kurtosis were between +1 and -1, except
for the variable purposes. Nonparametric statistics were used for skewed and ordinal variables
and parametric for interval variables with normal distribution. Therefore, with Mann-Whitney(M-W) U test and two-way analysis of variance (ANOVA) we tested the effect of gender andfield of study differences (Cohen d and partial eta squared ηp2) on items and scales of ChatGPT
usage. The level of statistical significance was p=.05.
Students Habits and Acceptability to Use ChatGPT in Completing Homework Assignments
Our analysis (Table 1) showed that most students have not developed the habit of using
ChatGPT in completing their homework assignments. Half of them used it only a few times bynow (50.6%) and the rest of them use it few times a month (24.3%), or on the weekly basis(22.5%), and only few admitted using it daily (2.6%). It was more often used by male thanfemale students (d=0.63), and by science and technology students than social sciences students(d=0.58).
On a 10-point acceptability scale almost one third (29.4%) of students considered ChatGPT
usage as acceptable tool for getting some initial information or serving as inspiration (level 1).
Most students (74.8%) found it unacceptable to take over half of the written content, and shapeand complete the rest themselves (level 5 and 6) while the rest found it acceptable. The usageof ChatGPT to completely write a paper or task (level 10) was considered acceptable by one
percent of the students. The median indicated that the average acceptability level was 3 (Table1). Male students rated its usage as more acceptable than female students did (d=0.24).

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Table 1
Gender and Field of Study Differences in Experiences of ChatGPT Usage in Completing
Homework Assignments
Variables
Frequency

PurposeAcceptabilityGroupsMale
FemaleSoc. Sci.
Sci. Tech.
Male
FemaleSoc. Sci.
Sci. Tech.
Male
FemaleSoc. Sci.
Sci. Tech.

Median
2
1
1
2
5
4
4
5
3
3
3
3

Mean Rank
213.75151.19142.03195.78210.49153.26146.24
188.66190.74165.82
163.12160.10M-W U test
9350

Z
-6.10

p
.000

8127.0

-5.39

.000

9793.5

-5.20

.000

8981.0

-3.98

.000

12479.5

-2.30

.022

11952.5

-0.29

.774

Note. Soc.Sci.=Social Science, Sci.Tech.=Science and Technology

On average, students chose four purposes (Median=4) of using ChatGPT in completing their
homework assignments, and as Figure 1 presents, mostly for finding content that interests them,
deeper analysis of a subject matter and for writing textual assignments (essays, seminars,
reviews, etc.). Male students (d=0.58) and those studying technical sciences (d=0.39) had morereasons for its usage (Table 1), which is in line with the previously mentioned higher frequency
of use within these subsamples.
Figure 1
Reasons for Utilizing ChatGPT in Completing Homework Assignments (%)

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Attitudes and Ethical Issues Regarding ChatGPT Usage in Completing Homework
Assignments
As evident in Table 2, average ratings of 3 (neutral opinion) were obtained on all scales, withstandard deviations suggesting not much variation. Two-way ANOVAs were conducted on thescales to determine potential differences between male (N=130) and female students (N=193),
as well as students majoring in social sciences (N=203) and science and engineering (N=120).
The subsamples for testing interaction effects consisted of males majoring in social sciences(N=50) and in science and engineering (N=80), and females majoring in social sciences(N=153) and in science and engineering (N=40).
Results of ANOVA showed that male students perceived more usefulness (ηp2=.022), and lessrisks (ηp2=.013) in using ChatGPT than female students. They are also intended to use it morethan female students (ηp2=.018). Field of study also showed significant effect on intention andreliability. Social science students had more confidence in ChatGPT (ηp2=.028) and higher
intention to use it for future assignments (ηp2=.027) in comparison to science and technology
students.
Table 2
Gender and Field of Study Differences in Attitudes Towards ChatGPT Usage in Completing
Homework Assignments
Descriptive statistics M (SD

ANOVA

ScalesM

F

Soc.Sci.

Sci.Tech.

Total

Fgender

FfieldFInteraction

Risks

3.13
(1.06)

3.42(0.97)

3.34(0.98)

3.24(1.06)

3.31(1.01)

4.14*

0.03

2.26

Usefulness

3.42(0.98)

3.13(0.91)

3.25(0.96)

3.23(0.93)

3.24(0.95)

7.34**

2.05

2.99

Easy &
Reliable

3.13(0.81)

3.18(0.80)

3.26(0.78)

2.99(0.76)

3.16(0.79).46

9.31**

0.01

Use
Intention

2.88(1.13)

2.71(0.99)

2.87(1.04)

2.62(1.05)

2.78(1.05)

5.95*

8.73**

0.35

EthicalIssues2.70(1.03)

2.95(0.92)

2.87(0.98)

2.82(0.97)

2.85(0.97)

3.23

0.42

8.92**

Note. * p<.05, **p<.01; Soc.Sci.=Social Science, Sci.Tech.=Science and Technology.

The results revealed statistically significant interaction effect of gender and field of study on
perceived ethical issues on ChatGPT usage when doing homework assignments (ηp2=.027). In

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social sciences female students were most concerned about potential implications on academic
integrity while male students had least ethical doubt (Figure 2).
Figure 2
Interaction Effect of Gender and Field of Study on Perceived Ethical Issues on ChatGPT Usage
When Doing Homework Assignments

Perception of Professors’ Knowledge of ChatGPT
Table 3 shows the students mostly had a neutral opinion on professors’ knowledge of ChatGPT.
Nearly half of the students (42.7% and 46.5% answered absolutely or mostly disagree on thefive-point scale for first two items) were completely or mostly convinced in professors’
efficacy of appraising authenticity of student papers, even though nearly equal percentage ofthe students expressed concern about objectivity of the assessment practices (39.4% answered
4-mostly agree or 5-absolutely agree). Twenty-two percent of students agreed that it is up to
the students to manage their assignments, while it is the professors’ responsibility to identify
any misuse.

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Table 3Students’ perceptions of professors’ knowledge of ChatGPT (%)
Items/Scale
Most professors do not verify theauthenticity of student papers.
Teachers cannot detect whether
homework assignments were
written by ChatGPT or the student.
It is up to us students to ‘get by’with the help of ChatGPT, and it is
up to the professors to try to catch
us in that.
Due to ChatGPT, homeworkassignments cannot be objectively
evaluated.

1

2

3

4

5

M

SD

14.0

28.7

30.9

15.8

10.6

2.80

1.18

22.8

23.7

30.6

15.9

6.9

2.60

1.20

31.7

20.6

25.7

12.0

10.0

2.48

1.31

10.4

15.6

34.6

25.6

13.8

3.17

1.16

Discussion
As seen in our sample of Croatian students, male students use ChatGPT more than female,consistent with previous studies (Draxler et al., 2023; Siregar et al., 2023). Similarly, inGermany male students and students of engineering sciences, mathematics and natural sciencesused AI-based tools most frequently (von Garrel & Mayer, 2023). According to Bouzar et al.
(2024) males reported longer usage times, indicating a potentially deeper engagement with thetool, while females demonstrated higher usage frequency, suggesting more frequentinteractions but possibly for shorter durations.
The unethical usage of ChatGPT (to completely write a paper or task) was considered
acceptable by only one percent of the students, with male students rating it more acceptable
than female. Such findings come as no surprise, as many previous studies suggest that maleparticipants are more prone to unethical academic behavior than female participants (Zhang etal. 2017).
Furthermore, results of ANOVA showed that male students perceived more usefulness and lessrisks in using ChatGPT than female students. They also intend to use it more than female
students. Yilmaz and colleagues’ (2023) findings that male students found ChatGPT easier to
use then female students, similarly, sheds light on the gender-specific responses to the userfriendliness of ChatGPT. The authors observed that understanding these gender-specificresponses is crucial for designing AI systems that are inclusive and accessible to all users,
regardless of gender. In our sample we can assume that both genders have equal access to
ChatGPT; therefore, it is crucial to explore why female students exhibit less interest intechnological innovations and have not fully explored the benefits of using this tool.

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Field of study also has a significant effect on intention and reliability. Social science students
had more confidence in ChatGPT and higher intention to use it for future assignments incomparison to science and technology students. This is somewhat surprising as we havepreviously seen that they use it less. We can assume that they are just discovering its
possibilities, as opposed to science and technology students who might have a better knowledge
and steadier dynamic of ChatGPT usage.
Additionally, two-way ANOVA indicated a statistically significant interaction between genderand field of study on perceived ethical issues related to ChatGPT usage. Female students insocial sciences were the most concerned about the potential impact on academic integrity,
whereas male students in social sciences had the least ethical concern. These results are in linewith recent research showing that chatbots are perceived by students as a valuable tool for
extensive research and analysis, often required in disciplines such as social sciences (Chan,
2023; Jowarder, 2023), however, women have more negative attitudes towards academic
dishonesty (Witmer & Johansson, 2015).
Finally, students mostly held a neutral view on professors’ knowledge of ChatGPT. Nearly halfof them were completely or mostly confident in professors’ ability to appraise the authenticityof student papers, although a nearly equal percentage expressed concern about the objectivityof future assessment practices. Additionally, 22% of students agreed that it is the students’
responsibility to find their own way in doing assignments and the professors’ role to catch any
misconduct. Given this attitude, if students are made aware that the consequences forcommitting academic dishonesty were little to non-existent, then students are likely to commit
such acts (DiPietro, 2010). According to San Jose (2022), students interpreted teachers’leniency during the pandemic as implicit consent to cheat on exams and even plagiarize theirsubmitted outputs. This lack of action or tolerance from teachers is likely to lead to an increasein academic dishonesty.
Study 2: Professors’ Survey
MethodMain goal of the professor’s survey was to estimate their (subjective) self-efficacy and actual(objective) efficacy in determining the authorship of student homework. Also, the aim was toraise awareness about possibilities and ease of utilization of AI for unethical purposes among
students, encourage mutual discussion and to initiate certain steps at the institutional level toeffectively address this challenge.
For practical reasons, focus of recruitment was all full-time faculty members of VERN’
University. They were sent an invitation to participate in “the workshop related to the use of
artificial intelligence". Although initially more of them applied, the final workshop had 12
professors from various disciplines. Among them 2 were males and 10 females, and majorityof them have at least 15 years of teaching experience at the same institution (besides one
younger male professor with “only” 8 years of teaching experience).

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Weeks before the study, professors were requested to provide homework instructions fromtheir courses, contributing to the final grade. They were also asked to submit several examples
of student homework of varying quality, focusing on assignments submitted before November
2022 (when ChatGPT became widely used). Based on their contributions, materials for thesurvey were created.
The following eight courses were selected for the study: Academic Writing, Basics ofMarketing, Organizational Psychology, Management, Research Methodology, ICT in
Hospitality, Transmedia Storytelling, and Basics of Entrepreneurship. For each course, 2 to 5
variants of assignments were created (25 in total), including both original student work andassignments written by ChatGPT. The proportion of ChatGPT-generated papers ranged from
50% to 66% per course. Most ChatGPT papers were created using the free 3.5 version, with
the instructions from the professors directly copied as a prompt. More complex papers,
including graphical displays of fictitious results or summaries of scientific papers, were craftedwith a prepaid version 4. In some cases, ChatGPT was instructed to write poorly to simulate astruggling student’s work.
The workshop began with an overview of the survey results conducted on students (describedearlier), with the aim of raising awareness of the presence of AI use and discussion about its
threats to academic integrity. The main part of it was professors’ evaluation of the authorshipof different course papers. Each professor received a folder containing mixed (student and AIgenerated) papers written for a particular course. All texts were formatted uniformly (font, size,alignment), and professors were instructed not to judge based on these elements.
For each paper professors answered three questions:
1) Who wrote the text (AI or student)?
2) How confident are they (on a scale from 1 to 5) in their appraisal?
3) How did they determine (open-ended question) the papers’ authorship?
After reviewing and grading all the papers in the folder, they took another folder, with adifferent course, and passed their folder to the next evaluator. Professors were instructed tokeep their grades sealed in a closed folder to avoid influencing the subsequent assessors. Theyevaluated the texts at their own pace and were not required to review all 8 courses but wereencouraged to select courses related to their teaching area. After about 45 minutes it wasdeemed that the professors were saturated, and the exercise concluded, with each course being
evaluated by at least 5 professors, and up to 9 in some cases.
Results
Before being provided with materials to assess assignments authorship, professors selfappraised (using the Curi.live platform) their efficacy in this task, on one question with answers
ranging from 1 (very low) to 5 (very high). The average score obtained from 12 teachers was
3, indicating a medium level of self-efficacy.

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A review of the completed evaluation sheets revealed that the accuracy of the assessed paper’s
originality varied significantly, ranging from 17% to 100% across the 8 courses and 25 papers.
The average overall accuracy rate was 53.75% which is akin to a roll of the dice. The average
confidence in one’s assessment also fluctuates, ranging from 3 to 4.33 per individual task;
however, as evident, this confidence is not accompanied by corresponding accuracy. Table 4
presents the main findings.
Table 4.
Results of Professors’ Evaluation of Authorship
Course (task description)

Academic Writing(5 paragraph essays ofdifferent types in English)

Basics of Marketing(1 page, survey questions fordifferent survey topics)

Organizational psychology(2 pages; parts of researchseminar with results section,e.g., graphs on different
survey questions)Management(1 page; movies reviews
written to show a clear
association to management)

Methodology(Research outline - problems,
goals, hypothesis, method,sources- in PPT on 10 slides)

Tasks(author)AW1 (student)
AW2 (student)
AW3 (AI)AW4 (AI as poor student)
AW5 (AI)
Average accuracy /certainty
BM 1 (student)
BM 2 (AI)BM 3 (AI as poor student)
Average accuracy /certainty
OP 1 (student)
OP 2 (AI)OP 3 (AI, asked to write
about known Croatiancompany)
Average accuracy /certainty
MAN 1 (student)
MAN 2 (AI)MAN 3 (student)
MAN 4 (AI)Average accuracy /certainty
MET 1 (student)
MET 2 (AI, as poor student)
MET 3 (AI)Average accuracy /certainty

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Accurately / Average
Total assessed certainty
4/6
3,17
3/6
3,33
6/6
3,67
2/6
4,17
6/6
3,67
70%
3,6
3/5
2/5
1/5
40%

4,2
3,75
3,5
3,82

6/6
3/6
2/5

3,33
3,4
3,33

63%

3,35

4/6
2/6
1/6
1/6
33%

4
3
3,5
3,33
3,82

4/6
1/6
6/6
61%

3,86
4
4,29
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ICT in hospitality(2 pages of a larger seminar;
ex of ICT in hospitality foundin scientific papers)
Transmedia storytelling(1 page; 2 narrativeextensions of the same
Croatian movie)
Basics of entrepreneurship(1 page; part of a larger paper- an interview with anentrepreneur)

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ICT 1 (student)
ICT 2 (AI)Average accuracy /certainty
TMS 1 (AI)TMS 2 (AI)TMS 3 (student)
Average accuracy /certainty
BE 1 (student)
BE 2 (AI, asked to write
emotionally)
Average accuracy /certainty

4/8
4/8
50%

3,63
3
3,32

5/6
2/5
4/6
63%

4
4
4,33
4,11

6/9
3/9

4,2
3,78

50%

3,99

For the Academic Writing course, the average accuracy is the highest among all courses at
70%. Moreover, one person appraised the authorship of all four essays correctly. On the other
hand, if the AI is instructed to write a poorly crafted paper, professors most frequently misjudge
the originality, confidently assuming it to be a student’s work. This finding is significant, as
students can easily direct AI to produce a paper resembling a student’s work, specifying thedesired educational level and quality.
In the Basics of Marketing, the accuracy of assessment was 40%, worse than guessing by
chance. It might be because, when instructed to mimic a poor student’s work, ChatGPT
produced highly convincing texts. However, one person (not teaching that course) appraised
all three papers correctly. There was a clear correlation between the accuracy of assessment
and the confidence in these judgments. The highest average certainty corresponded with themost accurately assessed work, and vice versa.
For Organizational Psychology, the accuracy was high, but certainty in judgments was low.
All teachers accurately identified the original student work, likely due to numerous typos. One
person (professor of the same course) appraised all three papers correctly. The poorest
estimation was for work where AI was instructed to fabricate research on employees of a
specific Croatian company, leading professors to believe it was genuine student work. The low
certainty in judgments might also stem from ChatGPT 4 creating graphs based on fictionaldata, a feature unfamiliar to many professors (since it is not available in the free 3.5 version).
In the Management course, the lowest accuracy in total was observed (33%) and no one
correctly appraised all four papers. ChatGPT convincingly wrote film reviews, drawing clear
parallels with the requested topic and choosing films akin to typical student selections, leadingteachers to believe these were authentic student works.

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In Methodology, assessment accuracy was among the highest (61%), but no one appraised all
the papers correctly. Also, the poorest accuracy was, once again, visible, when ChatGPT wasinstructed to write a paper as a bad student.
For the ICT in Hospitality course, the average accuracy was 50%, equivalent to guessing, andthree professors (not lecturers of the same course) were correct for both papers. Interestingly,
professors used nearly identical arguments to classify a work as either student or AI-created.
Transmedia Storytelling was evaluated with the highest certainty, and its accuracy was among
the better ones (63%), however only one professor (not of the same course) appraised all thethree papers correctly. A few of the professors noticed that all the nouns in the text were
capitalized, leading them to correctly conclude that the text was AI-generated.
Finally, for the Basics of Entrepreneurship, appraised by most professors (N=9), the average
accuracy was 50%, not any better than guessing. Three professors appraised both papers
accurately (two of them not lecturing the same course, the third one being anonymous). The
AI’s capability to write emotional and personal texts misled professors into believing thesewere student papers, which reflected in a high average certainty of 3.99 in these appraisals.
In Table 5, accurate and inaccurate arguments used by professors to assess the authorship ofpapers are collectively presented. Arguments employed in the evaluation of genuine student
work and ChatGPT-generated work are shown separately. It is evident that professors’
assessment criteria are inconsistent, with identical arguments sometimes used to declaresomething as original student work or to reject its originality.

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Table 5
Correct and Incorrect Professors’ Arguments for Assessing the Papers’ Authorship
Arguments /AuthorStudent

ChatGPT

Correct Arguments

Incorrect Arguments

Content:
personal details; realistic; childish;shortcomings in the description of
results; expression; illogical; imprecise;
typical student mistakes; unrelated; itrefers to examples; specific details; itsounds authentic; expected work for the
student; own opinion.

Content:
no emotions; too professional;
no feelings.

Form:
spelling; spelling errors; grammar;
minor text errors; first-person writing;writing style; simple language; typos;
mistakes; I form; short sentences; nonumbered tables; formatting tables;

Form:language; sentence structure;
no structure, spelling; nosource; a lot of foreign terms;
style; non-standard Croatian
language; advanced language;
too good wording for a student;
good English.
Content:
seems naive; a personal
perspective; gut feeling; as
expected for a student; toorealistic for GPT; like a song;modest; too casual; passionate
with own opinion; short andpersonal; emotional
expressions

Content:
Arguments; no sources; stereotypically;
too professional; it is not the level ofstudents; too good and advanced;
complex expressions; great photos; too
general; too poetically retold;
impeccable.

Form:brief; writing style; flawless; very neat;technical elements; style and language;eloquently; school template; 1st letter
capitalized; uppercase and lowercase
letter; colon then enumeration; pictures;
ChatGPT style; ornate literary style

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Form:
style as a student; writing style;strange constructions; sentence
structure; spelling; language,style;

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Discussion
Before the emergence of ChatGPT and other similar AI tools, various graphic elements – suchas different fonts within the same text or differently colored backgrounds of text paragraphs –were used by professors for quick detection of texts that were copy-pasted directly from theinternet. However, that "old" plagiarism detection argument in AI simply "doesn’t hold water."
As seen, our professors have a medium level of self-efficacy (M=3) in assessing the authorshipof student assignments versus AI-generated papers. As the sample consisted of 12 highly
motivated professors who voluntarily joined the research it is reasonable to presume that these
professors may have been more critically oriented and had a lower level of confidence in their
judgments compared to other professors who did not respond to the invitation to the workshop,
considering that they did not need it. On the other hand, their self-criticism is justified since
the objective assessment accuracy is low (53.75%). In a similar research Busch and Hausvik(2023) found that the authors were correctly identified in 60% percent of both the ChatGPTgenerated and student-written exam answers. However, as they tested the accuracy in rating
exam questions, their results are not fully comparable with ours, as we tested various
homework assignments, that usually produce higher quality and quantity variance among
students. We therefore assume that in more complex tasks the authorship will be lessdetectable, and with AI development this problem will only get bigger.
Busch and Hausvik (2023) also suggested that teaching experience correlates with higher
accuracy rates. In our sample (although small) we only had experienced professors, so we canspeculate whether the accuracy rate would be lower on a more heterogeneous sample orwhether it is impossible to go below the random guessing rate. Similarly, Liu et al. (2023)
suggest that training (previous exposure) can enhance participants’ ability to distinguishbetween student essays and machine-generated text. Likewise, our results indicate (Table 5)that some AI work is more easily noticeable to (some) professors, e.g., the one that follows the
typical format of ChatGPT output, with headings, subheadings, and bullets. This kind ofobservation can only be drawn from personal experience with ChatGPT, which suggests thatprofessors need to familiarize themselves with the AI tools that their students use. Some
professors do not have any experience with ChatGPT and are still surprised with its capabilities
and are therefore easier to deceive. Anyone who experiments with it a little can ascertain its
capability to produce emotional, creative, personal, and witty content, not to mention thecapabilities of premium ChatGPT 4.0 version. Over time, professors will hopefully learn the
typical style of texts generated by ChatGPT (or similar popular tools) and will be able to detect
at least those who have not made the effort to even format the text independently, let alone
write it.
Fleckenstein and colleagues (2024) found that professors were overconfident in their
judgments, particularly when they thought a text was written by a student and when the text
was of low quality. Likewise, in this research, professors overall tend to label papers that are"too good" as plagiarism, while those showing errors, writing clumsiness, or emotional
connotations are deemed original student work. As shown in Table 4, when ChatGPT is
instructed to write papers from the perspective of a student, a poor student, or emotionally,

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professors exhibit the lowest detection accuracy. In other words, it only takes minimal
modification to ChatGPT prompt to make its output almost undetectable. Therefore, it isessential for professors to remain vigilant and continuously update detection methods.
Limitations
This research has a few limitations that need to be considered. Firstly, the sample size of
professors was relatively small and consisted mainly of experienced middle-aged individuals.
Although the invitation to participate in the research was sent to all professors from the chosen
university (33), only 12 responded. This suggests those who participated were particularly
motivated and interested in AI in higher education. Additionally, while the student sample wasmoderately large, it may not fully represent the wider student population as students of social
sciences (58%) and females (61.1%) dominated the sample. To address these limitations, future
research should aim for larger and more diverse samples.
Secondly, while e-survey questionnaires are convenient, cost-effective, and eco-friendly, they
can introduce biases such as self-selection. Students who are more interested or motivated may
be more inclined to participate, skewing the results. Furthermore, there is a risk of social
desirability bias, particularly considering the potential for using ChatGPT as an academic
cheating tool. To delve deeper into this issue, future research should employ a combination ofqualitative and quantitative methods for both students and professors.
Lastly, the rapid evolution of technology may render some findings less relevant over time.
Currently, many students and professors may not fully grasp the possibilities offered by AI,
and institutions may lack comprehensive strategies for addressing academic integrity concerns
related to AI. However, AI will become more integrated into higher education as an additional
educational tool. Therefore, conducting longitudinal research at regular intervals is
recommended to track changes in attitudes and technology usage in higher education.
Conclusion, Implications, and Recommendations
Throughout history, the emergence of new technologies has consistently elicited both
enthusiasm and resistance, from various, and sometimes the same, individuals. The latter seems
to especially apply to the emergence of language models like ChatGPT, which many perceive
as the greatest technological breakthrough since the advent of the internet.
Kelly, Sullivan, and McLaughlan (2023) examined news articles about the influence of
ChatGPT on higher education and highlighted that the media focus was on academic integrity
issues more frequently than educational opportunities. As the authors suggested, reading about
ChatGPT mainly as a new tool for better cheating, more often than a possibility for better
learning, can influence students’ attitudes and behavior. A similar hype occurred in Croatian
media. However, our findings suggest, most Croatian students still do not regularly use
ChatGPT to complete their homework assignments, although, by the time of publication of thispaper, we certainly expect an increase in its usage.

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Currently, among regular users there are more male than female students, and more scienceand technology students compared to social sciences students. On average, students cite fourmain reasons for using ChatGPT in completing their homework assignments, among which thepredominant ones are finding content that interests them, deeper analysis of a subject matter,and, in third place (with almost 50% frequency of selection), for writing textual assignments(essays, seminars, reviews, etc.). The latter can be considered unethical usage, especially if it
involves uncritical copy-pasting. However, there is some hope in the findings, on the level of
acceptability of using this tool in homework assignments, with the majority considering it to
be unacceptable to literally copy more than half content (with gender differences once again
noted in the same direction). Hopefully students mostly perceive it as an auxiliary tool, as itshould be, and not a complete replacement for their own effort.
Our research shows that professors are almost powerless against such content, that is, theycannot accurately assess the authorship of the work – with the average accuracy rate of 53%.
Even the fact that a particular professor teaches the course he/she is evaluating is not aguarantee of accuracy.
A mitigating circumstance for professors is that there will always be a proportion of easily
detectable students, who are investing minimal effort even into plagiarism, delivering papers
they did not edit or even read. Unfortunately, even they can easily be taught how to create
highly convincing papers, just by slightly adjusting the prompt (e.g. “write a paper as if you
were an average student”).
A major institutional challenge here will be managing the damage caused by all of those who
consider plagiarism evidence of their resourcefulness, see no ethical problem in it, and evenboast about it. When this becomes a common occurrence in a system, conscientious individuals
perceive “distributive organizational injustice”. This becomes demotivating and leads tofeelings of injustice, frustration, and loss of trust in the assessment system. Furthermore, it
undermines fundamental principles of education such as honesty, integrity, and effort.
Professors play a significant role in how they will handle this relatively new situation – whether
to deny the existence of a freely available version of the program that can produce work inseconds, which would otherwise take students days, or to clearly communicate to students the"elephant in the room" and institutionally formalize procedures for this.
The fact that students are mostly unsure of what professors know and can do regarding AI
plagiarism detection (as seen in our data) leaves room for educational institutions to manipulatefear, at least until adequate software is developed that can more accurately detect plagiarism inAI-generated papers. Of course, a more ethical and sustainable solution in the long run is tofocus on education and nurture, for example, the development of internalized beliefs that such
behavior is inappropriate and unsustainable. Fostering a culture of integrity and respect for
academic rules should be encouraged. Educational institutions need to have clear policies andprocedures for detecting and penalizing academic dishonesty to ensure fair and equal

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assessment of all students. This includes the necessity for clear guidelines, plagiarism detection
tools, and educational initiatives to promote the ethical use of technology, specifically AI tools.
Acknowledgement
We would like to thank all the participants, especially the professors of VERN’ University whoparticipated in the workshop, and the students of the same institution, whose constructive
contributions helped to compose the questionnaire used in the research. Acknowledgement
should also be given to ChatGPT, which was used for creating materials for professors’ survey,
and for proofreading the text to improve its grammatical and linguistic accuracy.

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References
Abdaljaleel M., Barakat, M., Alsanafi, M. et al. (2024). A multinational study on the factors
influencing university students’ attitudes and usage of ChatGPT. Scientific Reports
14(1), 1983. https://doi.org/10.1038/s41598-024-52549-8
Abdullah, F., and Ward, R. (2016). Developing a general extended technology acceptancemodel for E-learning (GETAMEL) by analysing commonly used external factors.
Computers in Human Behavior 56, 238–256.
https://doi.org/10.1016/j.chb.2015.11.036
Ajzen, I. (1991). The theory of planned behaviour, Organizational Behaviour and Human
Decision Process, 50 (2), 179–212. https://doi.org/10.1016/0749-5978(91)90020-T
Basic, Z., Banovac, A., Kružić, I. & Jerković, I. (2023). ChatGPT-3.5 as writing assistance instudents’ essays. Humanities and Social Sciences Communications, 10(1).
https://doi.org/10.1057/s41599-023-02269-7
Bouzar, A., El Idrissi, K., Ghourdou, T. (2024) Gender differences in perception and usage of
ChatGPT, International Journal of Humanities and Educational Research 6(2), 571–
582. https://doi.org/10.47832/2757-5403.25.32
Busch, P. A., & Hausvik, G. I. (2023). Too Good to Be True? An Empirical Study of
ChatGPT Capabilities for Academic Writing and Implications for Academic
Misconduct. Paper presented at the Twenty-ninth Americas Conference on
Information Systems, Panama.
Chan, C. K. Y. (2023). A comprehensive AI policy education framework for university
teaching and learning. International Journal of Educational Technology in Higher
Education, 20(1), 1–25. https://doi.org/10.1186/s41239-023-00408-3
Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: Perceptions, benefits,
and challenges in higher education. International Journal of Educational Technology
in Higher Education, 20(1), 43. https://doi.org/10.1186/s41239-023-00411-8
Chiang, F.-K., Zhu, D., & Yu, W. (2022). A systematic review of academic dishonesty inonline learning environments. Journal of Computer Assisted Learning, 38(4), 907–
928. https://doi.org/10.1111/jcal.12656
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of
information technology. MIS Quarterly, 13(3), 319–340.
https://doi.org/10.2307/249008
DiPietro, M. (2010). 14: Theoretical frameworks for academic dishonesty. To Improve theAcademy, 28(1), 250–262. https://doi.org/10.1002/j.2334-4822.2010.tb00606.x
Draxler, F., Buschek, D., Tavast, M., Hämäläinen, P., Schmidt, A., Kulshrestha, J., Welsch,R., (2023). Gender, Age, and Technology Education Influence the Adoption andAppropriation of LLMs. In Manuscript submitted to ACM. ACM, New York, NY,
USA.

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Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A.
M., Koohang, A., Raghavan, V., Ahuja, M., Albanna, H., Albashrawi, M. A., AlBusaidi, A. S., Balakrishnan, J., Barlette, Y., Basu, S., Bose, I., Brooks, L., Buhalis,D., … Wright, R. (2023). “So what if ChatGPT wrote it?” Multidisciplinary
perspectives on opportunities, challenges and implications of generative
conversational AI for research, practice and policy. International Journal of
Information Management, 71. https://doi.org/10.1016/j.ijinfomgt.2023.102642
Farhi, F., Jeljeli, R., Aburezeq, I., Dweikat, F. F., Al-shami, S. A., & Slamene, R. (2023).
Analyzing the students’ views, concerns, and perceived ethics about ChatGPT usage.
Computers and Education: Artificial Intelligence, 5, 100180.
https://doi.org/10.1016/j.caeai.2023.100180
Fleckenstein, J., Meyer, J., Jansen, T., Keller, S., Köller, O. & Möller, J. (2024). Do teachers
spot AI? Evaluating the detectability of AI-generated texts among student essays.
Computers and Education: Artificial Intelligence, 6.
https://doi.org/10.1016/j.caeai.2024.100209
Granić, A., & Marangunić, N. (2019). Technology acceptance model in educational context:
A systematic literature review. British Journal of Educational Technology, 50(5),2572–2593. https://doi.org/10.1111/bjet.12864
Herbold, S., Hautli-Janisz, A., Heuer, U. et al. A large-scale comparison of human-written
versus ChatGPT-generated essays. Scientific Reports 13, 18617.
https://doi.org/10.1038/s41598-023-45644-9
Iqbal, N., Ahmed, H., & Azhar, K. A. (2022). Exploring teachers’ attitudes towards using
ChatGPT. Global Journal for Management and Administrative Sciences 3, 97–111.
https://doi.org/10.46568/gjmas.v3i4.163
Jowarder, M. I. (2023). The Influence of ChatGPT on Social Science Students: Insights
Drawn from Undergraduate Students in the United States. Indonesian Journal of
Innovation and Applied Sciences (IJIAS), 3(2), 194–200.
https://doi.org/10.47540/ijias.v3i2.878
Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., …
Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large
language models for education. Learning and Individual Differences, 103, Article
102274. https://doi.org/10.1016/j.lindif.2023.102274
Kelly, A., Sullivan, M., & McLaughlan, P. (2023). ChatGPT in higher education:
Considerations for academic integrity and student learning. Journal of Applied
Learning and Teaching, 6(1). https://doi.org/10.37074/jalt.2023.6.1.17
Kiralj, R. (2020). Akademska čestitost studenata – preduvjet gospodarskog razvoja. Radovi
Zavoda za znanstvenoistraživački i umjetnički rad u Bjelovaru, 0(14), 148–182.
https://doi.org/10.21857/m3v76te4ny
Kiryakova, G.; Angelova, N. (2023) ChatGPT—A Challenging Tool for the University
Professors in Their Teaching Practice. Educational Science, 13, 1056.
https://doi.org/10.3390/educsci13101056
Liu, Y., Z. Zhang, W. Zhang, S. Yue, X. Zhao, X. Cheng, Y. Zhang, and H. Hu. 2023.
ArguGPT: Evaluating, Understanding and Identifying Argumentative Essays
Generated by GPT Models. arXiv preprint arXiv:2304.07666; Retrieved fromhttps://arxiv.org/abs/2304.07666.

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Lozano, A.& Blanco Fontao, C. (2023). Is the education system prepared for the irruption of
artificial intelligence? A study on the perceptions of students of primary education
degree from a dual perspective: current pupils and future teachers. Educational
Science. 13, 733. https://doi.org/10.3390/educsci13070733
Majstorović, D. (2016). Stavovi studenata korisnika Nacionalne i sveučilišne knjižnice u
Zagrebu o plagiranju i javnoj objavi ocjenskih radova. Vjesnik bibliotekara Hrvatske,
59 (3–4), 131–152.
Memarian, B., & Doleck, T. (2023). ChatGPT in education: Methods, potentials andlimitations. Computers in Human Behavior: Artificial Humans, 1(2), 100022.
https://doi.org/10.1016/j.chbah.2023.100022
Miles, P. J., Campbell, M., & Ruxton, G. D. (2022). Why students cheat and how
understanding this can help reduce the frequency of academic misconduct in higher
education: A literature review. Journal of Undergraduate Neuroscience Education,20(2), A150.
Nguyen, T. C. (2023). University Teachers’ Perceptions of Using ChatGPT in Language
Teaching and Assessment. In Proceedings of the AsiaCALL International
Conference 4, 116–128. https://doi.org/10.54855/paic.2349
Obenza, B., Salvahan, A., Rios, A. N., Solo, A., Alburo, R. A., & Gabila, R. J. (2024).
University Students’ Perception and Use of ChatGPT: Generative Artificial
Intelligence (AI) in Higher Education. International Journal of Human Computing
Studies, 5(12), 5–18.
Pandan, M. V., & Lomibao, L. S. (2022). Integrity of Mathematics Classroom Assessment in
the New Normal. American Journal of Educational Research, 10(5), 282–287.
Rahman, M. S., Sabbir, M. M., Zhang, D. J., Moral, I. H., & Hossain, G. M. S. (2023).
Examining students’ intention to use ChatGPT: Does trust matter? Australasian
Journal of Educational Technology, 39(6), 51–71. https://doi.org/10.14742/ajet.8956
Rasul, T., Nair, S., Kalendra, D., Robin, M., de Oliveira Santini, F., Ladeira, W. J., ... &Heathcote, L. (2023). The role of ChatGPT in higher education: Benefits, challenges,
and future research directions. Journal of Applied Learning and Teaching, 6(1).
https://doi.org/10.37074/jalt.2023.6.1.29
San Jose, A. E. (2022). Academic Integrity of Students during the COVID-19 Pandemic: A
Mixed Method Analysis. European Journal of Education and Pedagogy, 3(4), 97–103.
https://doi.org/10.24018/ejedu.2022.3.4.400
Shaengchart, Y. (2023). A conceptual review of TAM and ChatGPT usage intentions among
higher education students. Advance Knowledge for Executives, 2(3), 1-7.
Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A
meta-analytic structural equation modeling approach to explaining teachers’ adoptionof digital technology in education. Computers & Education, 128, 13–35.
https://doi.org/10.1016/j.compedu.2018.09.009
Siregar, F. H., Hasmayni, B., & Lubis, A. H. (2023). The Analysis of ChatGPT Usage Impact
on Learning Motivation among Scout Students. International Journal of Research andReview, 10(7), 632–638. https://doi.org/10.52403/ijrr.20230774
Sok, S., & Heng, K. (2023). ChatGPT for education and research: A review of benefits andrisks. SSRN 4378735. https://doi.org/10.2139/ssrn.4378735

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Sozon M, Mohammad Alkharabsheh O. H., Fong P. W. & Chuan S. B .(2024). Cheating andplagiarism in higher education institutions (HEIs): A literature review [awaiting peer
review]. F1000Research, 13, 788. https://doi.org/10.12688/f1000research.147140.1
Sukackė, V. (2019). Towards extending the original technology acceptance model (TAM) fora better understanding of educational technology adoption. SOCIETY.
INTEGRATION. EDUCATION. Proceedings of the International Scientific
Conference, 5, 525–549. https://doi.org/10.17770/sie2019vol5.3798
Susnjak, T. (2022) ChatGPT: The End of Online Exam Integrity? ArXiv Preprint
ArXiv:2212.09292.
von Garrel, J., & Mayer, J. (2023). Artificial Intelligence in studies—use of ChatGPT and AIbased tools among students in Germany. Humanities and Social Sciences
Communications, 10, 1–9. https://doi.org/10.1057/s41599-023-02304-7
Venkatesh, V. and Davis, F. D. (2000) A Theoretical Extension of the Technology
Acceptance Model: Four Longitudinal Field Studies. Management Science, 46, 186–
204. https://doi.org/10.1287/mnsc.46.2.186.11926
Venkatesh, V. and Bala, H. (2008) Technology Acceptance Model 3 and a Research Agenda
on Interventions, Decision Sciences 39 (2), 273–315.
https://doi.org/10.1111/j.1540-5915.2008.00192.x
Waltzer, T., Cox, R. L., & Heyman, G. D. (2023). Testing the ability of teachers and students
to differentiate between essays generated by ChatGPT and high school students.
Human Behavior and Emerging Technologies, 1–9.
https://doi.org/10.1155/2023/1923981
Waltzer, T., & Dahl, A. (2023). Why do students cheat? Perceptions, evaluations, andmotivations. Ethics & Behavior, 33(2), 130–150.
https://doi.org/10.1080/10508422.2022.2026775
Witmer, H., Johansson, J. (2015). Disciplinary action for academic dishonesty: does thestudent’s gender matter? Internation Journal of Educational Integrity, 11, 6.
https://doi.org/10.1007/s40979-015-0006-2
Zhang, Y., Yin H. & Zheng, L (2017): Investigating academic dishonesty among Chinese
undergraduate students: does gender matter? Assessment & Evaluation in Higher
Education. https://doi.org/10.1080/02602938.2017.1411467
Zhao, L., Mao, H., Compton, B. J., Peng, J., Fu, G., Fang, F., Heyman, G. D., & Lee, K.
(2022). Academic dishonesty and its relations to Peer cheating and culture: A metaanalysis of the perceived peer cheating effect. Educational Research Review, 36,100455. https://doi.org/10.1016/j.edurev.2022.100455
Yeadon W, Inyang O, Mizouri A et al. (2023) The death of the short-form physics essay inthe coming AI revolution. Physics Education 58(3), 035027.
https://doi.org/10.1088/1361-6552/acc5cf
Yilmaz, H., Maxutov, S., Baitekov, A., & Balta, N. (2023). Student attitudes towards
chatGPT: A technology acceptance Model survey. International Educational
Review, 1(1), 57–83. https://doi.org/10.58693/ier.114
Corresponding author: Irena Miljkovic Krecar
Email: irena.miljkovic@vern.hr

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Appendix A
Exploratory Factor Analysis of Attitude Towards ChatGPT Usage in Education Scale
Items
For tasks I don’t have time for, I plan to use ChatGPT to do themfor me.
For tasks that are too difficult for me, I plan to use ChatGPT to dothem for me.
For tasks that I find uninteresting or don’t feel like doing, I plan
to use ChatGPT to do them for me.
It’s pointless to invest hours and days in writing a paper orassignment when ChatGPT can write it in seconds.
With frequent use of ChatGPT in seminar preparation, students
will not be able to develop academic skills such as paraphrasing
text, compiling various sources, or summarizing text.
With frequent use of ChatGPT, students will not be able todevelop digital literacy skills (skills for finding, evaluating, andusing information found on the internet).
By using ChatGPT for homework assignments, we negatively
impact the development of critical thinking skills on theassignment topic.
If students don’t invest time and effort in attempting to solve theirassignments themselves, but instead simply rely on ChatGPT to
do it for them, they won’t be able to learn the material well.
ChatGPT is a reliable source of information for writing mosthomework assignments.
I trust the accuracy of the information provided by ChatGPT.
ChatGPT is easy to use for most student tasks.
It’s easy to get ChatGPT to do what I want it to do.
Using ChatGPT for homework assignments promotes thedevelopment of students’ computer literacy skills.
Using ChatGPT for homework assignments fosters creativity.
Using ChatGPT for homework tasks is fun and interesting.
If ChatGPT is already freely available, I don’t see why I wouldn’t
use it to help me write a paper or solve a task.
Extraction Sums of Squared Loadings

F1

F2

F3

F4

.85
.79
.76
.73
.82

.75

.75

-.33

.73
.81
.75
.75
.71
.80
.77
.65
.54

5.19

2.70

1.35

1.22

% of Variance32.44 16.86 8.42 7.59
Note. Principal Component Analysis, Oblimin with Kaiser Normalization. KMO=.85, Bartlett’s Test of
Sphericity (Χ2=2285.61, p <.001).
Factor 1: Intention, Factor 2: Risks of Use Factor 3: Easy Reliable Use, Factor 4: Usefulness.

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Appendix B
Exploratory Factor Analysis of Ethical Issues of Chatgpt Usage in Writing Assignments
Items

Factor loading

Using ChatGPT to complete homework assignments is ethically0.85
unacceptable to me.
The use of ChatGPT in writing assignments should be prohibited.
0.86
By using ChatGPT to write homework assignments, we compromise the
0.82
ethical principles of studying.
I don’t see an ethical issue in submitting to the professor a paper that
-0.54
was entirely or mostly written by ChatGPT.
Extraction Sums of Squared Loadings
2.42% of Variance60.39
2Note. Principal Component Analysis. KMO=.75, Bartlett’s Test of Sphericity (Χ =430.82, p <.001).

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Developing a Course on Human-Centered Machine Learning-Based
Product Design
Eui-Chul Jung
Seoul National University, South Korea
Meile Le
Hanyang University, South Korea

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Abstract
Interpreting and incorporating machine learning technology from a human perspective helps
define the role of product designers in the era of artificial intelligence. With this background,
this study developed a 7-week design course about machine learning-based product design.
Subsequently, in Fall 2023, a class with seven undergraduate students from different majors
was held on the subject of “home camera.” The design process consisted of (a) user researchand need definition based on machine learning understanding and (b) machine learning-based
interaction design development using a coding platform. The course was designed to explore
the role of designers who implement human-centered design planning and prototypes
considering machine learning tools. In addition, the course provided a basis for suggesting thedirection of design education methods given emerging AI technology. The essence of design
education is not merely about efficiency; rather, it is about nurturing designers’ ability to thinkcritically and use tools to cultivate innovation.
Keywords: design education, designer role, human-centered design, machine learning, ML
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With the rapid advancement of technology, machine learning (ML) has become an
indispensable tool for enhancing and optimizing user experience (UX) design of product use.
ML applications are pervasive in our daily lives, whether through apps or online services. They
perform various tasks, such as detecting and filtering spam, sorting or curating media feeds,
predicting estimated driving times, converting speech to text, and auto-correcting typing errors.
These applications significantly enhance the quality of interaction between users andtechnology, demonstrating the substantial potential of ML to improve interactive experiences.
Despite the advancements in technology, the market frequently encounters failures with MLbased products. Dove and colleagues (2017) criticized that developers and designers often use
ML technology more as a marketing gimmick rather than a tool to address real user needs, apractice aptly described as ‘putting lipstick on a pig.’ Furthermore, the implementation of ML
does not always enhance UX. Errors in prediction and opaque decision-making processes canlead to user frustration. For instance, overly personalized recommendation systems may
confine users within ‘filter bubbles’ (Areeb et al., 2023), limiting their exposure to new anddiverse content. Automated customer service systems often fall short of providing satisfactory
solutions for complex or non-standard issues. The opaque ‘black box’ nature of these systems
can erode user trust. More critically, ML systems have been shown to amplify social
inequalities and biases (Holstein et al., 2019), exacerbating issues like gender bias andcontradictions (Ahn et al., 2022). Such challenges include privacy violations, data biases, andbroader issues of inequity, all of which pose hurdles in the UX design of ML-based products.
To address these challenges, product designers must move beyond the traditional foci on
usability, utility, and interaction aesthetics towards more comprehensive design strategies.
There is a pressing need to incorporate courses in design education that blend human-centered
design thinking with ML to truly meet user needs. Significant research and curriculum
development have already focused on the technical aspects of ML, such as those documented
by Sulmont and colleagues (2019) and Munir and team (2022). However, as ML products
increasingly encounter market bottlenecks and issues, more researchers are recognizing thatthe development of ML products should center more on human-centered design, not justtechnological innovation. Many studies, including those by Yang and colleagues (2021) and
Riedl (2019), have analyzed the challenges ML products face from the perspectives of social
ethics and user behavior psychology. Yet, from the standpoint of design education, the researchand practice on how to educate future designers to comprehensively utilize ML technology inthe design process while considering user needs are still very limited.
Therefore, this study proposed a human-centered product design planning and prototype
creation course based on machine learning. The purpose of the course is to cultivate designers
and developers who are more attentive to fulfilling actual user needs and enhancing UX whenutilizing ML technologies. In this study, a mixed-methods approach was utilized to develop,
implement, and evaluate the course. Our methodology focused on Course Development,
starting with a comprehensive review of the existing courses, industry requirements, and thelatest academic research. This initial analysis helped identify the key gaps and needs in theapplication of ML in UX design to ensure that the course incorporated the latest technological63

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advancements to address real-world challenges UX designers face. Based on the findings fromthe initial needs analysis, the course was structured as a seven-week program, segmented into
four distinct modules: Problem Definition and Planning, Model Construction, Model
Evaluation, and Implementation. Each module combined lectures with practical sessions,emphasizing real-world projects where students applied ML to tackle user-centric problems.
Evaluation methods included student surveys and interviews to assess their understanding andthe practical application of their skills. Feedback from industry stakeholders and long-term
tracking of alumni will further inform the course’s effectiveness and effects on professional
practices.
Through a combination of theoretical learning and practical application, the course aims toenable students to effectively apply ML technologies in design practices while criticallyassessing and integrating these technologies to ensure that design solutions genuinely meetuser expectations and needs.
Human-Considered Machine Learning Design Approaches
A lot of research has delved into how to use tools such as Midjourney in the discovery ordevelopment stage, which is the divergent stage of the design process that inspires the designconcept (Chiou et al., 2023; Turchi et al., 2023; Zhang et al., 2023). However, due to the
purpose-oriented nature of product design and the strong connection to human behavior,relying on interesting or unexpected accidental elements of the results generated by machinelearning tools is insufficient. Particularly in the field of interaction design, which has intricate
connections to human physiology and behavior, solutions are required that go beyond what
image-generating tools can typically offer (Tholander & Jonsson, 2023). To use the machinelearning tool more actively in the product design process, it is necessary to consider the
curriculum for creation that can understand human needs and behaviors and solve problems.
Machine learning research often ignores human factors, such as usability, intuition, effort, andhuman learning, and focuses only on the efficiency of algorithms. However, humanintervention is important for real-life applications. It is important to keep in mind that humanvalues, goals, and social structures always play an important role in collecting training data,
coordinating algorithms, and integrating machine learning into real-world systems. Humancentered machine learning is not a single approach but encompasses a wide diversity ofproblems, methods, technologies, and theories (Gillies et al., 2016).
However, many UX designers currently lack a deep understanding of artificial intelligence,
which hampers their ability to effectively contribute to interdisciplinary teams during ML
product development. UX designers may not fully grasp the dependent relationship of ML withdata and ground truth. Some designers and commentators even treat ML as something magical(Dove et al., 2017). Researchers have found that practitioners without a background in AI facechallenges in engaging with data and AI. Product professionals often struggle to understandwhat AI can do and find it difficult to translate business problems into data science problems(Piorkowski et al., 2021). This lack of designer involvement often leads ML products toemphasize technology at the expense of understanding user needs.

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For ML product development, Stanford d. School’s “Designing Machine Learning” course uses
dimensions of idealism vs. realism and creativity vs. descriptiveness to categorize UX design
into four stages: the Idealistic and Creative Crafting stage, the Idealistic and Descriptive
Research stage, the Realistic and Creative Mapping stage, and the Realistic and Descriptive
Monitoring stage. This framework highlights the unique challenges in machine learning-based
UX product design compared to traditional UX design. In the Research stage, designers face
the challenge of bridging the gap between theoretical research and practical application toeffectively translate theoretical research into practical technologies or products. The Craftingstage deals with challenges in realizing innovative ideas as concrete products, particularly
when technical or engineering support is insufficient. The Mapping stage involves addressing
the opaque nature of machine learning systems and enhancing system transparency andinterpretability to ensure that users can understand and trust these technological solutions. In
the Monitoring stage, the challenge lies in overseeing the actual deployment of products andresponding to potential system failures or performance declines to ensure continuity of the UX
and system reliability.
Moreover, the design of intelligent systems also involves managing the dynamic relationshipbetween user control and system automation. The challenge of integrating “intelligent”
technology into people’s lives or determining when automation is needed and when people
desire a sense of control can be distilled into the debate between “do it for me” and “do it
myself” automation (Koulu, 2020; Shneiderman & Maes, 1997). In machine learning (ML)systems, the relationship between user control and automation is often dynamic, influenced byuser capabilities, their needs, and the capabilities of the ML system itself. Finding the optimal
dynamic balance in a UX setting presents a significant challenge for designers (Berberian etal., 2012; Parasuraman et al., 2000). Designers must navigate these complexities to create
experiences that effectively balance automation and user control, ensuring that systems
enhance users’ lives without overwhelming or alienating them.
Another major challenge faced by UX designers in the field of Machine Learning (ML) is its
ethical implications (Dove et al., 2017). As machine learning technology increasingly affects
many aspects of everyday life, UX designers must navigate the complex ethical landscape toensure that these systems are designed responsibly. Ethical considerations in UX design affectnot only user trust but also the broader social effects of the technology. This requires designers
to address issues like data privacy, bias in algorithmic decision-making, and the transparencyof ML systems during the UX design process.
Several initiatives and tools have been developed to address the challenges faced in designingmachine learning systems centered on human needs and ethical values. People + AI Research(PAIR), an initiative in Google, is an approach to designing machine learning focused onhumans (Google, 2019). According to Google, the aim of PAIR is “to explore the human sideof AI by doing fundamental research, building tools, creating design frameworks, and working
with various communities.” This guidebook provides a series of articles outlining
considerations for product development in AI: User Needs Defining Success, Data Collection
Evaluation, Mental Models, Explainability Trust, Feedback Control, and Errors Graceful65

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Failure. Yildirim and colleagues’ (2023) research revealed that practitioners use the guidebook
not only to address AI’s design challenges but also to improve education, cross-functional
communication, and internal resources.
IDEO’s AI Ethics Cards help guide an ethically responsible, culturally considerate, andhumanistic approach to designing with data (Sampson & Chapman, 2019). The card set
consists of four major design principles and ten activities intended to be used in teams working
on the development of data-driven products and services. They help designers maintain ahuman-centered focus during the service or product development process.
Mathewson (2019) conducted an anthropocentric approach to interactive machine learning
design as follows: (a) Define the hypothesis and state the investigated question of interest, (b)Loop in humans and define your values and principles, (c) Define the goal, (d) Define the data,(e) Build a model, (f) Evaluate the model, (g) Analyze trade-offs, and (h) Re-evaluate the modeland iterate. Von Wangenheim and von Wangenheim (2021) explained the approaches in termsof the (a) needs identification and characterization of the context, (b) idea creation andspecification of the intelligent system, (c) requirements analysis of the ML model, (d) datapreparation, (e) model training and evaluation, (f) prediction, (g) model export, (h) model
deployment, and (i) SW system test.
These frameworks and tools, as outlined in Table 1, offer a structured approach for integrating
ethical considerations, user needs, and human factors into AI and ML system design, guiding
curriculum development.

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Table 1
Design Process for Human-Centered Machine Learning from Literature
Reference

Stanfordd.School(2018)
People + AI
Research(2019)

IDEO’s AI
Ethics Cards(2019)

Product Design Process for Human-Centered Machine Learning
Problem definition Model
Model evaluation Implement
and planning
construction
Researching
Crafting
Monitoring
Mapping
User Needs
Defining Success
Mental Models

Principles: Don’t
presume thedesirability of AI
Respect privacy
and the collective
goodMathewson, (a) Define theK. W. (2019) hypothesis Statethe investigatedquestion of interest(b) Loop in humans
Define your values
and principles(c) Define the goal
von(a)Needs
Wangenheim identification and& voncharacterization of
Wangenheim the context(2021)(b) Idea creationand specification of
the intelligent
system(c) Requirements
analysis of the ML
model

Data Collection
Evaluation

Principles: Data
is not truth(d) Define thedata(e) Build model(d) Data
preparation(e) Model
training

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Explainability
Trust
Feedback Control
Errors Graceful
FailurePrinciples:
Unintended
consequences ofAI areopportunities fordesign(f) Evaluate
model(g) Analyze tradeoffs(h) Re-evaluate
and iterate(e) Model
evaluation(f) Prediction,(g) Model
export(h) Model
deployment(i) SWsystem test

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Core of Machine Learning-Based Product Design
Course Structure
Drawing from the studies outlined in Table 1, the product design process for human-centeredmachine learning can be segmented into four key stages: problem definition and planning,model construction, evaluation and implement. In the problem definition and planning stage,
the following steps are included: (a) defining user needs, (b) generating ideas, and (c) creating
an interaction flow to concretize ideas. The model construction stage encompasses (a) defining
the necessary data, (b) collecting and preprocessing data, and (c) model training. The model
evaluation stage involves assessing the model’s performance through a confusion matrix and
exploring methods to address errors. The following two steps, emphasizing multimodal
interaction and implementing interaction using Arduino, have been added to improve theability to design and implement a product from a human interaction perspective. Multimodal
interaction is a key element of user-centered machine learning design that addresses diverse
user groups, natural interaction, information richness, and flexibility in responses. The course
follows the flow as depicted in Figure 1.
Figure 1
Product Design Course Structure Based on Design Process Sample

Phase 1: Definition of User Needs
The first phase of the course focused on human-centered machine learning solutions, and theemphasis was placed on equipping students with the necessary skills and insights to approachmachine learning (ML) from a user-centric perspective. To effectively utilize machine learning
to address real-world problems and create value, it is crucial to start from the user’s perspective.
Instead of asking whether machine learning can be used, designers should frame questions interms of human-centered machine-learning solutions. When designing user-centered machinelearning solutions, it is essential to first explore machine learning concepts and elements froma UX perspective, consider machine learning design within the context of UX, understand theprocess for structuring machine learning projects with UX in mind, and explore when artificial
intelligence (machine learning) can yield effective results and when it might not be the best fit.
The methodology for defining user needs involves three stages:

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1. Listing Existing Evidence: Collect existing data, research findings, and information
related to the issues and needs that users have encountered. This establishes afoundation for comprehending user requirements.
2. Detailed Description of User Needs: Utilize the gathered evidence to create acomprehensive and detailed description of user needs. This involves understanding what
users desire, where they face difficulties and the specific problems they aim to solve.
3. Assessing the Potential Suitability of AI Solutions: Based on the existing evidence andthe detailed description of user needs, evaluate whether machine learning-based
solutions have the potential to effectively meet these needs.
In this phase, when students are tasked with defining user needs in the context of machinelearning projects, the focus shifts towards practical application and engagement with real-world
problems through a user-centric lens. Understanding a user-centric standpoint and user needs
is fundamental to creating successful machine-learning solutions that genuinely address user
problems and enhance their experiences.
Phase 2: Multimodal Interaction Idea Creation
The second phase involved the practical application of combining machine learning withmultimodal interaction to enhance UX and facilitate seamless interaction between physical anddigital environments. Karray and colleagues (2008) argued that the interaction between humanmachine systems fundamentally occurs through the exchange of information via various input
and output methods between computers and humans. Hinckley and colleagues (2014) further
elucidated the nature of these exchanges, highlighting that inputs to a system constitute theinformation relayed by the user to the machine. Conversely, system outputs are essentially the
feedback provided to the user, which aids in navigating and accomplishing tasks. This dynamic
exchange effectively bridges the gap between the internal operations of the system and thetangible, physical world, thereby facilitating a seamless interaction that spans the digital andphysical realms. The multimodal human-machine interaction model is depicted in Figure 2.
Figure 2
Multimodal Human-Machine Interaction Model69

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Humans naturally engage in multimodal interactions with the world, utilizing various sensory
channels to perceive and respond to other people and the external environment. Several studies(Bolarinwa et al., 2019; Mathewson, 2019; Xiao et al., 2003) have demonstrated that
implementing multimodal interaction systems enhances flexibility and facilitates the exchange
of information, allowing for natural and realistic interactions between intelligent systems andusers. The combination of machine learning and multimodal interaction serves to improve the
UX and enables more effective handling of interactions between physical and digital
environments.
Therefore, in this phase, students were asked to critically examine the dynamics between data
input and output in the context of machine learning tools guided by a human-centeredperspective. This exploration was pivotal, as it required students to consider how users interact
with machine learning systems and how these systems, in turn, respond to and guide user actions.
The emphasis on a human-centered approach encouraged students to prioritize user needs,
preferences, and behaviors in the design and development of machine learning applications.
Phase 3: Developing an Interaction Flow
In the third phase, students were asked to develop interaction flows for machine learning-based
interactive products or systems. This phase emphasized the significance of understanding anddesigning products to enhance the dynamic relationship between humans and machine learning
systems. Ghim (2021) highlighted that interactive products, unlike static products, possess two
unique characteristics: human-machine interaction and temporal sequencing. For instance,
users provide input to the product through physical actions and interpret the product’s operation
through mental processes.
Machine learning-based interactive products detect input through sensors, which can come fromusers or environmental changes, and respond through actuators or output components. The
exchange of these actions and communications occurs sequentially over time to achieve theproduct’s intended goals. Based on these two aspects, a framework can be established to help
understand and design machine learning-based interactive products/systems, as shown in Figure 3.
Figure 3
Human-Machine Interaction Flow70

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In machine learning interactions, the role of humans in the relationship between humans andmachines varies depending on user characteristics, user needs, and task requirements. It isessential to consider the points at which the roles of humans and machine learning interchange
based on the user’s usage context. As shown in Figure 4, when users have a high level of task
performance capability, a strong interest in the task, and engage in creative work or highly
sensitive domains, machine learning interactions should be designed to enhance users’
capabilities and creativity. In such cases, the machine learning system should play a supportive
rather than a controlling role. On the contrary, when users have low task performancecapability, minimal interest in the task, and engage in repetitive tasks or work in low-sensitive
domains, machine learning interactions should be designed to simplify and automate tasks. The
system should require minimal user input and decision-making, focusing on efficiency andreducing the cognitive load for the user. The machine learning system should take on a moreproactive role, providing assistance, guidance, and automation to make the user’s tasks easierand more manageable.
Figure 4
Changing Relationship Between Human and Machine Learning Based on User
Characteristics and Task Features

Considering the fluid dynamics between humans and machine learning, as well as the intricate
aspects of human-machine interaction and temporal sequencing, students were assigned thechallenge of crafting interaction flows. These flows had to be tailored to meet the uniquerequirements and characteristics of both the users and their projects. This task emphasized theimportance of understanding not just the technological capabilities of machine learning
systems but also the human context in which these interactions occur.
Phase 4: Defining the Necessary Data and Data Preprocessing
Phase 4 emphasized the practical aspects of data handling for machine learning projects,
specifically the collection, labeling, and preprocessing of data to meet user requirements. The
process started with identifying the necessary dataset and then pinpointing the essential featuresand labels within this dataset that align with user needs. To enable machine-learning products
to make accurate predictions, their underlying machine-learning models had to be trained based71

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on patterns and correlations within the data. This data, known as training data, could encompass
a diverse range, including images, videos, text, and audio. Students could utilize existing data
sources or collect new data specifically for training their system.
The quality of the training data, including how they were sourced, collected, and labeled,played a pivotal role in determining the output of the system. The quality, relevance, andaccuracy of this data, rather than just quantity, were paramount. Properly labeled and wellstructured data ensure that the machine learning model can learn effectively, leading to reliable
and functional outputs. In addition to data collection, data preprocessing was an integral aspectof this phase. It involved cleaning the data, handling missing values, and standardizing or
normalizing it for consistency. Preprocessing may also include transforming variables to makethem more suitable for analysis. The goal of this step was to refine the data into a format thatis conducive to training a high-performing machine learning model.
Through these tasks, students learned the critical role of high-quality, well-labeled, andproperly processed data in building effective machine-learning systems. This phase not only
enhanced their technical skills in handling data but also deepened their understanding of thefoundational principles that govern the success of machine learning products, emphasizing theimportance of meticulous preparation and analysis of data to meet user requirements.
Phase 5: Model Training
In Phase 5, students used a coding platform called Naver Entry to process the collected data
and build machine-learning models based on that data. Naver Entry is an educational platform
designed to foster computational thinking and provide hands-on experience with artificial
intelligence features through a block-based interface. One of the key advantages of Naver Entry
is its user-friendly interface, which is particularly accessible for design students with minimal
programming background. Because of its ease of use, students could focus more on theapplication and implications of machine learning in their field rather than on the complexities
of coding. Another key reason for choosing Naver Entry for model training in this phase wasits adeptness in handling multimodal data, including images, audio, and text. This flexibility is
crucial for a comprehensive learning experience and aligns well with the diverse nature of datatypically encountered in real-world scenarios. It met the course requirements and provided
students with practical experience in model training. Naver Entry allows users to train themodels based on the following:a. Image Classification: Train a model that can classify images uploaded or captured via
a webcam.
b. Text Classification: Train a model capable of classifying text that you either write
directly or upload as a file.
c. Voice Classification: Train a model that can classify voices recorded from a
microphone or uploaded as audio files.
d. Numeric Classification: Train a model that classifies numeric data in tables into various
classes based on the nearest neighbors (K-nearest neighbors) for each data point.

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Phase 6: Assessing the Model’s Performance
In the “Evaluation” phase, students delved into the critical process of assessing the performanceof their machine learning models through the lens of a confusion matrix, a pivotal tool inunderstanding the nuances of model accuracy. The confusion matrix (Figure 5), also referred
to as an error matrix, is a structured representation that showcases how well a classification
model predicts outcomes across a set of test data, distinguishing between true positives (TP),true negatives (TN), false positives (FP), and false negatives (FN).
Figure 5
Confusion Matrix

Note. True positive (TP): Observation predicted as positive is positive. False positive (FP): Observation predicted
as positive is negative. True negative (TN): Observation predicted as negative is negative. False negative (FN):
Observation predicted as negative is positive.

The effectiveness of a model is gauged by the magnitudes of TP and TN indicating correct
predictions and the minimization of FP and FN signifying errors. The decision to either focus
on reducing FP and FN or on maximizing TP and TN plays a significant role in shaping theuser experience (UX), as noted by Davis and Goadrich (2006). This decision is not arbitrary
but hinges on the specific goals of the machine learning application, the nature of the data, andthe intended outcomes of the model.
When designing, the evaluation metrics vary based on the machine learning’s purpose, datacharacteristics, and model objectives. For example, even for the same recommendation system,
the important evaluation metrics can differ depending on the application. In product(advertisement) recommendations, high precision may be crucial because it increases the
proportion of recommended products that are of interest to the user. Precision is importantwhen leading to purchase actions. On the other hand, in music recommendations, high recall
can help users explore and discover various types of music. Accordingly, music platforms
should focus on increasing recall to assist users in finding diverse music and prevent churn.
In machine learning predictions, providing appropriate feedback to users when errors (FP/FN)
occur is essential for maintaining user trust in the AI product. Feedback methods include
notifying users of errors, explaining why those errors occurred, apologizing to users, soliciting73

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feedback from users to improve and adjust system performance based on user feedback, andproviding options for AI intervention for more accurate results when necessary.
In this study, students were first asked to assess what should be emphasized in this task. For
instance, in safety-related issues, one should focus on reducing the likelihood of FN (false
negatives). In other words, extra attention should be paid to cases where a problem genuinely
occurred but was not predicted by the machine learning model. With a match rate of 20-50%(an area with a high likelihood of false negatives occurring), they needed to consider how toprovide feedback to users. They were required to outline how to request feedback from users
and how to provide feedback to users in each situation, as shown in Figure 6.
Figure 6
Assessing the Model’s Performance and Exploring Methods to Address Errors

In each situation, students had to outline specific approaches for engaging with users, ensuringthat the feedback loop is not only informative but also fosters trust and reliability in the machinelearning application. By focusing on reducing false negatives or false positives and enhancing
the dialogue between users and the system, students learned to create more resilient,
responsive, and user-centric machine-learning solutions.
Phase 7: Implement Interaction Using Arduino
In Phase 7, the application of machine learning extended beyond theoretical concepts, merging
with the physical world and tangible products. The key objective in this phase was to bringmachine learning into real-life applications by employing Arduino. This phase reemphasized74

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the significance of multimodal interaction, focusing on the dynamic interplay between user
input and system output, and integrated this with practical work using Arduino.
Students were tasked with exploring and prototyping various methods of user sensing andfeedback. They used their machine-learning models in conjunction with Arduino to develop
intelligent system prototypes. This process involved designing and building Arduino circuits,
gaining a deep understanding of how inputs and outputs operate within the Arduino ecosystem,
and coding these interactions to function as intended. This phase allowed students to test andvalidate their designs against user needs and requirements. It provided an opportunity to assess
whether the prototypes meet the intended purposes and to understand the practical role ofmachine learning in actual product development.
By culminating the learning process with the development of a functioning prototype, students
gained valuable insights into the practical applications of machine learning, understanding its
role and potential in enhancing user interactions and experiences in the realm of physicalproducts, thereby bridging the gap between theoretical knowledge and practical application.
Course Implementation and Evaluation
In the Fall Semester of 2023, a course was conducted with seven undergraduate students under
the theme of ‘home camera.’ The group included four design majors, one education major, onelife sciences major, and one marine engineering major. Except for the marine engineering
student, who had some basic programming experience, the other students had no priorexperience in coding. The students carried out various projects, including smart cameras for
efficient study, Smile Boxes that help users maintain a positive mindset, pet-related products,
and security-related products. The researchers aimed to assess the effectiveness of the course
proposal by examining the student outcomes, focusing particularly on students who
demonstrated the most fidelity to the machine learning-based product design process.
Student Project: Smart Home Camera for Pet Companionship
Many people consider their pets as part of their family. These individuals require more thansimple observation; they need delicate care services. They want to provide appropriate support
to their pets when they are alone, including observation, health monitoring, communication,
provision of treats, and guiding their pets to specific areas.
One student defined the following design goals based on her experience of raising a pet andmarket research:a. Real-Time Monitoring: Design that allows users to monitor their pets’ status andactivities in real-time and respond effectively.
b. Behavior Analysis: Functional design that uses machine learning to analyze petbehavior and notify the user.

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c. Enhanced Interaction: Design that enables bidirectional interaction between users andtheir pets through a smartphone app.
d. User-Friendly: Design that considers elements to minimize pet stress.
She ensured that machine learning solutions can be helpful in providing personalized services
to users and aligning with the potential suitability of user needs for machine learning solutions.
Based on the use needs, she created multimodal interaction ideas for providing an enhanced‘pet care service’ experience Using Machine Learning:a. Utilize machine learning to provide personalized services to users (e.g., tracking pet
activity).
b. Process audio/video data based on learned patterns to send notifications only under
specific conditions.
c. Detect and respond to user-defined events using machine learning (e.g., entering
specific areas, behaviors like scratching food bowls).
d. Improve remote interaction between users and their pets (e.g., operating a speaker after
tracking the pet).
Accordingly, the interaction flow, shown in Figure 7, was created. The interaction flow design
should consider both the user’s and the pet’s actions to create a comprehensive UX. Since pets
can also be users, the actions of pets are included in the flow.
Figure 7
Interaction Flow of Smart Home Camera for Pet Companionship76

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Then, she defined the data needed to train the machine-learning model: User and pet basic
information data and user profiles, pet species, age, health status, behavior patterns, preferencedata, and audio/video data. Pet behavior data: Data related to a pet’s entry into specific areasor specific behaviors. User feedback and interaction data: Data on interactions between users
and pets, user feedback, and service usage records.
This student’s work considered various user needs, including real-time monitoring, behavior
analysis, enhanced interaction, and user-friendliness. The effectiveness of the project wasevaluated based on the integration of machine learning features that aligned with the user needs
and the comprehensiveness of the interaction design. The project was noted for its systematic
approach and deep consideration of both user and pet perspectives within the interaction flow.
The effectiveness of the student projects was assessed by examining how well the integrationof machine learning features met actual user needs and the depth of the interaction design. Thisparticular project was recognized for its systematic approach and thorough integration of bothhuman and animal perspectives within the interaction flow.
Course Evaluation
During the 7-week class held in the Fall Semester of 2023 with seven students, meaningfuladvancements in understanding user needs for machine learning-based product design wereobserved. The students learned to reflect these needs consistently throughout the design
process. Additionally, they gained insights into the design methods that utilize machinelearning technologies appropriate for their selected project topics with the aid of “Entry,” ablock coding tool, which simplified the implementation of machine learning programs.
According to student evaluations, the class was beneficial as it provided them with
opportunities to handle various AI-powered programs and apply machine learning to
unfamiliar areas. Design students appreciated the way the course bridged the gap between
creative design and technical implementation, allowing them to explore new dimensions in
product design. Students from other majors valued the human-centered approach and AI
understanding, noting that the problem-solving mindset gained from the course would benefit
their future work by enabling innovative AI integration in their fields. However, they notedsome challenges with remote delivery, including difficulties in performing practical exercises
due to the limitations of virtual instruction. From the instructor’s perspective, the class faced
certain limitations. Primarily, the focus was on supervised learning techniques due to time
limitations and the restrictions of the coding tools used, which narrowed the scope of AI
applications. Additionally, the collected data often did not adequately reflect the users’ actual
situations and needs, leading to a deficiency in students’ understanding of the data. Moreover,time constraints prevented the inclusion of product appearance and interface design in the
curriculum.
Future iterations of the course should incorporate a broader array of machine learningtechnologies, including unsupervised learning techniques, such as clustering models andreinforcement learning. This expansion would enable students to apply their design skills in77

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more diverse real-world contexts. Additionally, the curriculum should be enhanced with more
extensive data utilization, ensuring students can extract and model meaningful information
effectively. These improvements will not only deepen students’ understanding of machinelearning applications in design but also increase their ability to use machine learning to address
real user needs.
Conclusion
The rapid advancement of machine learning (ML) technologies brings with it a myriad ofopportunities and challenges, particularly in the realm of UX design. While ML has thepotential to significantly enhance interactions between users and technology, its practicalimplementation often falls short due to technological limitations, a general failure to meet realuser needs effectively, and ethical concerns. This paper underscores the existing gaps in design
education and practice, advocating for a shift towards more human-centered methodologies indeveloping ML-based products.
The newly proposed Machine Learning-Based Human-Centered Product Design Planning andPrototyping Course, structured as a seven-week program with four distinct modules—problem
definition and planning, model construction, model evaluation, and implementation—aims toredress these educational shortcomings. This course weaves together a comprehensive review
of existing courses, current industry demands, and recent academic research to meticulously
equip future designers with the necessary skills to effectively utilize ML technologies while
prioritizing user needs.
Feedback from the initial course, which included professional evaluations of student projects
and student surveys and interviews, indicated that the curriculum could successfully bridge thegap between theoretical knowledge and practical application. Participants showed marked
improvements in their ability to integrate ML technologies into their design practices,demonstrating a heightened sensitivity to user-centered design principles.
The continuous evolution of machine learning (ML) technologies will undoubtedly present
new challenges and opportunities. Therefore, ongoing research, curriculum updates, and robust
feedback mechanisms are crucial to ensure that educational offerings remain relevant andeffective. It is necessary to continue to refine the modules proposed in the machine learning
design education method. Furthermore, the findings from this course can serve as afoundational model for future developments in design education, particularly in integrating
emerging technologies and user-focused design. Subsequent research should try to refine thisapproach into a comprehensive design education methodology—one that can foresee andunderstand the ensuing changes in consciousness, societal norms, cultural shifts, economictransitions, and the broader life evolutions they instigate.
The essence of design education is not merely about efficiency; rather, it is about nurturing
designers’ ability to think critically and use tools to cultivate innovation. Historically,
photography was primarily approached from a technical perspective, aiming to capture subjects

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effectively. However, it soon evolved into a revered art form. The transformative power of newtechnology on human life and culture is evident, particularly when considering how modern
photography practices significantly influence our society and daily lives. As designers
increasingly incorporate ML tools into their toolkit, it becomes crucial to reflect on their
evolving role. These tools should be harnessed not just for efficiency but also for their ability
to foster human-centric innovation, potentially leading to shifts in human consciousness.

Acknowledgements
This work was supported by the Ministry of Education of the Republic of Korea and the
National Research Foundation of Korea (NRF-2021S1A5A2A01070316).

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References
Ahn, J., Kim, J., & Sung, Y. (2022). The effect of gender stereotypes on artificial intelligence
recommendations. Journal of Business Research, 141, 50–59.
https://doi.org/10.1016/j.jbusres.2021.12.007
Areeb, Q. M., Nadeem, M., Sohail, S. S., Imam, R., Doctor, F., Himeur, Y., ... & Amira, A.
(2023). Filter bubbles in recommender systems: Fact or fallacy—A systematic
review. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery,
13(f), e1512. https://doi.org/10.1002/widm.1512
Berberian, B., Sarrazin, J. C., Le Blaye, P., & Haggard, P. (2012). Automation technology
and sense of control: a window on human agency. PloS one, 7(c), e34075.
https://doi.org/10.1371/journal.pone.0034075
Bolarinwa, J., Eimontaite, I., Dogramadzi, S., Mitchell, T., & Caleb-Solly, P. (2019, June).
The use of different feedback modalities and verbal collaboration in tele-robotic
assistance. In 2019 IEEE International Symposium on Robotic and Sensors
Environments (ROSE) (pp. 1–8). IEEE. https://doi.org/10.1109/ROSE.2019.8790412
Chiou, L. Y., Hung, P. K., Liang, R. H., & Wang, C. T. (2023, July). Designing with AI: An
Exploration of Co-Ideation with Image Generators. In Proceedings of the 2023 ACM
Designing Interactive Systems Conference (pp. 1941–1954).
https://doi.org/10.1145/3563657.3596001
Chmielewski, D., Hu, K. (2023). Disney creates task force to explore AI and cut costs.
Reuters. Retrieved from https://www.reuters.com/technology/disney-creates-taskforce-explore-ai-cut-costs-sources-2023-08-08/
Davis, J., & Goadrich, M. (2006, June). The relationship between Precision-Recall and ROC
curves. In Proceedings of the 23rd international conference on Machine learning (pp.
233–240). https://doi.org/10.1145/1143844.1143874
Dove, G., Halskov, K., Forlizzi, J., & Zimmerman, J. (2017, May). UX design innovation:
Challenges for working with machine learning as a design material. In Proceedings ofthe 2017 chi conference on human factors in computing systems (pp. 278–288).
https://doi.org/10.1145/3025453.3025739
Ghim, Y. G. (2021). Allocated Flow Diagramming: A Structured Process and Methods for
Teaching Interactive Product Prototyping in Industrial Design. Archives of Design
Research, 34(d), 7–21. https://doi.org/10.15187/adr.2021.11.34.4.7
Gillies, M., Fiebrink, R., Tanaka, A., Garcia, J., Bevilacqua, F., Heloir, A., ... & Caramiaux,
B. (2016, May). Human-centred machine learning. In Proceedings of the 2016 CHI
conference extended abstracts on human factors in computing systems (pp. 3558–
3565). https://doi.org/10.1145/2851581.2856492
Google (2019). People + AI Guidebook. Retrieved fromhttps://pair.withgoogle.com/guidebook
Hinckley, K., Jacob, R. J., Ware, C., Wobbrock, J. O., & Wigdor, D. (2014). Input/Output
Devices and Interaction Techniques. Chapman and Hall.
Holstein, K., Wortman Vaughan, J., Daumé III, H., Dudik, M., & Wallach, H. (2019, May).
Improving fairness in machine learning systems: What do industry practitioners
need?. In Proceedings of the 2019 CHI conference on human factors in computing
systems (pp. 1–16). https://doi.org/10.1145/3290605.3300830

80

IAFOR Journal of Education: Technology in Education

Volume 12 – Issue 2 – 2024

Karray, F., Alemzadeh, M., Abou Saleh, J., & Arab, M. N. (2008). Human-computerinteraction: Overview on state of the art. International journal on smart sensing andintelligent systems, 1(a), 137–159. https://doi.org/10.21307/ijssis-2017-283
Koulu, R. (2020). Human control over automation: EU policy and AI ethics. Eur. J. LegalStud., 12, 9.
Mathewson, K. W. (2019). A human-centered approach to interactive machine learning.
arXiv preprint arXiv:1905.06289. https://doi.org/10.48550/arXiv.1905.06289
Marq’s Blog (2023). Artificial Intelligence Design Tool Statistics & Trends in 2023.
Retrieved from https://www.marq.com/blog/artificial-intelligence-design-toolstatistics-trends-in-2023
Munir, H., Vogel, B., & Jacobsson, A. (2022). Artificial intelligence and machine learning
approaches in digital education: A systematic revision. Information, 13(d), 203.
https://doi.org/10.3390/info13040203
Parasuraman, R., Sheridan, T. B., & Wickens, C. D. (2000). A model for types and levels ofhuman interaction with automation. IEEE Transactions on systems, man, andcybernetics-Part A: Systems and Humans, 30(c), 286–297.
https://doi.org/10.1109/3468.844354
Piorkowski, D., Park, S., Wang, A. Y., Wang, D., Muller, M., & Portnoy, F. (2021). How ai
developers overcome communication challenges in a multidisciplinary team: A casestudy. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW1), 1–25.
https://doi.org/10.1145/3449205
Riedl, M. O. (2019). Human‐centered artificial intelligence and machine learning. Humanbehavior and emerging technologies, 1(a), 33–36. https://doi.org/10.1002/hbe2.117
Roettgers, J. (2023). Ikea’s generative AI furniture designs are trippy, retro, and in-spiring,
FAST COMPANY. Retrieved from https://www.fastcompany.com/90871133/ikeagenerative-ai-furniture-design
Sampson, O., Chapman, M. (2019). AI NEEDS AN ETHICAL COMPASS. THIS TOOL CAN
HELP. IDEO. Retrieved from https://www.ideo.com/journal/ai-needs-an-ethicalcompass-this-tool-can-help
Shneiderman, B., & Maes, P. (1997). Direct manipulation vs. interface agents. interactions,
4(f), 42–61. https://doi.org/10.1145/267505.267514
Sulmont, E., Patitsas, E., & Cooperstock, J. R. (2019). What is hard about teaching machinelearning to non-majors? Insights from classifying instructors’ learning goals. ACM
Transactions on Computing Education (TOCE), 19(d), 1–16.
https://doi.org/10.1145/3336124
Tholander, J., & Jonsson, M. (2023, July). Design ideation with ai-sketching, thinking andtalking with Generative Machine Learning Models. In Proceedings of the 2023 ACM
Designing Interactive Systems Conference (pp. 1930–1940).
https://doi.org/10.1145/3563657.3596014
Turchi, T., Carta, S., Ambrosini, L., & Malizia, A. (2023, May). Human-AI co-creation:
evaluating the impact of large-scale text-to-image generative models on the creative
process. In International Symposium on End User Development (pp. 35–51). Cham:
Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-34433-6_3

81

IAFOR Journal of Education: Technology in Education

Volume 12 – Issue 2 – 2024

von Wangenheim, C. G., & von Wangenheim, A. (2021). Overview on a human-centricinteractive ML process for teaching ML in K-12. Working Paper
WP_GQS_01_2021_v10, GQS/INCoD/UFSC, Florianópolis, Brasil.
Xiao, B., Lunsford, R., Coulston, R., Wesson, M., & Oviatt, S. (2003, November). Modelingmultimodal integration patterns and performance in seniors: Toward adaptive
processing of individual differences. In Proceedings of the 5th international
conference on Multimodal interfaces (pp. 265–272).
https://doi.org/10.1145/958432.958480
Yang, S. J., Ogata, H., Matsui, T., & Chen, N. S. (2021). Human-centered artificial
intelligence in education: Seeing the invisible through the visible. Computers andEducation: Artificial Intelligence, 2, 100008.
https://doi.org/10.1016/j.caeai.2021.100008
Yildirim, N., Pushkarna, M., Goyal, N., Wattenberg, M., & Viégas, F. (2023, April).
Investigating how practitioners use human-ai guidelines: A case study on the people+
ai guidebook. In Proceedings of the 2023 CHI Conference on Human Factors in
Computing Systems (pp. 1–13). https://doi.org/10.1145/3544548.3580900
Zhang, C., Wang, W., Pangaro, P., Martelaro, N., & Byrne, D. (2023, June). Generative
Image AI Using Design Sketches as input: Opportunities and Challenges. In
Proceedings of the 15th Conference on Creativity and Cognition (pp. 254–261).
https://doi.org/10.1145/3591196.3596820
Corresponding author: Eui-Chui Jung
Email: jech@snu.ac.kr

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Internationalized Students: Examining Students’ Experiences in Virtual
Study Abroad Programs in Japan
Ariunaa Enkhtur
Osaka University, Japan
Ming Li
Osaka University, Japan83

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Abstract
This paper explored students’ experiences in an international virtual student mobility (VSM)
program at a Japanese national university implemented in collaboration with partner
universities in the Asia Pacific region and Europe. Through students’ narratives, the study
discerned how students perceive “mobility” and the concept of “international students” afterparticipating in a virtual student mobility program. Findings revealed a tension between
students’ understanding of “mobility,” official narratives, and students’ actual experiences.
While VSM programs facilitate communication and knowledge exchange across borders anddevelop cross-cultural communications, prevailing binary definitions in student mobility, suchas “inbound” vs. “outbound,” “domestic” vs. “international,” or “study abroad” vs.
“internationalization at home,” limit VSM potential to be fully realized. This misalignment of
the traditional understanding of student mobility that emphasizes place and borders challenges
the official narrative of student mobility and urges educators to re-align and re-frame how weunderstand “international students.”
Keywords: internationalization of higher education, Japan, students’ perspectives, virtual
student mobility

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In Japan, driven by the goal to attract top international talent, compete with leading economic
powers in research and innovation, and bolster its global status in education and industry, thegovernment has been steering the internationalization of higher education (e.g., Yonezawa,2014). Japan’s unique challenges, such as an aging society; declining birth rate and college
student population; and increasing pressure on higher education for talented graduates torevitalize the economy, have further propelled this push for internationalization (Ota, 2018;
Yonezawa, 2014). Consequently, the government has been funding universities to“internationalize,” establish overseas centers, develop English-taught programs targetinginternational talent, and encourage domestic students to study abroad.
These policies resulted in a significant rise in international students in Japan. In 2019, Japan
hosted 312,214 international students, marking a fourfold increase in ten years since 1999
(JASSO, 2019), accounting for 5% of mobile students (UNESCO Institute for Statistics (UIS)
database, 2023). However, as of 2023, 93% of these international students are from Asia, withalmost half of them (45%) from China (JASSO, 2023). Official documents highlight theimportance of attracting “diverse” international students, but the number-driven approach has
led universities to focus on achieving the target number of students who can be classified as“international students” (Ota, 2018; Yonezawa, 2021). The legal definition of “international
students” excludes those on short-term exchange programs under three months, thereby
directing universities towards longer programs.
On the other hand, most domestic students do not participate in study abroad programs. In
2019, 107,346 students from total 2.9 million university students studied overseas, with 70%
studying in under 1 month and 35% of students choosing one of three English-speaking
countries—the US, Australia, and Canada (JASSO, 2021). Financial constraints, lengthy jobhunting processes, perceived lack of language skills are the main reasons for the lack ofmotivation to study abroad (Yokota et al., 2018). In this context, collaborative online
international learning (COIL) and similar online programs have been introduced in Japanesehigher education institutions, as part of educational programs to enhance students’ language
skills or intercultural communication skills, and often as part of pedagogical tools or“internationalization at home” initiatives (Hofmeyr, 2021).
The pandemic prompted Japanese universities to experiment with virtual mobility programs,
as an emergency response to overcome border closures (Enkhtur et al., 2023; Shimmi et al.,2021). Following the pandemic, the government narrative also acknowledges ICT-based
internationalization as reflected in Ministry of Education’s J-MIRAI project discourse oninternational student exchange until 2033 (MEXT, 2021). Introducing the newinternationalization plan, the document states, “as higher education progresses towards a
hybrid model that combines face-to-face instruction with remote and online education, it isnecessary to come up with new innovations in studying abroad, taking into account thesetrends” (MEXT, 2021, p.3).
Another example is the introduction of an online-mediated program platform “Japan Forumfor Internationalization of Universities” (JFIU) under the government-funded Top Global

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University project to promote Japanese universities overseas and to attract international
students (Saito, 2022). Given the limited diversity among international students and thesignificant number of domestic students who remain immobile, virtual exchange models appeal
to universities (MEXT, 2020). Yet, the prevailing narrative on “student mobility” remains
anchored in physical mobility, continuing to use the binary descriptions like “outbound” versus“inbound” or “study abroad” versus “internationalization at home.” In this paper, we explore
students' lived experiences in international virtual exchange programs implemented at a top
national university in Japan to challenge this existing narrative.
Literature Review
In recent years, the concept of student mobility has undergone significant transformation,
particularly with the advent of virtual and hybrid models that emerged during the COVID-19
pandemic. These developments have prompted a re-evaluation of traditional approaches tointernational education, highlighting the need for a deeper understanding of how virtual student
mobility (VSM) can complement or even substitute physical mobility. This literature review
explores the existing research on VSM, focusing on its definitions, implementations, and thebroader implications for global higher education.
Virtual Student Mobility
The UNESCO’s report defined “virtual student mobility” as “a form of mobility that uses
information and communication technologies to facilitate cross-border and/or interinstitutional
academic, cultural, and experiential exchanges and collaboration” (UNESCO IESALC, 2022:
6). Drawing on 14 global cases, the report underscored the potential of VSM programs in postpandemic landscape of global higher education to broaden access to student mobility “throughvirtual modalities.” The report calls existing models of online exchange programs VSM,
including “virtual exchange” and Collaborative Online International Learning (COIL), as longas the program has aims similar to “physical student mobility such as knowledge exchange,
work experience in another country, and/or cultural immersion” (p.14). Conversely, Erasmus+
and other notable international education and exchanges use “virtual exchange” to denote
programs focusing on “people-to-people activities that promote intercultural dialog and soft
skills development,” highlighting the learner interactions (Helm & van der Velden, 2020).
Research on virtual student mobility has grown as institutions and researchers evaluate andreflect on the educational programs introduced during the pandemic, exploring various aspectsof VSM and their implications for education. Some studies emphasize the role of ICT in virtual
mobility programs, describing them as “technology-mediated” (Lee et al., 2022) or“technology-enabled” (Giralt et al., 2022) exchanges. These emphasize the delivery ofinternational exchange programs using technology, focusing on ICT and other online platforms
as educational tools for connecting students and facilitating discussion and collaboration.
Other studies examine the outcome of virtual mobility programs, gauging students’ orfacilitators’ satisfaction. For example, O’Dowd (2021) observed that virtual exchanges helped

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students overcome stereotypes, boost their confidence in communicating in their second
language (L2), and shift their perception of English from a mere academic subject to acommunication tool. Another study by Fritz and Marchewka (2023) found that afterparticipating in a four-week pilot virtual program, Japanese students developed new
perspectives about themselves and their perceptions of their Polish counterparts whenmeasured pre- and post-program.
The Stevens Initiative published reports from 2020 to 2023 based on programs carried out inthe Middle East and North America. These reports cover initiatives in secondary schools aswell as higher education institutions, shedding light on program types, learning outcomes, andoverall impact. They found that students’ “knowledge of other countries or cultures” increased(2020) and that these programs engaged more underrepresented students than traditional inperson exchange programs (2023).
While researchers continue to use different terminologies to describe virtual models, recent
studies recognize these programs as a form of “mobility” or an “alternative form of student
exchange delivering the same benefits as traditional student mobility” (Alami et al., 2022). In
Japan, telecollaboration, tandem, or COIL programs existed for many years before COVID-19(Akiyama & Cunningham, 2018). Yet, it was not until the pandemic, educational institutions
integrated ICT into international education exchange (Ota et al., 2023). As universities adopted
emergency virtual mobility programs, educators discovered the potential to engage a broader
student base (Shimmi et al., 2021). For instance, one of the top national universities, Tohoku
University, launched the "Be Global Project" consisting of online study abroad programs,intercultural collaboration learning programs, and virtual support and services for international
students. The university called the initiative “a comprehensive initiative aimed at building a
new international education model in post-pandemic international education” (Shimmi et al.,2021). Similarly, Enkhtur and colleagues’ (2023) case study, a university attracted diverse
students from different corners of the world to its online anniversary lecture series, where its
prominent faculties delivered lectures.
Given the success and interest in these programs, and the rising adoption of digital technologies
in educational institutions in Japan, researchers advocate for the continued implementation of
VSM programs as a regular activity even after universities shift back to physical mobility(Enkhtur et al., 2023; Shimmi et al., 2021). A survey by the Ministry of Education, Culture,
Sports, Science, and Technology in Japan (MEXT) revealed that top universities in Japan are
inclined to develop blended and hybrid exchange programs post-pandemic (MEXT, 2020).
Among universities participating in the Top Global University project, 86% expressed intent
to develop blended/hybrid international mobility programs.
Additionally, the government is supporting initiatives for increased virtual education programs.
For example, a new project, “Japan Forum for Internationalization of Universities” (JFIU), wasestablished in 2021 by MEXT. Its mission is to strengthen the internationalization activities ofleading Japanese universities through increased use of ICT (JFIU, n.d; Sato, 2022). Within thisoverarching project are several sub-projects by member universities. One of the projects led by87

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Tsukuba University has set up a so called “Japan Virtual (JV) Campus,” an online platform
featuring contents and lectures from participating universities to promote Japan as thedestination country for international education. In this context, while the official discourses on
student mobility are shifting to include online modalities, they are still about physical mobility
in Japan.
Student Mobility in JapanIn 1983, the Japanese government established its first target number for international students
to increase to 100,000, aiming to recruit Japanese-speaking students (MIC, 2005). Then, in
2008, the government introduced its next target to increase international students to 300,000
by 2020. This increase was expected to foster an international environment, stimulate thestagnating economy, and elevate Japan’s global competitiveness (Yonezawa, 2011). To
facilitate this growth, attract “talented” students worldwide, and “internationalize” the top
domestic universities, the government introduced several initiatives, such as the Global 30
project from 2009–2014 and Top Global Project from 2014-2024, aiming to create an academic
environment more appealing for international students.
In parallel, the government, in its goal to cultivate individuals capable of functioning within
the global labor market (referred to as “global jinzai”), has also set targets for “outbound”
mobility (Cabinet Office, 2014). The initial goal was to have 120,000 domestic students
participate in outbound studying abroad programs by 2020. In pursuit of this target, thegovernment introduced the “Tobitate! (Leap for Tomorrow) Study Abroad Initiative” aimed atincentivizing and providing assistance to domestic students to study abroad. The number ofJapanese students studying abroad in 2019 reached 107,346; however, due to the disruptive
impact of the COVID-19 pandemic, this figure plummeted to a mere 1,487 in 2020 (JASSO,
2020 & 2021).
However, these initiatives have emphasized quantitative results over the quality of programs
which is evident in institutional strategies focusing on increasing international students,
expanding study abroad programs, and institutional agreements (Ishikawa, 2011; Yamamoto,2018). Regrettably, little attention has been paid to understanding students’ actual experiences
or assessing the outcomes of these programs.
A review of these policies and projects indicates that the Japanese government’s policy forinternationalization and student mobility emphasizes physical student mobility, counting
students who enter the country. Official documents define “international students” as “foreign
students enrolled in Japanese higher education institutions (universities, graduate schools,
junior colleges, technical colleges, and vocational colleges) under the status of "College
Student" as defined in Appendix 1 of the Immigration Control and Refugee Recognition Act”
(MEXT, 2008). The “college student” visa, however, applies to students studying in Japan fora duration of more than 3 months but less than four years and 3 months (MOFA, 2023). On theother hand, universities count students who participate in not only curricular programs but also
co/extra-curricular programs such as cultural expeditions (Kuroda et al., 2018) regardless of88

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their study period in outbound mobility. This includes students participating in study abroad
programs based on university exchange agreements (MEXT, 2023).
Similarly, research on student mobility in Japan has focused on physical mobility, examining
mobility trends (e.g., Kuroda et al., 2018), student motivations for studying abroad (e.g., Haupt
et al., 2021), and aspects like adjustment, integration, and various social, academic, culturalexperiences in Japan (Sato, 2016). On the other hand, research on virtual exchange students
situates their studies within the “internationalization of higher education” theme, such as theinternationalization of curriculum (e.g., Hammond & Radjai, 2022) aimed at improving
domestic students’ language and communication skills. While these programs may involve
both foreign or domestic students joining from the host university or overseas, the existingbinary understanding of “international” or “domestic” students exclude the students in virtual
mobility programs.
In recent official documents by the Ministry of Education, however, virtual student mobility is
acknowledged or encouraged, even after the pandemic. For example, the JV-Campus project
noted that it “will increase the number of participating institutions from Japan and abroad,enrich and expand the contents we provide, including programs related to the Japanese
language and culture, and establish new international exchange programs utilizing the online
platform” (MEXT, 2022). Another MEXT document considering student mobility after the
Top Global University project ends in 2024 mentions, “as hybrid education that combines faceto-face classes and distance/online education is progressing in higher education as a whole, itis necessary to make innovations in studying abroad in light of this trend” (MEXT, 2023).
However, at the same time, the government announced that it is considering setting up anothertarget number for student mobility—to increase inbound international students to 400,000 andoutbound domestic students to 500,000 by 2023 (J-MIRAI, 2023). The target emphasizes
physical mobility as Jitsu ryūgaku or “authentic or actual study abroad.” This was highlighted
in the MEXT document:
Excellent international students are accepted to study in Japan and provided with
opportunities to receive education alongside Japanese students in Japanese society
while also taking advantage of distance and online access. (MEXT, 2023)
Analysis of these documents shows tension and lack of clarity on what “student mobility” isand its value for Japan. While the documents call for the “reconsideration of study abroad”
acknowledging virtual mobility, the virtual mobility programs are not perceived as a fullfledged program but more as a supplementary model to sustain or promote physical mobility.
In this unclear and shifting context, this paper explores virtual program students’ understandingof “international students” and “study abroad” by asking a) how they define “international
students” b) how they experience virtual programs and c) if their experience was similar ordifferent from their imagined “international students” experience. By exploring the alternative89

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definition of “international students” through the lens of VSM students, we aim toreconceptualize and update the traditional and outdated notion of “international students.”
Methodology
This qualitative study is based on discourses of students who have taken VSM course in 2022.
The VSM program in this study was launched in the fall of 2021 at one of the leading national
universities in Japan, under the term “Virtual Study Abroad” Program. Although the program
was originally implemented to counter the pandemic restrictions on mobility, it continued after
the pandemic, collaborating with more universities to share courses online in diverse
disciplinary fields. On average, approximately 16 courses were mutually shared with 3-7
partner universities since then up until 2023, involving on average 72 students from overseas
universities and approximately 23 students from the home university per semester.
Drawing on UNESCO’s VSM survey (UNESCO, 2022), we administered a nonmandatory
online questionnaire of virtual student mobility to students who participated in the university’s
Virtual Study Abroad program (VSM) at the end of each semester. The survey aimed tounderstand students’ motivation for taking the course, satisfaction, challenges, and future
expectations of study abroad experiences. It consisted of 54 items, four of which were optional
open-ended questions. This study focuses on the qualitative responses for the open-ended
questions that asked: “Why did you take this course?”, “Please list specific incidents or events
during the class you enjoyed.”, and “Please suggest things that could be done differently.” At
the end of the survey, we asked whether they would allow the researchers to contact them forpotential interviews about their experiences. 141 students answered the survey but 86 students
responded to open-ended questions, and 20 students agreed to be contacted for further
interviews.
In order to understand students’ lived experiences in VSM courses and their generalobservations about their peers’ experiences, we invited the students (n=20) who agreed to becontacted for potential interviews after completing the survey. After sending email invitations
to each student with an interview consent form and interview protocol, 15 students accepted
our request and scheduled online interviews via Zoom meeting. The interview protocol
consisted of three main parts with opening and probing questions. First, to elaborate on survey
responses, we asked about students’ motivations for taking VSM courses and their previous
experience in study abroad or online international programs. Then, we asked the participants
about their understanding of the concepts of “study abroad” and “international students” andwhether their experience in the virtual student mobility program aligned with theirunderstanding. Finally, we asked the participants about their learning outcomes, whether theirperspectives about themselves or the host or home country changed, and what they valued fromthe VSM learning experience. Interviews were conducted in English, Japanese, Chinese, orMongolian depending on students’ preference, and continued for approximately 1 hour.

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Participants
In total, 141 students from over ten universities answered the survey over one year at the endof the spring, summer, and fall semesters in 2022. Out of them 86 students responded to open
ended questions. For the purpose of this paper, we analyzed responses of these students to threeopen-ended questions. Of those who answered the open-ended questions, 58% (n=50) werefemale, and 42% (n=35) were male. By age group, 84% were 15-29 years old, and the rest,
16%, were above 30 years (4% per age group, 30-34, 35-39, and above 40). Over half of theparticipants (67%, n=58) were undergraduate, 19% (n=18) were master’s level, 12% (n=10)
were doctoral level students, and the rest (2%, n=2) indicated as “other.” By field of study,
23% (n=20) of the participants were majoring in Arts & Humanities, 19% (n=16) in Medical
Sciences and Social Sciences, and a few were in Computer Sciences (9%), Business &Management (8%), and Natural Sciences (6%). The majority of the respondents (64%, n=55)
indicated they did not have any previous experience in taking virtual courses, while 32%(n=28) indicated they had (see Table 1). Twenty-five students (29) are students of theuniversity we focused on, and the others are partner university students. Partner university
students come from Hong Kong, China, Germany, New Zealand, and the Philippines.
Table 1
Characteristics of Participants
Characteristics

Age, Median
GenderFemaleMale
Current program level
Undergraduate
Graduate
Field of study
Arts & Humanities
Medical Sciences
Social Sciences
Engineering & Technology
Computer Sciences
Business & Management
Natural Sciences

Questionnaire’s respondents(86 respondents)
number (percentage)
20–24 (55)Semi structured interviews(15 interviewees)
number (percentage)
25–29 (20)50 (58)35 (42)10 (67)5 (33)

58 (67)28 (33)7 (46)
8 (53)

20 (23)16 (19)16 (19)13 (15)
8 (9)
7 (8)
5 (6)

3 (20)
2 (13)
4 (27)
2 (13)
2 (13)1 (7)1 (7)The interviewed students came from 7 different countries, with 11 studying at Japanese
universities (8 from the case university) and four at overseas universities. The majority ofstudents were female (n=9) and graduate students (n=8).

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Data Analysis
Drawing on the social phenomenological approach (Schutz, 1972), we aimed to understand
students’ overall experience in the VSM program in Japan or overseas. The responses to openended questions gave an overview of students’ experiences in the program. The interview
provided more detailed nuanced understanding of each student’s experience. After analyzing
the qualitative data from the survey, we developed the interview questions that aimed tounderstand each interviewee’s experience during the interview. After transcribing theinterviews, we summarized each student’s experiences. We used students’ descriptions andmeanings they prescribed for their experiences during this process. We read each student’s
interview in full and described their experiences in our own words. For example, we wrote “theJapanese PhD student, participant 31, took … course as an opportunity to interact with overseas
students. With her heavy research tasks and financial difficulties, she could not physicallystudy abroad. The VSA program offered a chance to take a course that she was interested infrom the comfort of her home…” The summaries helped us develop an overall picture ofdiverse student experiences, particularly when the interviews were conducted in different
languages.
Then, we used a thematic analysis approach (Braun & Clarke, 2014) coding students’qualitative responses and interview transcripts separate from our summaries. We tried using
students’ own words during coding. For example, when a student said that they “felt abroad
virtually” when they were describing their experiences abroad, we coded this description as“virtually abroad.” Or when another student said he took the course for collecting local
students’ perspectives on his research topic, we coded it as “Motivation for VSM: For
research.” Then, two researchers worked together to apply categories for the codes, searchingfor overarching themes, and defining and naming the themes. The authors reviewed the themesin the context of literature and discussed them to reach a final consensus. We developed fivemain categories with 28 codes that included “motivations for taking the VSM courses”(information update, academic learning, research, job hunting etc.), “the learning experiencein VSM” (e.g. academic, cultural, friendship, language), “short term outcome” (e.g.
communication skills, cultural understanding, academic knowledge or other information,
perspective change), as well as “meaning of study abroad” and an “access to international
education.” In reporting the results, we mainly quote interviewees’ words as they provided
more detailed answers. Only when indicated, the quotes are from the survey.
Results
VSM as a More Accessible Form of International Student Exchange
When asked about their motivation for taking virtual study abroad course, students in thesurvey mentioned that they wanted to expand academic or research knowledge by exploring
academic content from other countries’ perspectives and practices, to expand theirunderstanding of other cultures and improve their cross-cultural communication skills andlanguage skills. While for some students, the VSM program was a more convenient option,92

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saving the logistics of going through visa applications, reserving accommodation, and planning
for academic and cultural activities, for some, it was the only possible option to get exposed tointernational education.
When we elaborated this during the interview, a Japanese PhD student said:“It would be difficult to study abroad because of my high amount of student loans.
Thanks to this course, I realized that even if I couldn’t go abroad, I don’t need to giveup learning with overseas students.”
Another doctoral student from South Korea studying in Japan also said:“Because I am married and have family obligations, I would not have consideredphysically studying abroad. However, this course provided an opportunity to getvaluable information about Chinese and Taiwanese family construction and cultural
aspects, which was an important topic for my research.”
These students did not consider physical study abroad programs for the financial burden andlengthy time commitment. For them, the VSM program provided access to international
education overseas that they could not have otherwise afforded due to financial difficulties,
family obligations, and other work and study responsibilities.
Furthermore, VSM was more convenient to participate in terms of logistics. A student from
Germany said, “for this course, I don’t need to take an intercontinental flight, which causes
jetlag, CO2 and is difficult to fit into the schedule. If this course had not been offered as a
virtual study abroad, I would not have taken it.” While she viewed the VSM course as very
important for her studies and employment, she did not consider traveling abroad only for taking
this course.
The VSM program also provided opportunities to interact with foreign students. A master’s
student in Japan said:“We do not have any foreign students in my lab, so I don’t have an opportunity tointeract with foreign students…due to my research work and job hunting, I cannot
afford to travel abroad during my master’s program”.
The majority of Japanese students do not have a chance to interact with foreign students outside
foreign language classes (Morita, 2012). While English-medium programs in Japan haveattracted a large number of foreign students, these programs are isolated from mainstream
programs, and there is little interaction among students (Poole et al., 2020). Therefore, theVSM program in diverse disciplines makes it possible for Japanese students to take a class intheir major without long-term commitment.

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At the same time, because the program was more affordable and easier for students toparticipate in, some took the program to test the overseas university program or the learning
environment before physically going there. In the survey, a Japanese student mentioned, “Iwant to go to China in the future, so I thought it was valuable to learn about China from Peking
University, a top university in China.” Another interviewee, a Chinese doctoral student
studying in Japan as an international student said, “I’m graduating soon. I plan to work [teachat a university] back home [in China]. Therefore, I took the course before going there to makeconnections and see what it’s like to be working [teaching] there.” For this student, the VSM
program was utilized to build social networks in the home country and prepare for the teachingprofession in China.
Additionally, students took the VSM course to remain engaged in international exchange afterparticipating in physical study abroad programs. A Japanese undergraduate student explained
during the interview, “I took the course because it had lots of discussion components withforeign students. I wanted to improve or not forget my English skills after coming back from
Australia.” These findings illustrate that students taking the VSM program are diverse, andtheir experiences and motivations are multifaceted, from academic to practical reasons.
Finally, in the survey, a few students mentioned climate change or environmental pollution as
an incentive to take the VSM model. While the student from Germany mentioned CO2 causedby air travel as one factor for avoiding the physical travel, another student who took an SDG
course in China said:“The global aviation industry generates around 2.1% of all human-induced carbon
dioxide (CO2) emissions. E-meetings, lectures, and conferences could effectively
reduce CO2 emissions and at the same time enable virtual interactions between students
from all around the globe”.
While students in this study thought the VSM program is a more green and environmentallyfriendly form of exchange, it was not their top reason for choosing to participate in VSM. For
the program participants, the reasons were rather related to being convenient and accessible.
Learning Experience: Mobility of Minds
Students’ experience in the VSM programs varied due to different academic content, delivery
types (e.g., whether it includes discussion sessions or not), or formats (e.g., length of the
program). At the same time, some students said they were able to become completely immersedin the host university’s academic course. A student from Germany said:“During the physical study abroad experience, we are often so overwhelmed by a newenvironment and need to take some time to adjust to the environment before actually
starting to take in the academic contents or drawing on our reflections. However, the virtual
course allowed me to just focus on the academic contents without worrying about things

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like adjustment because I joined the Japanese classroom and teaching style from the
comfortable space of my home.”
Similarly, during the interview, a student from Taiwan highlighted, ‘VSM is best for learning
new ideas. The program brought together top researchers from diverse fields, which issometimes difficult to experience if you visit one professor’s lab as a research student in Japan’.
Some of these students already had extensive overseas experience before participating in VSM
programs. For them, the academic content was more important than building relationships or
having cultural experiences. These students challenged underlying assumptions in international
education in Japan that short-term exchange students seek mainly cultural experience or thatphysically being present in host country will have a positive learning experience. For example,
a student from Germany further highlighted:“I know many students who did not make any Japanese friends or talked to any Japanese
students while on study abroad program. So being in the environment does not always mean
you get to have intercultural experience. Instead, during this kind of [VSM], you can takeclasses with Japanese students, interact with them, try to understand how they think etc\andthen you go there…is better.”Students’ experience also highlighted how VSM mobilizes students and encourages
perspective transformation; thus, we call it “mobility of minds.” While we expect VSM
students to be moving from home to host, it can also be reversed from host to home. A Chinese
student studying in Japan as an international student talked about her experience taking a VSM
course from her home country in China. Although she was also a Chinese student, students inthe class often approached her as an international student, mostly asking about her experiencein Japan. Through class discussions and interactions with students in China, she could reflect
on her mindset, values, and how she understood Chinese and Japanese society. She said:“I experienced education in China before, as I graduated as an undergraduate there, but
this course gave me a chance to pull back and look at it [Chinese education] like fromthe third space. I could observe better and find things I have never seen before.”
She also talked about her interaction with foreign students studying in China. These
interactions with students in China while she is living in another country helped her revisit her
old understandings and expand her perspectives about herself and her home country.
Another Japanese student viewed her experience as having “opened her eyes” to the Chinese
culture:“I learned a lot about the university’s academic culture, or the university structure basedon how they organize their classes, and their rules…learned about what students were
eating, cooking, and talking about by joining their chat group. I could follow their
updates on WeChat Moments.”

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She compared this experience with her previous experience abroad, where she felt
overwhelmed and could not participate in class or social activities as much as she wanted. She
connected it with her being in a host country where the English language is the native language
as well as the difficulty of navigating different new cultural and social expectations and thechallenges to fit in. Compared to this experience, she did not need to feel accepted or form her
own group of friends to be able to actively participate in class.
Overall, students talked about their exposure to different academic thoughts, expanding their
networks through class and outside-class online activities such as WeChat or WhatsApp,
practicing a foreign language, or gaining new perspectives about their perceptions. Thesestudents focused on interaction, communications, and perspectives change as a result of taking
the VSM class.
“Student Mobility” and “International Student” through the Students’ Eyes
When asked about their understanding of “study abroad” or “international students,” students
did not have a shared understanding. The majority of interviewees' understanding of“international students” was often around the technical definitions of having a visa of thatcountry or being physically present in a foreign country. For example, a Mongolian student inJapan said:“I am an international student in Japan, and I do not think my experience of taking acourse virtually from other countries equals my current experience. Therefore, I would
not call my virtual experience a true “study abroad experience” because I am notphysically there.”
She talked about everyday life in a foreign country and interacting with citizens and neighbors
as more important to the “international students” experience than “limited experience of talking
to other students in a limited setting.” Spontaneous interactions during everyday life and how
they overcome new challenges are important for these students’ international experience.
Several students said they would consider “international students” as those who are in anenvironment where they are not native speakers of that country, or they do not have citizenshipof that country. These students considered “international students” as not necessarily being
physically in a foreign country. A Japanese master’s program student in Japan said ‘I felt
foreign, only my name was Japanese on the Zoom list, and the students in my class approached
me on WeChat asking questions about student’s life in Japan.’ For these students, the VSM
program provided “study abroad experience.”
Another group of students who viewed their experience as “international” even though they
did not travel abroad or talk in a foreign language. Some of them called it as “virtually being
abroad.” The VSM program had an international student studying in Japan who took an online
course from her home university while doing her fieldwork overseas. We had Chinese students

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studying in Japan taking an online course from China or German students taking a class in her
native language from Japan. For example, the Chinese student said:“Although I probably do not match the definition of international student, I had aninternational mobility experience. I discussed with foreign students in China, I was able
to expand and shift my perceptions about China, the Confucius philosophy.”
For these students, the current definition of “inbound” or “outbound” or “study abroad” or“internationalization at home” cannot fully describe them or their experiences. This isproblematic because it has wider implications for the support and services that VSM students
receive in international exchange. A Mongolian student in Japan touched on this topic when
she illustrated certain privileges associated with being labeled as an “international student.”
She said:“There can be extra support and services available for international students on
campus, such as a tutor to help you adjust to the new culture. However, during theonline exchange, we did not have such wide support available.”
During the pandemic, due to the full stop of physical mobility, universities started using virtual
modes as “mobility,” counting students who join these programs and “study abroad,” counting
domestic students who take other universities’ programs as outbound and vice versa. However,as physical mobility resumes, VSM students might not receive certain types of support services
available to physically “inbound” or “outbound students.” In this context, it is important toconsider expanding the notion of “study abroad” or “international students” to be inclusive of
programs such as VSM.
Given the above insights from the students, we believe that the term “internationalized” student
is a more appropriate term, to describe students who participate in mobility. A Vietnamesestudent in Japan describes this necessity, “it doesn’t matter where you study [physical mobility]or what language you use. What you learn as a result of this experience matters more.” Another
Chinese student said:“An internationalized student depends on whether there is a change in the way of
thinking, whether there is an improvement in language skills, and whether there is achange in the way of looking at the world.”
Thus, a term “internationalized students” shifts their focus to the outcome and quality of
programs, while the more traditional term of “international students” focuses on thecharacteristics of students participating in the program (see table 2).

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Table 2.
“International” vs “Internationalized Students” in Japanese Higher Education Context

Description

CommonmeasuresInternational students“inbound” or “outbound”

Internationalized students
Students who have developed
intercultural skills, more wider
Students physically in a foreign
perspectives about themselvescountry, often with a “student visa” and the world as a result ofstudying for a certain period.
participating in the international
exchange, including VSM
Quantitative:
Qualitative:
Counts students who participates in Evaluates students’ learning“study abroad” program
outcomeOur study found discussions of “student mobility” parallel or diverging from more “traditional”
discussions of mobility focused on “outbound” or “inbound” physical mobility. Some of thelearning outcomes, skills, and networks the students participating in VSM develop are similar
to “study abroad” programs, but the technical definitions of “international students” do not
fully describe VSM students and their experiences. Instead, “internationalized” students would
be a more appropriate term to describe VSM students, in the sense of their outcomes whosecharacteristics are more qualitative.
Discussion and Conclusions
In this paper, we explored virtual student mobility (VSM) from students’ perspectives basedon sample students who joined several VSM programs at a top national university in Japan andits partner universities. While Japan has been at the forefront of implementing virtual student
mobility (VSM) programs in response to the COVID-19 pandemic, other countries have also
explored similar models of international education. For example, in Europe, the Erasmus+
program has expanded its offerings to include virtual exchanges, focusing on fosteringintercultural dialogue and soft skills development through technology-mediated interactions(Helm & van der Velden, 2020). In the United States, the Stevens Initiative has been
instrumental in promoting virtual exchanges, particularly between students in the Middle Eastand North America, demonstrating the potential of VSM to increase access to international
education for underrepresented student populations (Stevens Initiative, 2023).
These initiatives reflect a broader global trend toward integrating virtual components into
traditional student mobility frameworks, thereby increasing the accessibility and inclusivity ofinternational education. However, the adoption and integration of these models varysignificantly across different educational contexts, influenced by factors such as technologicalinfrastructure, institutional support, and cultural attitudes toward online learning.

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As the internationalization of higher education in Japan is one of the national strategies torevitalize its economy, it has largely focused on student physical mobility. Until the COVID19 pandemic, government initiatives focused on physical student mobility with little or noalternative digital provisions. While collaborative online learning programs (e.g., COIL,
telecollaboration) were implemented to expose students to international education, they havenot been considered “mobility” programs. However, our findings show that VSM programsare viewed as more accessible, convenient, green, and inclusive alternatives to physicalmobility programs. Students chose this mode not only due to pandemic restrictions or postCOVID uncertainty but also because they could afford it—in terms of time, logistics, finances,or space. Furthermore, they did not need to cope with, as they described, a daunting physicalexperience of being in a foreign environment, or they were not ready yet to physically come tothe host university. Therefore, most students wanted to have the VSM option open andavailable even after the pandemic.
In this sense, VSM has much more potential than being “supplementary” to physical mobility.
VSM models have great potential to internationalize students and universities. As mentioned
in EADTU (2010, p. 4), virtual mobility could be viewed not just as a tool to enhance physicalmobility, but as an innovative and complete form of international mobility in its own right. It
offers new and creative educational opportunities, allowing for the involvement of multiple
international universities simultaneously, rather than being limited to just one host and one
home institution, as is typically the case with physical mobility programs. Virtual mobility also
provides more flexible study options that can be shorter in duration, less dependent on specifictimeframes, and not tied to a particular location, offering students more personalized andspecialized opportunities
The lack of physical change of space to different cultural and social environments meant thatstudents could concentrate more on academic content or communication with instructors or
peers without being distracted by the new environment. While students still needed to navigate
online systems and different academic calendars, teaching styles, and expectations, they wereless overwhelmed. Some students said they were more “equal” in the learning process because
they were all “same,” learning the same content without the need to feel accepted by the hoststudents.
This more “neutral” space encouraged students to be more active in class, develop a newperspective and a more global mindset, and understand and accept different cultural
backgrounds and values through the VSM program and their interaction with peer students
during and after class. Students could also develop networks beyond the classroom based onsocial media groups and other platforms, without the shadow of being different and needing tofeel accepted.
There were also students who were not interested in the cultural component of the “study
abroad experience.” While we often associate “study abroad” experience with “interculturallearning,” some students in our study did not choose study abroad programs for interculturallearning purposes. Students who have studied in the host country before or who have developed

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rich intercultural skills, or students specifically looking for academic or research content, were
only interested in the academic or research part of the study abroad programs. For thesestudents, virtual mobility programs may provide the information they seek in a more focusedand condensed format. This sharing of knowledge fosters collaboration and communication
across borders stepping up on cultural competencies, social networks developed.
Overall, students in VSM programs had more fluid experiences of mobility—mobility ofknowledge and communication. Some students were joining the VSM program from home,while others were taking home institution classes from overseas while physically studying or
traveling abroad. They do not fit in the current categorization of “inbound,” “outbound,” “study
abroad,” “internationalization at home initiatives,” or “international students” that Japanese
universities keep track of and get evaluated by in their goals of internationalization. However,this technical categorization and definition of “international student” matters because it entails
different services and privileges for students as well as investment to other alternative types ofinternationalization. Focused on physical mobility, universities allocate staff and resources tohelp with inbound and outbound students. However, VSM students’ experience and challenges
show the danger of falling into the gray zone—not receiving support from either institution
because they are not counted as either “inbound” or “outbound.”Although they may not be counted as “international students” based on the more traditional
definition, some students nevertheless viewed their experience as “international” and that they
have become more “internationalized” without crossing the border. As the VSM model is fluid
and inclusive, and emphasizes students’ learning outcomes over their physical experiences, theterm “internationalized student” seems more appropriate to describe these students. In thissense, while the “international students” definition is more about students’ characteristics (suchas their visa status), the term “internationalized students” highlights the skills or knowledge
students develop as a result of joining the programs. It therefore shifts the focus from the input
and characteristics of students to the output, outcome, and the impact of mobility.
Suggestions for Future Research
Given the evolving landscape of global education, further research is needed to explore thelong-term impacts of VSM programs on students' academic and personal development.
Comparative studies that examine the effectiveness of VSM programs across different culturaland institutional contexts would provide valuable insights into the factors that contribute tosuccessful virtual exchanges. Additionally, future research could investigate the role of VSM
in promoting equity in higher education by making international learning opportunities
accessible to a more diverse student population. By exploring these avenues, researchers cancontribute to the ongoing dialogue about the future of international education in a postpandemic world, helping to shape policies and practices that enhance the accessibility,
inclusivity, and impact of student mobility programs globally.

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Acknowledgements
We thank Dr. Xixi Zhang and Prof. Brendan Barrett for their contributions and advice on
designing the original survey for the virtual study abroad courses that we adopted in this study.
Also, we thank the international student affairs office for their assistance in distributing thesurvey to the students.

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References
Akiyama, Y., & Cunningham, D. J. (2018). Synthesizing the practice of SCMC-based
telecollaboration: A scoping review. CALICO Journal, 35(1), 49–76.
https://doi.org/10.1558/cj.33156
Alami, N. H., Albuquerque, J., Ashton, L. S., Elwood, J. A., Ewoodzie, K., Hauck, M., ... &Satar, M. (2022). Marginalization and underrepresentation in virtual exchange:
Reasons and remedies. Journal of International Students, 12(S3), 57–76.
https://doi.org/10.32674/jis.v12iS3.4665
Bradford, A., Ishikura, Y., & Brown, H. (2022). Sustaining internationalization: Englishmedium programs in Japan. International Higher Education, (110), 15–16. Retrieved
from https://ejournals.bc.edu/index.php/ihe/article/view/14981
Braun, V., & Clarke, V. (2014). What can “thematic analysis” offer health and wellbeing
researchers?. International Journal of Qualitative Studies on Health and Well-being,
9, 26152. https://doi.org/10.3402/qhw.v9.26152
Cabinet Office (2014). Wakamono no kaigai ryugaku sokushin jikkokeikaku [Action plan for
promoting study abroad for young people]. Retrieved fromhttps://www.cas.go.jp/jp/seisaku/ryuugaku/dai2/siryou2_2.pdf
EADTU (2010). EPICS: New opportunities for international student mobility. Retrieved
from
http://www.eadtu.eu/documents/Publications/VM/EPICS_New_Opportunities_For_In
ternational_Student_Mobility.pdf
Enkhtur, A., Li, M. & Zhang, X. (2023). Virtual student mobility from students’ perspectives:
Case study from Japan. Higher Education Forum, 20, 59–78.
https://doi.org/10.15027/538511
Fritz, R., & Marchewka, M. (2023). Transformative learning: Investigating perspective
changes toward developing global human resources in a virtual exchange project.
Innovations in Education and Teaching International, 61(2), 370–384.
https://doi.org/ 10.1080/14703297.2022.2163277
Giralt, M., Betts, A., Pittarello, S., & Stefanelli, C. (2022). Scenarios for the integration ofvirtual exchange in higher education. Journal of International Students, 12(S3), 116–
134. https://doi.org/10.32674/jis.v12iS3.4629
Hammond, C. D., & Radjai, L. (2022). Internationalization of curriculum in Japanese higher
education: Blockers and enablers in English-medium instruction classrooms in the eraof COVID-19. Higher Education Forum, 19, 87–107.
Haupt, J. P., Sutrisno, A., & Hermawan, M. S. (2021). Motivations and outcomes in dualdegree programs: Insights from graduate scholarships for Indonesian civil servants.
Asia Pacific Education Review, 22, 535-548.
https://doi.org/10.1007/s12564-021-09687-9
Helm, F., & van der Velden, B. (2020). Erasmus+ virtual exchange impact report 2019.
Publications Office of the European Union. Retrieved from:https://data.europa.eu/doi/10.2797/513584
Hofmeyr, A. S. (2021). Rethinking the concept of global human resources in the Japanesehigher education context. Asia Pacific Journal of Education, 43(1), 62–78.
https://doi.org/10.1080/02188791.2021.1889970

102

IAFOR Journal of Education: Technology in Education

Volume 12 – Issue 2 – 2024

Ishikawa, M. (2011). Redefining internationalization in higher education: Global 30 and themaking of global universities in Japan. In Willis, D. B., & Rappleye, J. (Eds.),
Reimagining Japanese education: Borders, transfers, circulations, and thecomparative (pp. 193–223). Symposium Books Ltd.
JASSO (2019). Result of an annual survey of international students in Japan 2018. Retrieved
from https://www.studyinjapan.go.jp/en/statistics/zaiseki/data/2018.html
JASSO (2021). 2019 nendo nihonjin gakusei ryugaku jokyo chosakekka [Survey results ofJapanese students studying abroad in 2019]. Retrieved fromhttps://www.studyinjapan.go.jp/ja/_mt/2021/03/date2019n.pdf
JFIU (n.d.). Japan Forum for Internationalization of Universities. Retrieved fromhttps://www.jfiu.jp/en/
J-MIRAI. (April 27, 2023). Japan-mobility and internationalisation: Re-engaging andaccelerating initiative for future generations. Retrieved fromhttps://www.cas.go.jp/jp/seisaku/kyouikumirai/pdf/230427jmirai.pdf
Kuroda, K., Sugimura, M., Kitamura, Y. & Asada, S. (2018). Internationalization of higher
education and student mobility in Japan and Asia. Global Education Monitoring
Report, Japan International Cooperation Agency and UNESCO. Retrieved fromhttps://www.jica.go.jp/jica-ri/publication/other/l75nbg000010mg5uatt/Background_Kuroda.pdf
Lee, J., Leibowitz, J., & Rezek, J. (2022). The impact of international virtual exchange onparticipation in education abroad. Journal of Studies in International Education,
26(2), 202–221. https://doi.org/10.1177/10283153211052777
MEXT (2008). Ryugakusei30mannin keikaku no kosshi torimatome no kangaekata [Conceptof the 300,000 foreign students plan]. Retrieved fromhttps://www.mext.go.jp/b_menu/shingi/chukyo/chukyo4/houkoku/attach/1249711.htm
MEXT (2020). Supagurobaru daigaku soseishien jigyo oyobi daigaku no sekaitenkairyoku
kyoka jigyo saitakuko nitaisuru kinkyuanketo kekka no hokoku [Results of theemergency survey from universities in Top Global University project]. Retrieved
from https://www.mext.go.jp/content/20200924-mxt_daigakuc03-000010089_5.pdf
MEXT (2021, June 25). Shingata korona o fumaeta aratana kokusai kyoiku koryu ni muke.
[Towards a new international educational exchange considering the Covid-19].
Retrieved from https://www.nii.ac.jp/event/upload/20210625-03_MEXT.pdf
MEXT (2023). Gaikokujin ryugakusei zaisekijokyo chosa oyobi nihonjin no
kaigairyugakushasu nado ni tsuite [Survey of international student enrollment andnumber of Japanese students studying abroad]. Retrieved fromhttps://www.mext.go.jp/content/20230201-mext_kotokoku01-1412692_00003.pdf
MIC (2005). Ryugakusei no ukeire suishin shisaku ni kansuru seisakuhyokasho [Policy
evaluation report on measures to promote the acceptance of international students].
Retrieved from https://www.soumu.go.jp/main_content/000250658.pdf
Morita, L. (2012). English and intercultural interaction in the internationalization of aJapanese university. Journal of Intercultural Communication, 12(3), 1–07.
https://doi.org/10.36923/jicc.v12i3.607

103

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Volume 12 – Issue 2 – 2024

O’Dowd, R. (2021). What do students learn in virtual exchange? A qualitative content
analysis of learning outcomes across multiple exchanges. International Journal of
Educational Research, 109, 101804. https://doi.org/10.1016/j.ijer.2021.101804
Ota, H. (2018). Internationalization of higher education: Global trends and Japan’schallenges. Educational Studies in Japan, 12, 91–105.
https://doi.org/10.7571/esjkyoiku.12.91
Ota, H., Shimmi, Y., & Hoshino, A. (2023, March 13-17). International education with ICT
during COVID-19 Pandemic: Japanese Universities’ Experiences [Conference
presentation]. APAIE 2023, Bangkok, Thailand.
Poole, G., Ota, H., & Kawano, M. (2020). Tracing the developments of the “global education
effect” in Japanese higher education: Discourses, policy, and practice. In Doerr, N. M.
(Ed.), The global education effect and Japan: Constructing new borders andidentification practices (pp. 33–60). https://doi.org/10.4324/9780429292064-3
Rumbley, L. E., Altbach, P. G., & Reisberg, L. (2012). Internationalization within the higher
education context. In Deardorff, D. K., De Wit, H., & Heyl, J. D. (Eds.), The SAGE
handbook of international higher education (pp. 3–26).
https://doi.org/10.4135/9781452218397.n1
Sato, Y. (2016). Hi-kanjiken shusshin shihi ryugakusei no nizu to tokucho-Nihon gakusei
shien kiko shihi ryugakusei seikatsu jittai chosa no bunseki kekka kara [Needs andcharacteristics of funded international students from non-kanji regions-based on theanalysis of the living conditions survey by the Japan student services organization(JASSO)]. Web Magazine Study Abroad Exchange, 69, 1–16. Retrieved fromhttps://www.jasso.go.jp/ryugaku/related/kouryu/2016/__icsFiles/afieldfile/2021/02/18/201612ryugakukoryu.pdf
Sato, K. (2022). Shingatakorona wo fumaeta aratana kokusaikyoikukoryu ni mukete [Toward
a new international educational exchange in light of the new coronavirus]. IDE, 638,
4–9.
Schutz, A. (1972). The Phenomenology of the Social World. Northwestern university press.
Schweighofer, E. (1996). Teleteaching in the EU HUMANITIES project’, BILETA’96
Conference Proceedings, 3. The Journal of Information, Law and Technology.
Retrieved fromhttp://www2.warwick.ac.uk/fac/soc/law/elj/jilt/1996_3/special/schweighofer
Shimmi, Y., Hoshino, A., & Ota, H. (2021). Posutokorona nimuketa kokusaikoryu:
johotsushin gijutsu (ICT) wo katsuyo shita aratana kyoikujissen yori [International
educational exchange and use of ICT in the post Covid 19 era: Emerging newpractices]. Web Magazine Study Abroad Exchange, 120, 26–41. Retrieved fromhttps://www.jasso.go.jp/ryugaku/related/kouryu/2020/__icsFiles/afieldfile/2021/04/19/202103shimmihoshinoota_2.pdf
Stevens Initiatives (2020). Virtual exchange impact and learning report. Retrieved fromhttps://www.stevensinitiative.org/resource/virtual-exchange-impact-and-learningreport-2/
Stevens Initiatives (2023). Virtual exchange impact and learning report. Retrieved fromhttps://www.stevensinitiative.org/resource/2023-virtual-exchange-impact-andlearning-report/

104

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Volume 12 – Issue 2 – 2024

UIS (2023). UNESCO Institute for Statistics (UIS) database. Extracted fromhttp://data.uis.unesco.org
UNESCO IESALC (2022). Moving minds: Opportunities and challenges for virtual student
mobility in a post pandemic world. Retrieved fromhttps://unesdoc.unesco.org/ark:/48223/pf0000380988
Yamamoto, B. A. (2018). The internationalization of Japanese higher education: Incremental
change in a dynamic global environment. In: Yonezawa, A., Kitamura, Y.,
Yamamoto, B., & Tokunaga, T. (Eds.), Japanese education in a global age.
Education in the Asia-Pacific Region: Issues, concerns and prospects (pp. 221–239).
Springer, Singapore. https://doi.org/10.1007/978-981-13-1528-2_13
Yokota, M., Ota, H., & Shimmi, Y. (2018). Kaigai ryugaku ga kyaria to jinsei ni ataeru
impakuto:daikibo chosa niyoru ryugaku no kogasokutei [Impact of study abroad on
career development and life]. Tokyo: Gakubunnsha.
Yonezawa, A. (2011). The internationalization of Japanese higher education: Policy debates
and realities. In Marginson, S., Kaur, S., & Sawir, E. (Eds.), Higher education in the
Asia-Pacific: Strategic Responses to Globalization, 36, 329–342.
https://doi.org/10.1007/978-94-007-1500-4_17
Yonezawa, A. (2014). Japan’s challenge of fostering “global human resources”: Policy
debates and practices. Japan Labor Review, 11(2), 37-52. Retrieved fromhttps://www.jil.go.jp/english/JLR/documents/2014/JLR42_all.pdf#page=39
Yonezawa, A. (2021). Reimaging university identities through rankings in Japan: Thetransformation of national policies and university behaviors in the broader East Asian
context. In Hazelkorn, E., & Mihut, G. (Eds.), Research Handbook on University
Rankings, 231–246). https://doi.org/10.4337/9781788974981.00027
Corresponding author: Ariunaa Enkhtur
Email: enkhtur.ariunaa.cgin@osaka-u.ac.jp

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Using AI-Powered Speech Recognition Technology to Improve English
Pronunciation and Speaking Skills
Nutprapha K. Dennis
Ubon Ratchathani Rajabhat University, Thailand

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Abstract
This study aimed to investigate the impact of AI-powered Speech Recognition Technology (AISRT) in improving English pronunciation and speaking skills among EFL learners.
Additionally, it explored the opinions and responses of EFL learners towards the use of thistechnology for pronunciation and speaking skill enhancement. The research employed a pretest/post-test design and a survey questionnaire with a multiple-choice rating scale and openended questions. The study included a sample of EFL learners who received instruction andpractice using an AI-SRT program over a designated period. Data were collected through pretest and post-test assessments to measure changes in pronunciation and speaking skills. A
survey questionnaire was administered to gather participants’ opinions and responses regarding
the program’s effectiveness and their overall experience. The data were analyzed using
descriptive statistics to examine the central tendency, variability and distribution of pre-test andpost-test scores. Comparative analysis was conducted using paired t-tests to determine thesignificance of the improvement in pronunciation and speaking skills. Qualitative data fromthe survey questionnaire were analyzed thematically to identify recurring themes and patterns
in participants’ opinions. The findings of this study contribute to the understanding of the
impact of AI-powered speech recognition technology on EFL learners’ pronunciation andspeaking skills. The results reveal the effectiveness of such technology and provide valuable
insights into learners’ perceptions and experiences. This research has implications for language
educators, curriculum designers, and developers of educational technology in designingeffective pronunciation and speaking skill enhancement tools for EFL learners.
Keywords: AI-powered speech recognition technology, EFL, English pronunciation, English
speaking skills, pre-test/post-test design, survey questionnaire

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Artificial intelligence (AI) is starting to revolutionize English pronunciation and speaking. AIpowered speech recognition technologies have shown potential in assisting students inimproving their speaking skills. These programs, which are software applications or systems
that employ AI algorithms and machine learning, convert spoken language into text. They arecapable of analyzing and comprehending human speech, enabling devices or applications to
respond to voice commands, transcribe audio recordings, or provide real-time, speech-to-text
services.
While Speechling, Duolingo, and Google Assistant are prominent examples of AI-powered
speech recognition technologies, it is important to explore additional alternatives to furnish
learners with a diverse array of tools and resources (Garca-Sánchez, Soler-Urzúa, & Nussbaum,2020). This study delves into various AI-powered speech recognition programs that are
beneficial for improving English pronunciation and speaking abilities.
The use of AI technology has become more and more popular in language teaching. A
significant number of studies have been done on how AI-powered speech recognition systems
improve speaking and pronunciation. AI-based pronunciation feedback has been demonstrated
by Garca-Sánchez and colleagues (2020) to enhance non-native English speakers’
pronunciation. Similar increases in language learners’ speaking fluency were observed by Zou
and colleagues (2021) when they used AI-powered speech recognition.
The current study focuses on the implications of employing the Speechling program, an AIpowered speech recognition technology for English as a Foreign Language (EFL) learners,
especially regarding pronunciation and speaking. Speechling is an online platform to help
individuals improve their foreign language skills. The application uses artificial intelligence(AI) to deliver individualized pronunciation and fluency feedback. Learners can practice withactual human voices while recording sentences and receive expert guidance. It uses spaced
repetition algorithms to improve language skills and is completely free.
The study aims to bridge the research gap by evaluating the effectiveness of AI-SRT in English
pronunciation and speaking practice. Factors such as user-friendliness, program accuracy,
feedback mechanisms, and additional functionalities will be explored. This inquiry will aid
educators and students in selecting optimal tools for their requirements (Chen & Duan, 2020).
Moreover, the study will explore the advantages and impact of speech recognition tools driven
by AI. By broadening knowledge and comprehension of AI-SRT programs, students can makeinformed decisions in selecting the most suitable one for their personal learning objectives (Jiaet al., 2019). Finally, this study seeks to empower language learners, educators, and researchers
to harness AI technology effectively for practicing pronunciation and speaking skills. By
evaluating AI-SRT programs for English improvement, learners can discover accessible
language learning solutions tailored to their preferences. The ultimate goal is to assist students
in achieving superior English speaking and pronunciation capabilities.

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Conceptual Framework
The theoretical underpinnings of this research were grounded in well-established theories oflanguage acquisition, second language learning, and computer-assisted language learning(CALL). To inform the design and implementation of AI-powered speech recognition
technology (AI-SRT) programs for English pronunciation and speaking instruction, the study
drew on Krashen's Input Hypothesis (Krashen, 1981) and Vygotsky's Sociocultural Theory(Vygotsky, 1934). These frameworks provided a robust theoretical base for understanding the
cognitive and social dynamics of language learning and the advantages of integrating
technology in language instruction. By incorporating these established theoretical perspectives,
the study set a strong foundation for evaluating the effectiveness of the Speechling programand its impact on improving the pronunciation accuracy and speaking proficiency of language
learners.
Background of the Study
Pronunciation and speaking present continual challenges for language learners, who have tonavigate the complexities of linguistic and cultural integration to communicate accurately. The
role of AI-powered speech recognition technology in language education has been a subject ofinterest, especially as it offers real-time feedback on pronunciation and speaking. Through thecapabilities of machine learning and natural language processing, AI-SRT is able to pinpoint
errors, mispronunciations, and areas in need of improvement within a learner's spoken language(Jia & Zhang, 2018). This advanced technology can facilitate personalized feedback, enabling
tailored practice that enhances pronunciation, fluency, and overall speaking skills. Empiricalevidence has suggested that AI-SRT could be instrumental in advancing language proficiency,with Computer-Assisted Pronunciation Training (CAPT) showing notable benefits for language
learners (Jia & Zhang, 2018).
While existing studies yielded promising insights, there remains a need for further study into
the application of AI-SRT in language learning. Additional study was essential to address
potential limitations, refine its deployment, and verify its constructive contribution to language
learning outcomes. This is important as its potency in augmenting pronunciation and speaking
competencies for specific language functions has gained recognition in the realm of second
language acquisition.
Moreover, the insights gleaned from such investigations have the potential to influencecurriculum design and teaching methodologies. This study sought to delve into the influenceof AI-SRT on the pronunciation and speaking abilities of language learners. Furthermore, it
aimed to provide language educators and instructional designers with valuable guidance on
how to effectively incorporate AI-powered tools in language education programs.

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Statement of the Problem
The process of learning may have been impeded by limited access to high-quality learning
tools and a shortage of experience between teachers with technology. These challenges may
obstruct the effective practice of English pronunciation by EFL students. Therefore, thepurpose of this study was to determine whether Speechling, an AI-powered speech recognition
technology program, facilitated the accurate pronunciation of English by EFL learners.
Furthermore, the study explored the manner in which EFL learners utilized an AI-SRT program
to enhance their English pronunciation and speaking skills. This research supported educatorsin the implementation of a user-friendly and straightforward technology to enhance self-study,
pronunciation, and speaking practice.
Research Questions
The study was designed to address two research questions:
1) What is the effectiveness of an AI-SRT program in enhancing EFL learners' English
pronunciation and speaking abilities?
2) What are EFL learners’ perceptions and responses to the use of AI-SRT for English
pronunciation and speaking practice?
Purposes of the Study
This research is dedicated to exploring the potential benefits of utilizing an AI-powered speech
recognition technology program to improve the English pronunciation and speaking
proficiency of EFL students learning English. The study sets out to examine two principal
areas.
The first area of focus is to assess the impact of the AI-SRT program on the participants' ability
to enhance their English pronunciation and speaking skills. The research will employ
systematic evaluation and in-depth analysis to determine the extent to which the AI-SRT
program supports learners in the precise articulation of English words and phrases, as well as
assesses its contribution to the overall advancement of their spoken language abilities.
The second aim is to explore EFL students' perceptions and reactions to the utilization of the
AI-SRT program as a means to practice English pronunciation and speaking. By soliciting
feedback from participants and conducting interviews and surveys, the study seeks to uncoverlearners' attitudes and opinions about the integration of AI-SRT technology into their language
education. An understanding of their experiences will offer valuable insights into the program's
receptiveness and the ease with which learners can engage with this educational technology.

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Significance of the Study
This research expands the literature on using AI-powered speech recognition technology to
improve English pronunciation and speaking for EFL learners. It aims to assist language
instructors integrate such technologies into their teaching. The study will evaluate AI-SRT in
language education to assist learners develop their language abilities without advanced learning
resources. This study could further enhance English language instruction, improving
communication between learners and native speakers. The study will examine AI-SRT's
effectiveness for enhancing pronunciation and speaking skills, advancing English language
learning. This could optimize language learning by using AI technology to improve teaching
methods.
The findings could also assist curriculum developers develop more inclusive and adaptablelearning materials for diverse learners. AI-SRT's practical benefits are demonstrated in the
study to encourage policymakers to support the integration of advanced technologies in
educational settings, which could lead to more language teaching innovation.
Literature Review
Importance of Pronunciation and Speaking Skills in Language Learning
Mastering pronunciation is essential for effective communication in a second language. Studies
underscore the necessity of incorporating teaching of pronunciation, which includes elements
such as individual sounds, intonation patterns, and speech flow, within language teaching
programs to develop well-rounded oral abilities (Kang, 2016; Major, 2016).
Research conducted by Munro and Derwing (2016) and Saito and Lyster (2019) showed thatphonetic training, activities that emphasize linguistic form, and constructive correction are key
components in improving speech pronunciation. Furthermore, Thomson (2018) highlighted theneed for dedicated instruction for educators in the art of teaching pronunciation, while also
advocating for the integration of effective pronunciation instruction techniques into language
education programs to aid in more successful language learning and oral proficiency.
Bohn and Munro (2020) delved into the difficulties that arise in learning how to pronouncewords in a second language, shedding light on the obstacles students encounter and the targeted
instruction needed to surmount them. Trofimovich and Isaacs (2019) argued for an increased
emphasis on pronunciation within language teaching to enhance communicative skills. In
addition, Zhang and Wei (2019) explored how second language acquisition, speaking abilities,
and learner anxiety are interrelated, suggesting that fostering a relaxed learning environment
can boost speech development. The collective findings of these studies make a compelling casethat the role of pronunciation in language learning is pivotal, and requires personalized teachingstrategies that build student confidence and skill. Recognizing the emotional aspects oflanguage learning, such as anxiety, is also vital in crafting an educational setting that addresses
personal learning hurdles and also advances overall language fluency.

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Scholarly work emphasizes the importance of accurate pronunciation in second language
acquisition and effective communication. To fully understand a language, teaching mustaccount for sound subtleties, stress and intonation patterns, and speech flow. The relevance of
these strategies across learning settings, and the ability to accommodate learners' diverse needs,
such as native language influences, age, and unique cognitive approaches, vary and are subject
to debate. Language teaching requires nurturing learning spaces and emotional support, as wellas technical skills. Academics also want more pronunciation training for educators. Efforts tointegrate pronunciation into academic curricula must be balanced against syllabus design,
teaching materials, and educational institution strategy. Thus, while pronunciation is essential
to language learning, its instruction requires overcoming logistical and learner-related
obstacles.
Technology-Assisted Pronunciation and Speaking Training
Recent studies have shown a growing endorsement for the use of technology in the teaching ofpronunciation and speaking skills for language learners. Research conducted by Morris (2020),
Garca-Sánchez and colleagues (2020), and Liao and Xue (2019) have revealed the positiveeffects of artificial intelligence (AI) and Computer-Assisted Pronunciation Training (CAPT) in
advancing learners' abilities to pronounce accurately and speak more proficiently. These
technological tools, especially noted for their role in the betterment of vowel sounds andgeneral spoken language performance, underscore the benefits of integrating digital aids into
language education.
However, the success of technology-assisted pronunciation and speaking training is influenced
by a variety of factors. Chen and Duan (2020) and Zou and colleagues (2021) highlighted thecritical role of learner motivation, engagement, and individual learning differences in theefficacy of these programs. The literature suggests that learners' personal preferences andresponses to training significantly shape the outcomes. Accordingly, CAPT programs that offer
personalized feedback and adaptive learning experiences are recommended to cater to thediverse needs of learners. These adaptive features can optimize technology-assisted training byaligning with learners' attitudes, motivational levels, and specific requirements, thereby
enhancing the overall effectiveness of such educational technology.
The literature reviewed herein supports using AI and CAPT to improve language learning
pronunciation. However, a thorough analysis reveals that their efficacy depends on various
factors. These factors include software quality, training program design, integration into
comprehensive curricula, and adaptability to diverse learner needs and styles. Personalized
systems require resources, and learners and instructors may have different technological skills.
Although technology provides consistent feedback, it may lack the detail of human instructors.
To improve language acquisition, technology-based language training must be carefully
weighed against individual variations, learner attitudes, and instructor responsibilities.

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AI-powered Speech Recognition Technology for Language Learning
Research examining AI-SRT suggests they offer substantial benefits for language learning.
These technologies have been shown to enhance learners' pronunciation, fluency, and alleviate
their anxiety towards language learning. For example, Li and Li (2021) affirmed that AI-driven
language learning tools significantly improve pronunciation skills. Furthermore, Zhao et al.,(2021) concluded in their meta-analysis that AI-assisted tools boost pronunciation precision,
fluency, and reduce anxiety in individuals learning a second language. Yalcin and Korkmazgil(2021) discovered that an AI-infused mobile application markedly improved English
pronunciation for EFL learners Kim (2019) observed that personalized AI-based pronunciation
lessons notably advanced the English pronunciation of Korean EFL students. Additionally, Fanand colleagues (2019) reported improvements in the spoken English abilities of Chinese EFL
learners through the use of AI. Collectively, these findings point to AI-powered speech
recognition as a promising tool for enhancing the pronunciation of language learners.
Critical analysis of the literature reviewed on AI-powered speech recognition technology inlanguage learning reveals several key areas for further study. There is a lack of discussion about
AI's potential drawbacks, such as reduced human engagement and accessibility issues. The
analysis suggests studying how the technology affects different learner demographics. Itemphasizes the need for detailed pedagogical strategies to integrate technology into language
learning. To determine the credibility and long-term effects of AI-facilitated learning, thereferenced studies' research methodologies must be carefully examined. Ultimately, itemphasizes the need for resources and help in integrating these technologies into thecurriculum and the challenges of teaching educators to use them.
Recent Developments in AI-SRT for EFL Learners (2023-2024)
Recent years have witnessed significant strides in the field of artificial intelligence (AI),particularly in its application to language learning and speech recognition technologies. Thes
following literature reviews examined studies from 2023 to 2024 that exploring the use of AIpowered tools to improve English pronunciation and speaking skills.
With a cohort of English language learners, Mohammadkarimi (2024) tested Listnr and Murf
AI pronunciation aids. A mixed-method study found that students who used these tools overtraditional teaching methods improved their pronunciation. Positive attitudes toward AI-driven
aids included increased engagement and confidence. The researchers also found difficulties in
feedback interpretation and capturing subtle pronunciation nuances, suggesting AI technology
needs improvement (Mohammadkarimi, 2024). Du and Daniel (2024) conducted a systematic
review of AI chatbots' impact on English speaking skills. Their analysis of 24 studies showedthat AI chatbots are still in their infancy, requiring further research. The results suggested AI
chatbots can boost learning, reduce speaking anxiety, and improve pronunciation. Because AI
chatbots can improve learning, English teachers, chatbot designers, and researchers should
investigate them (Du & Daniel, 2024). Persulessy and colleagues (2024) examined civilengineering students' perceptions of learning engineering English vocabulary with ELSA

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(English Language Speech Assistant). Students loved learning with AI, according to thedescriptive research. The app's feedback mechanism motivated users, indicating a bright future
for AI-assisted language learning in specialized fields (Persulessy et al., 2024).
The studies reviewed provide compelling insights into the integration of AI in language
learning, particularly in enhancing English pronunciation and speaking skills.
Mohammadkarimi (2024) presented a robust case for AI pronunciation tools, with quantitative
data to back up qualitative perceptions. However, the study's limitations in feedback accuracy
pointed to the need for more sophisticated AI algorithms capable of mimicking human-likefeedback. Du and Daniel's (2024) systematic review broadened the scope of the field byanalyzing the potential of AI chatbots across various studies. This meta-analytical approach
was helpful in understanding broader trends and collective outcomes. Nonetheless, the review
underscored the infancy of research in this domain, suggesting a vast potential for future studies
to bridge the gap in knowledge and application. The focus on specialized vocabulary learningin Persulessy and colleagues’ (2024) study added a unique dimension to the understanding ofAI's role in language education. By examining a niche area of engineering English, thisresearch offered a glimpse into the adaptability of AI tools across different disciplines.
However, the study's sample size and single application focus might limit the generalizabilityof its findings.
Collectively, these studies highlight the transformative potential of AI in language learning.
They also underscored the nascent state of this research field, advocating for continued
development and exploration of AI capabilities. Future research should aim to address thechallenges identified, such as feedback interpretation, and extend the application of AI tools toa wider range of learning environments and disciplines.
Research Methodology
To comprehensively evaluate the effectiveness of the Speechling program—an AI-powered
speech recognition technology—in enhancing English pronunciation and speaking skills, thisstudy employed a mixed-methods approach. Quantitative data was collected using a pretest/post-test design to quantitatively measure the program's impact. Concurrently, qualitative
insights were gleaned from participants' responses to a survey questionnaire, tailored to capture
their perceptions of the program.
Participants and Pilot Testing
Participants for the research were drawn from a local university's sophomore EFL students
during the Academic Year 2022. A convenient sample of 25 students, aged between 18 and 22,
was selected from the English Listening & Speaking 3 Course. They were identified as having
an intermediate level of English proficiency. Before commencing the full study, a pilot test was
conducted with a smaller, separate group of students from a similar demographic, to refine theresearch instruments. The participants for the pilot test were chosen based on their availability
and similarity to the target study group. Following the pilot, adjustments were made to the test115

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items and survey questions based on the initial feedback to enhance clarity, relevance, andengagement.
Research Instruments: Pre-test, Post-test, and Survey Questionnaire
The pre-test and post-test consisted of 30 items designed to evaluate pronunciation (20 items)
and speaking skills (10 items). The Speechling program's content informed the test items,
guaranteeing their pertinence. Each component of the test had a 30-minute completion time,with consistency in difficulty maintained across pre-test and post-test items.
The survey questionnaire, comprising 12 questions with a 5-point Likert scale and an openended question, solicited students' subjective experiences with the Speechling program. The
selection of questions was informed by the pilot test feedback to ensure they effectively
captured a comprehensive range of student experiences.
Data Collection Procedures
Data collection commenced with a thorough review of the research instruments. Participants
information and privacy were safeguarded through strategic planning and the establishment ofclear protocols. This included the distribution of consent forms, which were securely stored
along with participants' data. Only the researcher had access to the collected data to maintainconfidentiality.
During the first class, participants were introduced to the Speechling program and trained onits usage. The pre-test was administered under standardized conditions, followed by theintervention period with the Speechling program. Upon completion of the intervention, thepost-test and survey questionnaire were administered with an emphasis on honest andthoughtful completion.
Data Transcription, Coding, and Analysis
Information gathered from the assessments and questionnaires was methodically transcribedand encoded. This entailed a thorough routine of inputting data, succeeded by statisticalevaluation with the aid of suitable software tools. Descriptive statistics were utilized toencapsulate the data, and inferential statistics, including averages and standard deviations,evaluated the program's effect. The qualitative feedback underwent scrutiny to extract themes
and discern patterns, and these insights were then examined in the context of the study's
primary objectives.
Ethical Considerations
To protect participants, ethical protocols were strictly adhered to throughout the study. This
included obtaining informed consent, ensuring anonymity in data handling, and securing data

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access to authorized researcher only. These measures were paramount to ensuring the ethicalintegrity of the research.
In conclusion, the methodology was designed to be comprehensive and rigorous, incorporating
pilot testing feedback for instrument refinement, adhering to ethical standards for participant
protection, and employing a combination of quantitative and qualitative analysis to provide aholistic evaluation of the AI-SRT program's efficacy.
Results
Pre-Test and Post-Test
The step of the analysis was to delineate the outcomes of the pre-test and post-test evaluations.
These evaluations were designed to measure learners' pronunciation proficiency. The data
encompassed the performance of 25 participants
Table 1 illustrates the enhancement of pronunciation skills among 25 participants following an
educational intervention. The mean score increased from 6.40 to 13.20. The pre-intervention
score ranged from 10% to 70%, reflecting a range of abilities, while post-intervention scores
ranged from 35% to 95%, marking substantial improvement. Despite a slight increase in thestandard deviation from 3.14 to 3.30, the data suggests a more uniform improvement acrossthe group, with median scores also rising from 35% to 75%. A paired t-test confirmed thestatistical significance of these improvements, with a t-value of 7.46 and a p-value of less than.05. Hence, the progress in pronunciation abilities can be confidently attributed to theeducational intervention.
Table 1
Pronunciation Abilities Pre-Test and Post-Test Results
Test
Pre-Test
Post-Test

n
25
25

Mean
6.40
13.20

SD
3.14
3.30

Minimum
2.00
7.00

Maximum
14.00
19.00

The data underscores an improvement in pronunciation among the 25 students from the pretest to the post-test. This finding implied that educators and language instructors can enhance
students' pronunciation skills by offering more speaking opportunities, employing visual aids,
and addressing pronunciation errors promptly. A paired t-test was conducted to ascertain thesignificance of this improvement.
Table 2 displays a significant increase in the speaking skills of the 25 participants after an
educational intervention. The means scores rose from 3.28 to 7.12 out of 20. The standard
deviation remained relatively stable, shifting slightly from 1.55 on the pre-test to 1.47 on the
post-test, indicating a consistent spread of scores around the mean. The minimum scores

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increased from 3 to a higher minimum of 12, while the median scores also improved from 3 to8, showcasing the participants' improved performance.
Table 2
Speaking Abilities Pre-Test and Post-Test Results
Test
Pre-Test
Post-Test

n
25
25

Mean
3.28
7.12

SD
1.55
1.47

Minimum
3.00
8.00

Maximum
7.00
12.00

A paired t-test confirmed the statistical significance of these improvements, with a t-value of
13.11 and p<.05. Hence, the progress in speaking abilities can be confidently attributed to theeducational intervention.
General Implications and Limitations
The findings from this study offer valuable insights for language instructors aiming to bolster
speaking proficiency. The successful intervention led to a demonstrable improvement in
students' speaking skills, as shown by both descriptive and inferential statistical analyses.
However, it's important to recognize the limitations of this research. Conducted with arelatively small cohort, the study focused narrowly on a single aspect of language learning.
Future research should consider larger sample sizes and encompass additional language skills
to build upon these findings.
Despite these limitations, the investigation highlights the potential efficacy of targetedlanguage-improvement interventions, with a particular emphasis on the role of descriptive andinferential statistics in gauging their success.
Table 3 shows 25 students' improved speaking abilities, with normalized scores rising from0.33 to 0.70. The intervention improved participants' performance more uniformly, lowering
SD from 0.18 pre-test to 0.16 post-test. Adding AI-powered speech recognition to thecurriculum improved academic performance. This technology, along with personalized
instruction and immediate feedback, improves pronunciation and oral communication. The
study confirms previous research on technology-assisted language learning. However, thesmall sample size and participant homogeneity may limit the results' applicability.
Table 3
Speaking Abilities Pre-Test and Post-Test Results
Test
Pre-Test
Post-Test

n
25
25

Mean
0.33
0.70

SD
0.18
0.16

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Finally, using AI in language learning improves pronunciation and speaking. Language
teachers can provide more engaging, individualized learning experiences that improve
language acquisition by incorporating such innovations.
Results from the Survey Questionnaire
The survey results revealed a unanimous agreement that the program exercises significantly
enhanced the participants’ pronunciation, speaking abilities, and vocabulary acquisition.
Specifically, 22 (88%) students strongly believed in the program's effectiveness in improving
pronunciation, while the majority confirmed its benefits for speaking skills. This aligns withthe findings reported by Shao and colleagues (2018). Shao and colleagues (2018) and Lee andJang (2018), reported similar findings regarding speech recognition systems. All participants
acknowledged the program's role in learning new words, which echoes Kuo and colleagues’(2017) findings on vocabulary development through speech recognition technology.
Furthermore, the program was found to be engaging and attention-grabbing. Twenty-three(92%) of participants reported sustained focus during exercises. Motivation to practice English
was reported by 15 (60%) participants, supporting Liu and Yang (2019)'s discovery of speech
recognition's motivational impact. An overwhelming majority concurred that the program wasinstrumental in refining their pronunciation and bolstering their confidence in English
speaking, a sentiment found by Wang and Hisao (2018).
Every participant strongly agreed on the program's efficacy in correcting pronunciation errors,
reinforcing Zhang and colleagues’ (2018) study outcomes. The program also proved helpful inamending speaking errors, as agreed by 18 (72%) participants, mirroring Lee and Jang (2018)'s
findings. The exercises' variety and the flexibility of practicing English anywhere were highly
praised, aligning with mobile and online language learning benefits highlighted by KukulskaHulme and Shield (2008). Additionally, the program's ease of use and enjoyment factor wereemphasized, which are crucial for user satisfaction and effective learning as noted by SanzTorrent and colleagues (2018) and Jang and Wu (2018).
In summary, the Speechling program, facilitated by AI-powered speech recognition technology(AI-SRT), was unanimously recognized for its positive impact on language skills, with a strong
endorsement from the participants for its pronunciation, speaking, and vocabulary exercises,
motivational appeal, error correction capabilities, and enjoyable, accessible learning
experience.
Quantitative Results
Participants were asked what they liked best about the Speechling program. The program
received positive feedback from students for its effective approach to language learning. Key
aspects that students valued the most included the program's flexibility, allowing for practiceat any time and place, and its user-friendly interface, which enhanced the learning experience.
The inclusion of a rich vocabulary and the ability to receive immediate, precise feedback on

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pronunciation were highlighted as significant benefits, contributing to improved confidence
and speaking abilities. The program's structure, which facilitates tracking progress, alongsidea variety of accents for listening practice, was appreciated for helping students become
accustomed to different English speakers and overcome language apprehension.
Moreover, the program's adaptive difficulty levels enabled personalized progression, and theengaging speaking practice questions kept learning enjoyable. Its high-precision speech
recognition technology, which closely resembles human speech, added authenticity to thelearning process. The program also offered a vast collection of audio recordings and realistic
speaking scenarios, making it both comprehensive and practical. Affordability and easy accesswere mentioned as additional advantages for students who are mindful of their budgets.
In summary, the study highlighted the Speechling program’s effectiveness in enhancing
English pronunciation and speaking skills, supported by current research on the positive
impacts of AI-assisted language programs. The program's varied features—flexible practicesettings, diverse vocabulary, adaptive levels, and authentic speech recognition—contributed toa dynamic and effective learning journey. The study’s qualitative findings suggests thattechnology-assisted pronunciation and speaking training should be an integral part of languagelearning curricula, offering a tailored and enriched experience for learners. Further
investigation is encouraged to explore the long-term benefits, best instructional practices, andintegration strategies for such programs in diverse learning environments.
Discussion and Conclusion
The application of AI-powered speech recognition technology in enhancing English
pronunciation and speaking skills among EFL students has promising outcomes. This study,
underpinned by the interactionist approach to Second Language Acquisition, highlighted thesignificant role of meaningful interactions and targeted practice facilitated by AI-SRT. The
instant feedback and the opportunity for repetitive, focused oral practice have proven to beeffective in improving pronunciation and oral fluency, resonated with findings by Liu and Yang
(2019) and Chen and Wang (2017). The technology's role as a virtual language instructor
offering personalized feedback suggested its potential to substantially improve soundproduction and speaking skills.
EFL students' English pronunciation and speaking skills have improved with AI-SRT. This
study, based on the interactionist approach to Second Language Acquisition emphasized AISRT-facilitated meaningful interactions and targeted practice. Liu and Yang (2019) and Chen
and Wang (2017) found that instant feedback and repetitive, focused oral practice improved
pronunciation and fluency. The technology's role as a virtual language instructor providing
personalized feedback suggested it could greatly improve sound production and speaking.
Learner variables like motivation, attitudes toward technology, and prior language proficiency
affected engagement and perceptions of the technology's impact. This is consistent with thework of Kim and Lee (2019) and Sung (2018), that reported learner motivation and positive120

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attitudes toward technology improve language learning outcomes. Wang and Sun (2020) and
Gao and colleagues (2019) agreed that AI-SRT's authentic conversational practice andimmediate feedback support interactionist SLA theory and the importance of meaningfulpractice. Wu and colleagues (2018) and Zhang and team (2022) recommended seamless andpurposeful AI-SRT integration into language learning environments that aligns with
instructional design and pedagogical goals. The study recommended including speech
recognition technology in the curriculum to aid language development, provided that teachersand students receive proper training and support, as noted by Lee and Jang (2018) and Zhang
and colleagues (2022).
The current study supports AI-SRT's efficacy, but practical considerations for its integration
into language curricula must be critically examined. To keep up with learners' changing needs
and language acquisition, technology must be assessed regularly. Technology's potential as a
crutch rather than a tool for learning raises the question of how to balance it with other teaching
methods to promote independent language development. The accessibility and inclusivity of
AI-SRT must also be assessed. The technology allows anytime, anywhere practice, but
socioeconomic factors and student digital literacy may limit access. Additionally, AI-SRT's
ability to accommodate different learning preferences and disabilities has not been extensively
explored. Furthermore, the study's findings suggest that while immediate feedback isbeneficial, it is the quality and specificity of the feedback that may determine the extent of
improvement in pronunciation and speaking skills. Therefore, ongoing refinement of speech
recognition algorithms to provide nuanced feedback is crucial for the sustained effectivenessof AI-SRT.
Finally, AI-SRT may continue improving language learning, but more nuanced, longitudinalstudies are needed to determine its long-term effects and optimal integration strategies fordiverse learning contexts. This includes studying how it affects students of different proficiency
levels, learning styles, and cultures. In conclusion, the Speechling program and other AI-SRT
tools offer innovative language learning methods, but their practical application requires
careful consideration of pedagogical, technological, and learner-related factors to ensure thatthey complement traditional language instruction and serve a wide range of learners.
Conclusion
An AI-powered Speech Recognition Technology was tested to improve EFL students' English
pronunciation and speaking. The study yielded important findings.
Drawing on the interactionist approach to second language acquisition, AI-powered speech
recognition technology aided meaningful interactions and targeted practice. Students could
receive instant feedback and practice pronunciation and oral fluency with the technology.
Technology integration in language learning was also shown to be important. Students were
supported and engaged by speech recognition technology powered by artificial intelligence.
The technology provided personalized feedback and instruction as a virtual language instructor,
improving students' sound production and speaking skills.

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The study also considered how learner variables affect intervention efficacy. Learners'
engagement with AI-powered speech recognition technology and perceptions of its impact on
language learning were influenced by motivation, attitudes toward technology, and priorlanguage proficiency. The results of this study contribute to the existing literature on AIassisted language learning and shed light on the potential benefits of AI-powered speech
recognition technology for enhancing pronunciation and speaking abilities. When designingeffective language learning interventions, the study highlights the importance of considering
the dynamic relationship between language acquisition theories, technology integration, andlearner factors.
This study supports the use of speech recognition technology powered by artificial intelligence
as a valuable tool for improving EFL students' English pronunciation and speaking abilities.
The findings indicate that integrating technology into language learning environments canenhance the learning experience and result in more effective language acquisition. However,additional research is needed to investigate the long-term effects of these interventions and toaddress potential challenges associated with the use of technology in language education.
Limitation of the Study
This research study provided valuable insights, but its limitations should be considered infuture studies. First, the study only included EFL students and may not apply to learners ofother languages. AI-powered speech recognition may not work for all EFL learners due to age,
language proficiency, and culture. Second, the small sample limited generalizability. A larger,
more diverse sample could explain how AI-powered speech recognition affects pronunciationand speaking. Another drawback is self-reporting and subjective evaluations. Pre-tests, posttests, and surveys are useful, but more objective measures are needed to confirm the findings.
Expert evaluations and objective pronunciation tests would strengthen the study's findings.
The study also did not examine the long-term effects of AI-driven speech recognition on
language learning. The technology's durability can be better understood through longitudinal
student studies. The study did not address technological barriers to implementing AI-powered
speech recognition in language learning environments. To ensure technology applicability andefficacy, future research should focus on system precision, user interface design, andaccessibility.
This study illuminates how artificial intelligence-powered speech recognition technology may
improve English pronunciation and speaking. To better understand how technology affects
language learning, future research should address these limitations and expand on the findings.
Suggestions for Further Studies
This research study has a number of recommendations for future research that can be made inorder to broaden and deepen our understanding of this field. These recommendations are basedon the findings and limitations of the research study.

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The study of AI-powered speech recognition technology in language learning includes multiple
narrative threads with different perspectives on its effectiveness and use. Foundational
longitudinal studies track student progress over time. These studies examine the long-term
effects of AI-powered speech recognition technology on pronunciation, speaking, and language
proficiency. By studying this perspective, researchers can better understand how thistechnology affects language learning over time and describe its benefits.
Comparative research seeks understanding. The user compares AI-powered speech recognition
technology to other language learning methods to determine its benefits. Teachers learn how to
use this technology most effectively by analyzing different teaching methods and intervention
strategies. In complex studies, learner factor shifts are important. Researchers examine howage, language proficiency, motivation, and AI-powered speech recognition technology affectlearner performance. This study illuminates the complex interaction between individual
variations and technological intervention, resulting in customized and flexible learning
environments for a variety of learners.
The study emphasizes technological optimization. AI-powered speech recognition technology
is researched to improve its design and functionality. By assessing system clarity, user interface
layout, and user input, they strategically plan to improve the technology's usability andeffectiveness to meet learners' needs. Language acquisition is fascinating and varied whenstudied across cultures. Researchers test AI-driven speech recognition technology in different
cultures and languages. This study examines how subtle cultural influences affect technology
use and effectiveness by comparing the experiences and results of EFL students from diverse
cultural backgrounds. This research helps us understand multicultural technology use.
These recommendations can help researchers better understand how AI-powered Speech
Recognition Technology can improve EFL learners' English pronunciation and speaking skills.

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References
Bohn, O.-S., & Munro, M. J. (2020). Second language speech learning: Theory, findings, andproblems. In T. S. Cardoso & J. N. Y. Levis (Eds.), The Routledge Handbook ofSecond Language Acquisition and Phonology (pp. 69–85). Routledge.
Chen, C.-M., & Wang, H.-W. (2017). A review of using speech technology in English
pronunciation training. Journal of Educational Technology & Society, 20(2), 228–238.
Chen, G., & Duan, J. (2020). Artificial intelligence in language learning: A review of current
applications and future potential. Computer Assisted Language Learning, 33(3), 257–
274.
Du, J., & Daniel, B. K. (2024). Transforming language education: A systematic review of AIpowered chatbots for English as a foreign language speaking practice. Computers andEducation: Artificial Intelligence, 6, 100230.
https://doi.org/10.1016/j.caeai.2024.100230
Fan, Y., Huang, X., & Tan, S. (2019). The effectiveness of artificial intelligence technology inimproving Chinese EFL learners’ oral English proficiency. Computer Assisted
Language Learning, 32(5–6), 495–514.
Gao, Y., Yang, J., & Zhang, X. (2019). The role of learner engagement in computer-assisted
pronunciation training: A study with EFL learners in China. Computer Assisted
Language Learning, 32(5-6), 507–529.
García-Sánchez, S., Soler-Urzúa, M., & Nussbaum, M. (2020). Effectiveness of an AIassisted program to enhance English pronunciation in EFL university students.
Computer Assisted Language Learning, 33(5–6), 568–590.
Jang, H., & Wu, Y. (2018). The impact of mobile game-based English learning on EFL
students' motivation and English proficiency. Journal of Educational Technology &Society, 21(1), 1–13.
Jia, J., Wang, H., Yang, D., & Zhang, X. (2019). How effective is a computer-assisted
pronunciation training (CAPT) program? A case study in China. Computer Assisted
Language Learning, 32(5–6), 560–578.
Jia, R., & Zhang, C. (2018). The effectiveness of computer-assisted pronunciation training for
speakers of different languages. Educational Technology & Society, 21(3), 261–274.
Kang, O. (2016). The importance of pronunciation in English communication. International
Journal of Language and Linguistics, 3(1), 45–50.
Kim, J. (2019). The effects of artificial intelligence-based personalized pronunciation
instruction on the English pronunciation skills of Korean EFL learners. English
Language Teaching, 12(6), 32–51.
Kim, S., & Lee, J. (2019). The role of learner motivation and attitude in an intelligent
computer-assisted language learning environment. Computer Assisted Language
Learning, 32(5–6), 457–476.
Krashen, S. D. (1981). Second language acquisition and second language learning.
Pergamon Press.
Kukulska-Hulme, A., & Shield, L. (2008). An overview of mobile assisted language learning:
From content delivery to supported collaboration and interaction. ReCALL, 20(3),271–289. https://doi.org/10.1017/S0958344008000335

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Kuo, C. H., Yang, Y. T. C., Chen, I. J., & Wang, T. I. (2017). Effects of using speech
recognition-assisted multimedia pronunciation learning system on English
pronunciation and vocabulary learning. Journal of Educational Technology & Society,20(1), 243–256.
Lee, C., & Jang, J. (2018). Effect of mobile pronunciation technology on English
pronunciation practice. Journal of Educational Technology & Society, 21(2), 187–
198.
Li, Y., & Li, C. (2021). Using artificial intelligence to support language learning: A
systematic review. Journal of Educational Computing Research, 59(5), 1295–1326.
Liao, X., & Xue, J. (2019). Computer-assisted pronunciation training (CAPT) on improving
vowel production of Chinese EFL learners. Computer Assisted Language Learning,
32(7), 607–629.
Liao, Y. K., & Xue, S. (2019). Investigating the effects of computer-assisted pronunciation
training (CAPT) on English vowel production of Chinese EFL learners. Computer
Assisted Language Learning, 32(5–6), 579–604.
Liu, W. C., & Yang, C. F. (2019). Mobile-assisted speaking practice for EFL college
students. Journal of Educational Technology & Society, 22(3), 203–217.
Major, R. C. (2016). Teaching pronunciation: The reference complement. Routledge.
Mohammadkarimi, E. (2024). Exploring the use of artificial intelligence in promoting
English language pronunciation skills. LLT Journal: A Journal on Language andLanguage Learning, 27(1), 98–115. Sanata Dharma University.
https://doi.org/10.24071/llt.v27i1.8151
Morris, R. L. (2020). AI meets CAPT: Artificial intelligence-supported computer-assisted
pronunciation training. Computer Assisted Language Learning, 33(3–4), 349–377.
Munro, M. J., & Derwing, T. M. (2016). Pronunciation teaching and learning: Effects of
explicit phonetic instruction in the L2 classroom. Canadian Modern Language
Review, 72(2), 166–189.
Persulessy, S. I., Nikijuluw, R. P. J. V., & Jakob, J. C. (2024). Utilizing artificial intelligencein language learning: What about engineering students’ perception? Journal of
English Education Program, 5(1), 48–58. https://doi.org/10.26418/jeep.v5i1.71159
Saito, K., & Lyster, R. (2019). Effects of form-focused instruction and corrective feedback on
L2 pronunciation development of /ɹ/ by Japanese learners of English. Applied
Linguistics, 40(2), 244–268.
Sanz-Torrent, M., Moreno-Sandoval, A., & Blanco-Suarez, C. (2018). Design and validationof an innovative mobile application to promote language learning. Educational
Technology Research and Development, 66(6), 1395–1411.
Shao, L., Gao, Y., Wang, L., & Li, Y. (2018). Effects of an online speech recognition systemon learners' English speaking performance. Educational Technology Research andDevelopment, 66(2), 427–445.
Sung, Y. T. (2018). Using a mobile app-based video reflection activity to enhance student
motivation and achievement in speaking proficiency. Computer Assisted Language
Learning, 31(5-6), 490–510.
Thomson, R. I. (2018). Teaching and learning English pronunciation: Research and practice.
Routledge.

125

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Volume 12 – Issue 2 – 2024

Trofimovich, P., & Isaacs, W. (2019). Second language pronunciation. In J. W. Schwieter &A. Benati (Eds.), The Cambridge Handbook of Second Language Acquisition (pp.
349–369). Cambridge University Press.
Vygotsky, L. S. (1934). Thought and Language (E. Hanfmann & G. Vakar, Trans.). MIT
Press.
Wang, J., & Sun, Y. C. (2020). Effects of focused instruction and interactional feedback on
the improvement of L2 Chinese learners' Mandarin tones. Computer Assisted
Language Learning, 33(5-6), 466–490.
Wang, S., & Hisao, C. (2018). Effects of the use of an automatic speech recognition system
on Taiwanese EFL learners' oral proficiency. Computer Assisted Language Learning,31(1–2), 39–59.
Wu, W. H., Wu, Y. C. J., Chen, C. Y., Kao, H. Y., Lin, C. H., & Huang, S. H. (2018).
Review of trends from mobile learning studies: A meta-analysis. Computers &Education, 123, 119–135.
Yalcin, S., & Korkmazgil, S. (2021). The effectiveness of an AI-powered mobile applicationon the English pronunciation skills of EFL learners. International Journal of
Emerging Technologies in Learning (iJET), 16(12), 135–148.
Zhang, H., Song, Y., & Lu, X. (2018). Investigating the effect of using a mobile-assisted
pronunciation application on learners' pronunciation accuracy and fluency. Language
Learning & Technology, 22(3), 111–129.
Zhang, Y., & Wei, L. (2019). The relationship between speaking ability and speaking anxiety
in second language acquisition. The Asia-Pacific Education Researcher, 28(3–4),
181–192.
Zhang, Z., Li, Y., Wang, Y., & Cheng, G. (2022). The effectiveness of artificial intelligence
technology on foreign language learning: A meta-analysis. Educational Technology
Research and Development, 70(2), 713–741.
Zhao, Y., Chen, Y., & Ma, F. (2021). Effects of AI-assisted technology on second language
pronunciation accuracy, fluency, and anxiety: A meta-analysis. Computer Assisted
Language Learning, 34(7), 756–779.
Zou, D., Deng, Z., Li, C., & Chen, Q. (2021). Improving oral English skills with artificial
intelligence: The effectiveness of AI-powered speech recognition technology.
Educational Technology Research and Development, 69(4), 1919–1944.
Corresponding author: Nutprapha K. Dennis
Email: nootprapa@gmail.com

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Academic and Social Adjustment to Post-Pandemic Hybrid Learning:
A Phenomenological Study of Filipino First-Year Engineering Students’
Experiences
Maria Cristina M. Firmante
De La Salle University, Philippines

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Abstract
The effects of the COVID-19 pandemic caused immense changes, particularly in education.
There has been a shift from mostly face-to-face to increased remote learning, and students have
tried to adapt to this change. Hybrid classes are now implemented as students adjust to the newnormal of the post-pandemic era. While the transition for some can be smooth and challenging
for others, literature shows that adjustment comes with a wide range of experiences. Thus, thisaction research aims to understand better first-year engineering students' academic and social
adjustment in a hybrid setting. A phenomenological approach was employed, involving thirty(30) first-year engineering students who participated in online focus group discussions.
Transcripts from the focus group discussions were coded, categorized, and thematicallyanalyzed. Observations during the interview were analyzed, and students' written essays were
examined. Findings of the academic adjustments toward hybrid learning indicated that theonline modality is integral to student learning, making it flexible and inclusive. Combining
online and in-person classes on the same day can add to the challenge of transitioning.
Moreover, the social adjustment of first-year students focused on the lack of social interaction
and social support that can significantly impact their success. Implications for program
development were discussed.
Keywords: academic adjustment, engineering, first-year students, hybrid learning, lived
experiences, social adjustment

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Scholars started investigating the idea of college student adjustment in the 1960s (Jones, 1966),
while researchers established a college student adjustment model focusing on academic, social,personal-emotional adjustment, and institutional attachment (Baker & Siryk, 1989). Research
conducted globally suggests that adjustment to the multiple transitions from higher education
experiences plays a significant role in students' success (Jindal-Snape & Rienties, 2016). For
many students, starting their first year of college includes significant changes in their levels of
independence and responsibility. While going to university is an exciting experience, somestudents have expectations before leaving home. Some are eager to experience more freedom
and adventure, while others may discover that the experience falls short of their expectations(Al-Qaisy, 2010). They must learn to work in a new environment, live independently, and workwith new people (Van Viet, 2021) and experience stressors related to their academic and social
adjustments (McCabe, 2016; Terenzini et al., 1994; Van Viet, 2021).
However, when the COVID-19 pandemic appeared in early 2020, it caused an immense change
in everyone's lives. The unprecedented effects of the pandemic challenged universities andstudents. In particular, schools adapted and shifted to new education methods surprisingly well
(Bubb & Jones, 2020). The unexpected turn of events led by the outbreak of the COVID-19
pandemic forced educators, regardless of their stance on online education, familiarity, and levelof preparedness, to transition into a completely online system of education with hardly any
time to make this transition (Dhawan, 2020). The abrupt switch to fully online learning wasparticularly stressful for many professors and students who prefer in-person instruction. Withor without a global pandemic, there is a significant jump in the academic workload first-year
students experience going from high school to college. At the same time, socially, first-year
students may have left some of their previous social networks and must establish a new support
system, which was made more difficult due to restrictions during the COVID-19 pandemic(Ang et al., 2022). Hence, online education is frequently labeled as less desirable, offering alower-quality education than in-person, face-to-face instruction (Hodges et al., 2020).
The abrupt shift to online learning presents challenges for engineering students, as theireducation relies heavily on hands-on experiences in laboratory classes and direct interaction
with instructors and peers (Balta-Salvador et al., 2021). A significant portion of engineering
curricula is centered around applying theoretical knowledge in practical settings, particularly
in laboratory classes (Jacques et al., 2020). Despite these preferences and constraints, online
education has gradually become integral to higher education in engineering subfields such as
electrical and computer engineering, computer science, and information technology over the
past decade (Martinez et al., 2005). Notably, conducting laboratory classes presents a specificchallenge (Grodotzki et al., 2021). As highlighted by Park et al. (2020), the transition has beendifficult, especially in facilitating lab courses, which are essential for undergraduate
engineering programs and are a requirement for program accreditation by the Accreditation
Board for Engineering and Technology (ABET).
Within this context, the researcher of the present study, who is also a practicing school
counselor, hopes to provide information on the lived experiences of first-year engineering
students as they transition from face-to-face to hybrid classes. The study also hopes to129

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contribute to the existing literature on first-year engineering students' academic and social
adjustment experiences, particularly in a new learning modality. At present, there is no existing
program in the researcher's counseling office related to the transition to hybrid classes for firstyear engineering students. The result of the present study will provide the researcher withessential information in developing a program specific and unique to first-year engineering
students' needs.
Research Problem
The present study explored the academic and social adjustment experiences of first-yearengineering students as a basis for program development. It was the hope of the researcher,
who is also a practicing school counselor, to come up with a program specific and unique tothe needs of first-year engineering students.
Specifically, the study sought to address the question: What are the lived experiences of firstyear engineering students toward the hybrid learning in terms of: 1) academic adjustments and1.2 social adjustments?
Literature Review
Academic Adjustment
Academic adjustment is considered an individual ability to manage various academic
challenges. Baker and Siryk (1989) defined academic adjustment as the ability of students toadequately cope with the educational demands of the college experience. Students who succeedin academic adjustment will show success in academic lifestyle, academic achievement, andacademic motivation (Anderson et al., 2016). Academic adjustment is how students overcome
various academic challenges to attain better grades in the university (Clinciu & Cazan,2013). However, the adverse effects of academic non-adjustment of students may result inearly dropout, difficulty handling stress, less drive to study, and low academic performance(Reddy et al., 2018). Failure to adjust academically to first-year students could also lead tostress, anxiety, moodiness, depression, and even poor mental health (Clinciu, 2012).
Several factors influence students' academic adjustments. A study by Montgomery andcolleagues (2019) found that intrapersonal factors significantly influence associations withacademic adjustment even when controlling for academic motivation. Students' academic
motivation mattered, as did their orientation. External factors appear unaffected by academic
adjustment, but intrinsic motivation was linked with higher adjustment. At the same time,
perfectionism was shown to have a superlative and discriminative association with academic
adjustment, depending on whether it was an adaptive or maladaptive form of perfectionism. In
the Philippines, Alipio (2020) examined whether the senior high school (SHS) students’specific area of specialization mattered for Filipino first-year college students' academic
adjustment and performance. Results of the study found that a significant difference existed
among the students in different SHS strands, suggesting that the academic adjustment and130

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performance of the students varied based on the SHS strand taken. Moreover, the researcher
recommended that guidance counselors in higher-education institutions create guidance
programs specifically designed for first-year college students to facilitate their transition andadjustment to college.
Social Adjustment
Transitioning to university entails integration into a new social environment. Students get toknow and spend time with fellow students and participate in social activities organized by
student organizations (Stephens et al., 2015). Like academic adjustment, social adjustment is
as vital as any other dimension in students' transitions (Enochs & Rolland, 2006; Gerdes &Mallinckrodt, 1994). Social adjustment refers to how students maintain a sense of belonging
in their respective colleges and connection with other members by fostering positive
interpersonal relationships and participating in diverse activities in school (Ko & Lee, 2013).
The National Academies of Sciences, Engineering, and Medicine (2017) posited that students'
feelings of belonging is a possible lever to support success, engagement, and well-being incollege. In addition, Sahin and colleagues (2016) found that students who cannot establish
positive relationships with friends, teachers, and school administrators do not like the school,
and have a higher tendency to be absent from school and to drop out. According to Shim andRyan (2019), when a student's goal is social development, college adjustment and social
competence were higher.
Hybrid and Blended Learning Approach
Dorn and colleagues (2020) posited that the academic year 2020–2021 was one of the mostchallenging times for faculty, students, and administrators. K-12 schools, universities, andother higher education institutions rapidly transitioned to online learning to continue education
during the COVID-19 pandemic (Singh et al., 2021). Some studies have reported that whiletransitioning to online teaching, teachers faced issues related to their classroom pedagogicalpractices; some even doubted their teaching abilities and questioned the learning outcomes ofstudents (Ulla & Perales, 2021a; Maatuk et al., 2022), while students encountered challenges
like lack of access to academic resources and distractions in the home learning environment(Clabaugh et al., 2021). In addition, students may have had experienced negative emotions dueto the hurried switch to an unfamiliar learning situation (Park et al., 2020).
Despite the challenges that arose in education due to the COVID-19 pandemic, the transition
to online learning caught the interest of some education scholars, practitioners, and researchersinterested in innovating within the traditional face-to-face learning practice (Ulla & Perales,2022). Online learning started gaining popularity in the 1990s when students could complete
their coursework without coming to campus and being physically present (Nortvig et al., 2018).
Previous evidence suggested hybrid or blended learning offers creative options to academic
leaders and faculty, making information available to students outside the classroom. Hybrid or131

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blended learning can help in optimizing and maximizing students’ productivity during inperson sessions (Powell et al., 2015).
In engineering education, online education has become a viable component of higher educationin engineering subfields like electrical and computer engineering, computer science, andinformation technology, particularly at the master’s or postgraduate level (Martinez et al.,2005). However, online, hybrid or blended learning programs can be successful or unsuccessful
if not appropriately planned and executed in terms of their structure, objectives, learning
outcomes, taxonomy, and specific to students’ needs (McGee & Reis, 2012). One study by
Alfiras and colleagues (2021) explored students’ perceptions of hybrid classes at the Collegeof Interior Design Engineering, Gulf University. The study results identified that hybrid classes
are practical, and to make them even better, specified parameters such as teachers’ efficiency,
the importance of advanced learning management system and communication over the internet
should be regularized and enhanced. The study also reiterated that the emotional connection ateacher establishes, and the constructive feedback that a teacher provides, make studying morepositive and welcoming for students.
Conceptual Framework
The current study’s conceptual framework provides a proposition for understanding the lived
experiences of first-year engineering students as they transition from face-to-face to hybridclasses. This framework is guided by Tinto’s (1975) attrition model, which explores various
educational experiences, competencies, and skills of students. The model also considers
students’ values and family and community backgrounds before they enter higher education.
As a result, academic and social adaptation are shown to influence students’ integration intohigher education, increase school cohesion, and lead to student perseverance and graduation.
Tinto emphasized the importance of academic and social integration, arguing that it is essential
for sustaining institutional and goal commitments and, consequently, college retention. He
contends that in order to succeed in college, students must adequately integrate into both theacademic and social facets of college life.
Figure 1
Academic and Social Adjustment Experiences of First-year Engineering Students

Lived ExperiencesofFirst-year
Engineering College
Students

Academic
Adjustment

SocialAdjustment

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Figure 1 shows the conceptual framework of the present study. The framework was summed
up based on previous literature. Guided by this framework, the lived experiences of first-yearengineering students were explored in terms of their academic and social adjustments. Results
will be the basis for program development.
Methodology
Research Design
The method used in the present study is action research employing a phenomenologicalqualitative approach. Mills (2000) posited that action research is any systematic investigation
carried out by teacher researchers, principals, school counselors, or other individuals in alearning environment to know more about how specific schools operate, how teachers teach,and how well students learn. Employing a phenomenological qualitative approach to action
research was selected for this study because the aims was to explore the experiences of firstyear students in depth. In a qualitative approach, researchers aim to comprehend theparticipants' unique world by listening carefully and attempting to capture the significance theyplace on their lives and experiences (Ponterotto, 2005).
Research Participants
The present study's participants comprised thirty (30) first-year engineering students enrolled
in hybrid classes. Since this paper utilized the action research design, the participants who
experienced the action to be investigated (i.e., the transition to hybrid classes) were chosen andwere composed of five (5) participants per academic program within the engineering
department. Specifically, the participants were selected purposively as they were the ones who
experienced the action under investigation. The programs included chemical engineering, civilengineering, electronics and computer engineering, industrial and systems engineering,
manufacturing engineering and management, and mechanical engineering. Purposefulsampling seeks cases with as much in-depth information that can be investigated (Patton,2002). It is also interesting to note that the participants were the first batch experiencing thefull implementation of the hybrid class.
Table 1 shows the demographic profile of the participants. Their ages ranged between 17 years
old and 22 years old. Minor participants were required to complete consent and assent forms
before participating. Of the 30 participants, 16 (53.0%) were male, 11 (37.0%) were female,and 3 (10.0%) chose not to provide their gender.

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Table 1
Demographic Profile of First-Year Engineering Students
Variable
Age

Characteristics
17-18 years old
19-20 years old
21-22 years old

n
18
8
4

%
60.0%
27.0%
13.0%

GenderMale
FemalePrefer not to respond

16
11
3

53.0%
37.0%
10.0%

Major/Program Civil Engineering
Chemical Engineering
Electronics and Computer Engineering
Industrial Engineering
Manufacturing Engineering
Mechanical Engineering
Note. N=30

5
5
5
5
5
5

16.6%
16.6%
16.6%
16.6%
16.6%
16.6%

Research Instruments
Three research instruments were used in the present study: (1) the researcher-made focus group
discussion protocol, (2) the researcher’s observations during the interviews (field notes), and(3) students’ essays about their academic and social adjustment.
Focus Group Discussion Protocol
Three school counselors handling college students validated the researcher-made focus group(FGD) protocol. Focus groups are group interviews that communicate with study participants
to gather information. They enable interactive and in-depth exploration of respondents'
experiences, and the group process can help clarify each view that might not emerge from a
one-on-one interview that the present study's author believes best fits its purpose (Kitzinger,
1995). The focus group technique allows for spontaneous information exchange and theexpression of participants' knowledge and ideas. Participants can think aloud in a focus group
discussion and are free to say what they believe or think. Moreover, information obtained fromfocus group discussions can be deeper than from face-to-face interviews because of the social
interaction among the participants (Thomas et al., 1995).
Researcher’s Field Notes (Observations)Field notes were an additional data source that included detailed information on observations
before, during, and after the focus group interview. The researcher's field notes included thedate, time, and details of the observation period. The researcher established rapport and trust134

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Volume 12 – Issue 2 – 2024with the virtual research participants by delivering non-verbal cues and engaging themthroughout the interview. Direct quotations from the participants that were significant or
representative of common themes, including non-verbal cues, were written down to ensure
accurate capture of what was said and how it was communicated. Additionally, the researcher'sinterpretations of and reflections on the observations and gathered data were also documented.
Students’ Essay
The researcher initially reached out to a professor who teaches first-year engineering students
to discuss the research they were conducting. The use of the essay in the study was thoroughly
discussed with the professor. Upon the professor's confirmation of participation, informed
consent was obtained from the students. They were provided with detailed information and theobjectives of the research. The essay focused on students' academic and social adjustment in a
hybrid setting. The students were asked to write a one to two-page essay about their experiences
as first-year engineering students, focusing on their academic and social adjustment. This waspart of their class requirements and was done in collaboration with the professor. The professor
provided the criteria for the essay, which included focus and details, organization, choice ofwords, and sentence structure. All thirty (30) participants completed the essay.
Data Gathering Procedure
The researcher followed the three phases of action research: pre-implementation,
implementation, and post-implementation. In the pre-implementation phase, the researcher
obtained ethical approval from the research ethics office of the participating private university
in Manila, Philippines. Upon the approval, an invitation to participate in the study wasannounced to the engineering student council. The researcher sought assistance from thestudent council to share and disseminate the invitation letter to participate in research intended
for first-year engineering students per program through a focus group discussion. Along with
this, the researcher also connected with the professors handling first-year engineering students
for the essay part of data collection. The essay was part of the requirements of students in theclass. The researcher of the present study prepared semi-structured questions during the focus
group discussion that focused on the lived experiences of first-year engineering students'
academic and social adjustment in the hybrid setting. Similar questions were also asked of thestudents who answered the essay. Students were also provided with informed consent.
During the implementation phase, students who opted to participate in the study received email
instructions about the online focus group discussion and were informed that the meeting
platform was Zoom. They were instructed to use their university-assigned email addresses tojoin the online discussion. Participants consented to allowing the researcher to use the gathereddata for research and program development. Consent was obtained via Google Forms, which
reiterated that no identifiable information would be shared if the collected data was to be used
for research presentation or publication. The study conducted during the academic year 2023–
2024.

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Moreover, after acquiring the result of the focus group discussion, program development will
follow for the post-implementation phase.
Data Analysis
Thematic analysis was performed on the focus group, observation, and student essays data. The
analysis followed Braun and Clark's (2006) guidelines. First, the participants’ audio-recordedinterviews were transcribed verbatim. The researcher familiarized herself with the data by
carefully listening to the recorded interviews, reading the transcripts, going through the wholedata.. The second step was generating initial codes and categories by labeling data segments or
the gathered answers. The researcher listed all the codes, combined similar codes into broader
categories, and counted the number of respondents who responded the same. Third, theresearcher searched for themes and then reviewed them as the fourth step. The fifth step was
to define and name the themes accordingly, and the last step was generating the final report.
Furthermore, to increase the present study's accuracy and objectivity, two college professors,
licensed teachers completing their doctoral degrees, were invited to review the findings andprovide suggestions. Their input was incorporated into the final results. One of the professors
is a registered guidance counselor, and the other is a registered psychologist. Consensus coding
and a review of the themes and subthemes were conducted. Additionally, the researcher's
observations and textual descriptions from the students' essays were included to finalize thethemes and subthemes for each research question.
Trustworthiness of the Research
Various strategies were implemented to ensure the research's validity and reliability. The
researcher followed the four criteria Lincoln and Guba (1985) outlined to guarantee thetrustworthiness of qualitative investigations. These criteria are explained in detail below.
Credibility
Patton (2002) identified three critical elements of credibility in qualitative research: rigorous
methods, a credible researcher, and a belief in qualitative inquiry. Achieving rigor in research
can be ensured through various methods, such as extended engagement with participants,
researcher reflexivity, member checks, triangulation, and peer debriefing. All five were usedin the current study and are described below.
The researcher of the present study spent additional time with participants to validate theinformation gathered during interviews. The increased time built trust in the data. It allowed
both the researcher and participants to clear up any misunderstandings and add any informationthat may have been overlooked during the initial interview.
Reflexivity allows the researcher to detect ethical concerns throughout the research(Kleinsasser, 2000). Qualitative researchers understand that bias can play a part in their studies,but they need to acknowledge this bias early on. Reflexivity was utilized in this study by

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detailing the researcher's role and maintaining researcher field notes (observation) to capture
any thoughts and emotions that might influence the data throughout the study.
It is also crucial to check with members in order to increase the credibility of research, as statedby Lincoln and Guba (1985). Therefore, the researcher shared the present study's findings withthe participants and let them suggest changes if they wanted more from her interpretation.
Moreover, direct excerpts were given to participants to enable them to understand the findings.
The point of triangulation is to test for consistency between multiple sources of data eventhough it may not lead to a single answer (Patton, 2002). Triangulation was accomplished inthis study through the use of multiple sources of data collection, including interview transcripts,
essay, and the researcher field notes (observation).
Lastly, the researcher considered that some participants might feel distressed during or after
the study. Participants were informed that they could withdraw or discontinue the interview ifthey experienced any discomfort without impacting their relationship with the researcher. The
researcher provided a debriefing to the participants after the interview.
Transferability
According to Morrow (2005), transferability can be achieved through detailed information
about the researcher, context, processes, and participants. The researcher focused on
observations before, during, and after the focus group interviews that added richness to thedata. The participants selected for this study were chosen through a purposeful sample. The
specific type of purposeful sample was criterion sampling. Anfara and colleagues (2002)
mentioned using purposeful sampling and thick descriptions to help ensure transferability.
Dependability
The dependability of a qualitative study is achieved through consistency and by developing anaudit trail (Morrow, 2005). The audit trail for this study was documented carefully in the
researcher's field notes throughout the research. Triangulation was also used withdependability, as mentioned under credibility. Data are more dependable when gathered froma variety of sources.
Confirmability
Confirmability describes the ability of the researcher to bring all data gathered and to be able
to trust their findings. The overall findings of the study must reflect the actual information
learned. To have confirmable results, researchers must reduce bias in their results (Morrow,2005). This was accomplished in this study through triangulation, as mentioned previously, ofthree primary data sources. Combining the focus group interview data, field notes, and areflexive journal, the data was continually examined to be trustworthy and confirmable.

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Ethical Considerations
Since a qualitative study is an in-depth examination of the personal experiences of theparticipants, the following precautions were taken into consideration to protect the participant’s
rights: 1) acquiring approval from the research ethics office of the participating university, 2)communicating the purpose of the research to the participants in writing, as well as collecting
informed consent and assent forms from minors, 3) ensuring participant anonymity through theuse of pseudonyms, and 4) maintaining strict confidentiality of all personal information shared.
The researcher acknowledged that some participants might feel distressed during or after thefocus group discussion. Participants were told they could withdraw or discontinue at any time
if they felt uncomfortable without affecting their relationship with the researcher. After theinterview, the researcher provided a debriefing to the participants. Data privacy was ensured
through anonymization and aggregate presentation of results.
Results
Analysis of data revealed the lived experiences of first-year engineering students in terms of
their academic and social adjustments in a hybrid setting. The initial phase of thephenomenological analysis of the statements is found in the transcripts (student’s essay), which
offer details about the participants' actual experiences.
Table 2 presents significant statements gathered from first-year engineering students about
their academic and social adjustments toward hybrid learning. Participants expressed thathybrid classes were better rather than pure online classes during the pandemic. As seen in thetable above, one participant stated,“I could say that the hybrid classes are actually far better than having full online
classes like during the pandemic. And having full face to face classes since it makes
the students' time flexible and not having to bother going back and forth in the campus,
which increases efficiency”.
Most participants expressed that there were still pros and cons of hybrid classes. For example,
one participant shared,“I think one of the biggest disadvantages now…based on my personal experiences andwhat I hear from other froshies as well is that there are days where f2f classes are rightafter online classes. As a result, students like me who commute would still have to go
to school early despite the online class, even if their f2f classes are later on. Though
this may seem like a minor inconvenience, it can be really draining”.
Overall, students' essays about their academic and social adjustment stressed both theadvantages and disadvantages of the hybrid setting. Students were trying their best to adapt

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and take advantage of the opportunity hybrid classes were offering them while also recognizing
the challenges they had to deal with and work through.
Table 2
Participants Significant Statements in the Essay
Significant Statements in the Essay“Academic adjustment is still somehow above the average than last semester since goingback to face-to-face classes felt somewhat new after the pandemic resided”“I could say that the hybrid classes are actually far better than having full online like during
the pandemic. And having full face to face classes since it makes the students' time forflexible and not having to bother going back and forth in campus which increases
efficiency”“For me, the advantages of hybrid class are you wouldn't have to worry about spending a
lot of money for food or transportation since there will be times when you will stay athome”“Being able to schedule myself efficiently to do schoolwork’s and learning at a
comfortable pace.”“Most of the students still go to campus to attend only one class with face to face and therest being online. For me, I think this is a disadvantage since it contradicts the idea of
having the flexible time that both students and staffs.”“I think one of the biggest disadvantages now (based on my personal experiences and what
I hear from other froshies as well) is that there are days where f2f classes are right after
online classes. As a result, students like me who commute would still have to go to school
early despite the online class, even if their f2f classes are later on. Though this may seem
like a minor inconvenience, it can be really draining.”“I still haven't joined any organizations since I'm still trying to test the waters but as soon
as I become more adjusted”“I would say that my home org rarely has activities that would allow me to meet
upperclassmen to ask for advice and help and make friends, which is why I know so little
people in my course.”“Hopefully engineering department has more events that will make college life less"serious and technical" and enjoyable, as it is in these events where students will reallyremember throughout the years.”+25 other significant statements

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In Table 3, four main themes were generated from the focus group interviews of first-yearengineering students. Generally, participants expressed the idea that hybrid learning provides
a flexible learning environment in which there is less academic fatigue. They felt hybrid
learning saves both time and money. They also saw it as an opportunity to have a comfortablelearning pace, and it is convenient from any location. One participant, Harvey (BS Civil),stated,“I think the advantages of hybrid classes is that me not going to school every day. In aweek I go to school for at least 2-3 times. Then for the rest, I just stay at home for myonline classes. I really like this because not only does it saves me from commuting andneeding to go to school, it saves a lot of my energy. Being well rested allows me toeven be more attentive and participative. As a student, hybrid learning is the best typeof set up, since pure f2f classes can be draining and pure online classes isn't always
effective”.
Adaptability to technology was also identified. Participants expressed that in hybrid learning,they were becoming more tech-savvy and integrating synchronous and asynchronous learning.
Another participant, Ana (BS IE), described, “Our generation now are into technology andinternet, and I can say that the hybrid learning allows us to become even more tech-savvy.”
The other theme that was generated was embracing a new education setup. Participants
recognized that despite the many challenges that the pandemic brought, it opened up otheropportunities for students and the educational system as a whole. Participants in the present
study identified that the experience helped them adjust when faced with learning adversity.
Jana (BS Che) explained,“The hybrid class this time maximized the advantages of both worlds. As a student,
the experience during the pandemic gave a lot of insight also on how to survive -what
would be helpful and how to continue”.
Lastly, participants also pointed out that even though hybrid learning provided a flexible
learning experience, they also reiterated that class scheduling, for some, defeated the purpose.
It was stressed by James (BS ME) saying,“First of all, inconvenience, especially when there will be an urgent announcement that
all classes will be shifted online, then you are already at school without your resources(laptop in my case since I don't bring my laptop whenever I have face to face classes).
Second, it's very tiring as someone who is leaving far from the university.”
While Carlo (BS ECE) shared similar sentiments, stating,“Same with my lab classes, they did not put the online lab classes together, whichmeans I have to go to school on two separate days instead of one. But since this is140

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beyond my control and is now past the half of the term, I really have to work on myschedule.”
The lived experiences of first-year engineering students' academic adjustments toward hybrid
learning showed that the online modality was integral to student learning, making it flexible
and inclusive. However, combining online and in-person classes on the same day can add tothe challenge of transitioning. Solving these challenges can help first-year engineering students
achieve a smooth academic transition.
Table 3
Themes and Sub-Themes of First-Year Engineering Students’ Academic Adjustments Toward
Hybrid Learning
Theme
Flexible Learning

Sub-theme
Less academic fatigue
Saving both time and money
Opportunity to have a comfortable learning pace
Convenient from any location

Adaptability inTechnology

Becoming tech-savvy
Integration of synchronous and asynchronous learning

Embracing New
Education Setup

Able to adjust when faced with learning adversity
Accepting changes and moving forward

Class Schedule

Difficulty having face-to-face and online class in a day

Table 4 presents the themes and sub-themes of first-year engineering students’ social
adjustment toward hybrid learning. There were two main themes and sub-themes. Participants
identified both the advantages and disadvantages of hybrid learning in their social adjustment
but highlighted less interaction and support. Chino (BS MEM) mentioned,“I think the main advantage would be having more social interaction among otherstudents in the campus. Even though through online setting, there are still ways tosocially interact since we have all adjusted from online setting during the pandemic.
Although a disadvantage would be having less physical interaction and also having lesstime going out with peers inside and outside of campus and since we are froshies, mostof us are shy and adjusting.”
Similarly, another participant, Rafael (BS CIV), expressed,“It is difficult to make friends and build relationships with others since there are times
when we won't be able to meet or see each other…only face to face twice a week. It

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will also be hard to adjust and adapt to the new environment if it is not always on aconsistent basis.”
Participants also identified that when it comes to social support, it would be best if they havesomeone with them, particularly since they are starting to adapt to the new environment as
first-year students. It was expressed and reiterated by Joy (BS IE), who stated,“I would say that my home organization rarely has activities that would allow me tomeet upperclassmen to ask for advice and help and make friends, which is why I know
so little people in my course.”
The social adjustment identified by first-year engineering students focused on the lack of social
interaction and social support. Social experiences play a crucial role in well-being in general,
but especially for first-year college students entering an entirely new environment where theymight not know many people. First-year engineering students pointed out that the social
adjustment process can greatly impact the success of transitioning.
Table 4
Themes and Sub-Themes of First-Year Engineering Students’ Social Adjustments Toward
Hybrid Learning
Theme
Interaction

Sub-theme
Less physical and social interaction
Limitations in meeting other batchmates

SupportLack of emotional connection
Minimal activities from home organizations
Discussion

The process of adjustment entails changing one's behavior to work more harmoniously in asituation that has changed. As such, the transition from high school to college marks a critical
period of adjustment for first-year students. At present, the trend to integrate face-to-face andonline learning in the post-pandemic era, is also part of the transition among first-year student.
Literatures acknowledge both the advantages and disadvantages of hybrid settings (Firmante,2022). Students can experience learning opportunities and challenges in both educational
modalities. Hybrid learning may lead to more independent learning and more opportunities foreveryone to pursue education at any time or location. In-person instruction allows students tocommunicate directly with their teachers and peers. At the same time, online time allows
students to interact with numerous resources and materials that could assist them inunderstanding particular ideas and improving their learning at their own pace (Brown, 2016).
However, these changes can be challenging at the same time. The results of the present study
confirm these documented opportunities and challenges.

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First-year engineering students have identified opportunities and challenges related to theiracademic and social adjustments. The four main themes regarding the academic adjustment of
first-year engineering students show that they have identified advantages and disadvantages ofhybrid learning. For academic adjustments, participants expressed flexibility, being adept withtechnology, and being able to adjust to adversity. Conversely, the remote learning environment
has also brought new educational opportunities (Hoss et al., 2022; Li et al., 2022). Students
can access various digital resources, including open educational resources, research databases,
and online libraries through hybrid learning. These materials can motivate students' creativity
by introducing them to fresh concepts, viewpoints, and data (Haleem et al., 2022). For instance,Azorín (2020) argued that the crisis provided scarce opportunities to trial, improve, and rethink
the role, content, and innovative education delivery methods. Despite this, it is also important
to note that participants stressed the difficulty of having both an online and face-to-faceschedule in a single day. The participants expressed that it would have been better if theircourse schedules alternated (one full online day and one complete face-to-face day) to avoid
academic fatigue. For study participants, addressing concerns about course scheduling could
be an adjustment that might be vital to their academic fulfillment.
Regarding social adjustment, participants expressed limitations, although they used social
media for communication and interaction. Participants pointed out that there were still
limitations regarding social interaction and social support. They expressed difficulty
connecting with their classmates, as well as their professors. Some participants mentioned there
was no connection at all. Most of them described a lack of support. This is important because
Tindle and colleagues (2022) argued that social support is essential for accepting andnavigating challenging circumstances. Social connections and the bonds that students havewith their teachers and fellow students are crucial to higher education because they give
students a feeling of community and enhance their online learning experience (Händel et al.
2020). As such, the difficulties in building social relationships online (Wilson et al., 2020) arevital areas that need to be addressed.
Moreover, the results of the present study are crucial in the post-implementation phase of thisaction research. Interventions should be available for students to address their academic,
personal, career, and even psychological needs exacerbated by the pandemic. Intensifying
counseling and other support services could be available at the university (Firmante, 2022). In
addition, guidance is a process that consists of services offered to students to help them acquire
the information and abilities required to make appropriate decisions, plans, and interpretations
necessary for a satisfactory transition in various contexts (Sindabi, 1991). As such, theguidance and counseling programs in higher education institutions address the needs ofstudents to enhance their adjustment to the immediate environmental challenges that affecttheir social growth and academic adjustment, especially in this post-pandemic time.
Participants highlighted the lack of social interaction and support in their college transition.
They said having more activities from their professional organizations could have helped. In
this way, there would be opportunities to meet, collaborate, and seek assistance from seniors
that might help them cope socially and even with their academic transition. Furthermore, thisresult will be the primary goal in program development for the post-implementation phase.

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Conclusions and Implications
Hybrid learning is an integration of both face-to-face and online education modalities. This
educational setup corresponds to the shift brought about by COVID-19 and continuously
evolving in the post-pandemic era. Megahed and Ghoneim (2022) claimed that combining
technology with in-person lectures creates settings that can improve students' capacity forlearning. Hence, the educational shifts and lessons learned during the pandemic are suitable forthe post-pandemic transition. Therefore, to provide meaningful and engaging transition
experiences to first-year engineering students, building appropriate supports should be
addressed.
Hence, this action research explored the experiences of first-year engineering students’
academic and social adjustment toward hybrid learning. The goal was to better understand their
specific needs and how they can be addressed in their transition. The primary goal was todevelop a program that will support the smooth transition of first-year engineering students tohybrid learning contexts. The findings of this study are crucial for the post-implementation
phase of this action research, especially in the counseling profession, where evidence-based
programs are vital.
Limitations of the Study
By design, this action research was meant to explore first-year engineering students' academic
and social adjustment as the basis for program development. It highlighted their experiences in
the hybrid education setting post-pandemic. However, this study is limited to a specific groupwithin a particular context, making it difficult to generalize the findings. The focus group was
also conducted purely online. Conducting the focus groups in a face-to-face context may haveyielded different results. Furthermore, the essay was a class requirement, which might haveinfluenced how candid and open students were in their responses.

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References
Alipio, M. (2020). Academic Adjustment and Performance among Filipino Freshmen College
Students in the Health Sciences: Does Senior High School Strand Matter?
https://doi.org/10.35542/osf.io/xq4pk
Alfiras, M., Nagi, M., Bojiah, J., & Sherwani, M. (2021). Students’ Perceptions of HybridClasses in the Context of Gulf University: An Analytical Study. Journal of Hunan
University Natural Sciences, 48(5).
Al-Qaisy, L. M. (2010). Adjustment of college freshmen: The importance of gender and theplace of residence. International Journal of Psychological Studies, 2(1), 142.
https://doi.org/10.5539/ijps.v2n1p142
Anderson, J. R., Guan, Y., & Koc, Y. (2016). The academic adjustment scale: Measuring theadjustment of permanent resident or sojourner students. International Journal ofIntercultural Relations, 54, 68–76. https://doi.org/10.1016/j.ijintrel.2016.07.006
Anfara, V. A., Brown, K. M., & Mangione, T. L. (2002). Qualitative analysis on stage:
Making the research process more public. Educational Researcher, 31(7), 28–38.
https://doi.org/10.3102/0013189X031007028
Ang, J. Y. Z., Monte, V., & Tsai, W. (2022). First-year college students’ adjustment during
the COVID-19 pandemic: The protective roles of hope and gratitude. Translational
Issues in Psychological Science. https://doi.org/10.1037/tps0000320
Azorín, C. (2020). Beyond COVID-19 supernova. Is another education coming?. Journal ofProfessional Capital and Community, 5(3/4), 381–390.
https://doi.org/10.1108/JPCC-05-2020-0019
Baker, R. W., & Siryk, B. (1989). Student adaptation to college questionnaire. In Program of
the Seventy-Fourth Annual Meeting.
Baltà-Salvador, R., Olmedo-Torre, N., Peña, M., & Renta-Davids, A. I. (2021). Academic
and emotional effects of online learning during the COVID-19 pandemic onengineering students. Education and Information Technologies, 1–28.
https://doi.org/10.1007/s10639-021-10593-1
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Researchin Psychology, 3(2), 77-77–101. https://doi.org/10.1191/1478088706qp063oa
Brown, M. G. (2016). Blended instructional practice: A review of the empirical literature on
instructors' adoption and use of online tools in face-to-face teaching. The Internet andHigher Education, 31, 1–10. https://doi.org/10.1016/j.iheduc.2016.05.001
Bubb, S., & Jones, M. A. (2020). Learning from the COVID-19 home-schooling experience:
Listening to pupils, parents/carers and teachers. Improving Schools, 23(3), 209–222.
https://doi.org/10.1177/1365480220958797
Clinciu, A.I. (2012). Adaptation and stress for the first year university students. ProcediaSocial and Behavioral Sciences, 78, 718–722.
https://doi.org/10.1016/j.sbspro.2013.04.382
Clinciu, A. I., & Cazan, A.M. (2013). Academic adjustment questionnaire for the university
students. Procedia-Social and Behavioral Sciences, 127, 655–660.
https://doi.org/10.1016/j.sbspro.2014.03.330

145

IAFOR Journal of Education: Technology in Education

Volume 12 – Issue 2 – 2024

Dhawan, S. (2020). Online learning: A panacea in the time of COVID-19 crisis. Journal of
Educational Technology Systems, 49(1), 5–22.
https://doi.org/10.1177/0047239520934018
Dorn, E., Hancock, B., Sarakatsannis, J., & Viruleg, E. (2020). COVID-19 and student
learning in the United States: The hurt could last a lifetime. McKinsey & Company, 1.
Enochs, W. K., & Roland, C. B. (2006). Social adjustment of college freshmen: The
importance of gender and living environment. College Student Journal, 40(1).
Grodotzki, J., Upadhya, S., & Tekkaya, A. E. (2021). Engineering education amid a global
pandemic. Advances in Industrial and Manufacturing Engineering, 3, 100058.
https://doi.org/10.1016/j.aime.2021.100058
Haleem, A., Javaid, M., Qadri, M. A., & Suman, R. (2022). Understanding the role of digitaltechnologies in education: A review. Sustainable Operations and Computers, 3, 275–
285. https://doi.org/10.1016/j.susoc.2022.05.004
Händel, M., Stephan, M., Gläser-Zikuda, M., Kopp, B., Bedenlier, S., & Ziegler, A. (2020).
Digital readiness and its effects on higher education students’ socio-emotional
perceptions in the context of the COVID-19 pandemic. Journal of Research on
Technology in Education, 54(2), 267–280.
https://doi.org/10.1080/15391523.2020.1846147
Hodges, C. B., Moore, S., Lockee, B. B., Trust, T., & Bond, M. A. (2020). The difference
between emergency remote teaching and online learning.
Hoss, T., Ancina, A., & Kaspar, K. (2022). German university students’ perspective on remotelearning during the COVID-19 pandemic: a quantitative survey study with implications
for future educational interventions. Frontiers in Psychology, 13, 734160.
https://doi.org/10.3389/fpsyg.2022.734160
Jacques, S., Ouahabi, A., & Lequeu, T. (2020). Remote Knowledge Acquisition andAssessment During the COVID-19 Pandemic. International Journal of Engineering
Pedagogy, 10(6), 120. https://doi.org/10.3991/ijep.v10i6.16205
Jindal-Snape, D., & Rienties, B. (2016). Understanding multiple and multi-dimensionaltransitions of international higher education students: setting the scene. In Multidimensional Transitions of International Students to Higher Education (pp. 21–38).
Routledge. https://doi.org/10.4324/9781315680200-8
Jones, R. L. (1966). The College Adjustment of Superior High School Students Who
Participated in a Program of Acceleration. Exceptional Children, 32(9).
https://doi.org/10.1177/001440296603200911
Li, J., Huang, X., Lei, X., Wen, J., & Lu, M. (2022). ICT literacy, resilience and online learning
self-efficacy between Chinese rural and urban primary school students. Frontiers in
Psychology, 13, 1051803. https://doi.org/10.3389/fpsyg.2022.1051803
Lincoln, YS., & Guba, E.G. (1985). Naturalistic inquiry. Newbury Park, CA: Sage.
https://doi.org/10.1016/0147-1767(85)90062-8
Kleinsasser, A. M. (2000). Researchers, reflexivity, and good data: Writing to unlearn.
Theory Into Practice, 39(3), 156–162. https://doi.org/10.1207/s15430421tip3903_6
Kitzinger, J. (1995). Qualitative research: introducing focus groups. BMJ, 311(7000), 299–
302. https://doi.org/10.1136/bmj.311.7000.299

146

IAFOR Journal of Education: Technology in Education

Volume 12 – Issue 2 – 2024

Ko, S. H., & Lee, J. Y. (2013). The influence of college students club activities in school life
resilience: Testing mediation model of social support. Welfare Counseling Edu
Research, 2(1), 35–51.
Martínez, P. J., Aguilar, F. J., & Ortiz, M. (2019). Transitioning from face-to-face to blended
and full online learning engineering master’s program. IEEE Transactions on
Education, 63(1), 2–9. https://doi.org/10.1109/TE.2019.2925320
McCabe, J. M. (2016). Connecting in college. In Connecting in College. University of
Chicago Press. https://doi.org/10.7208/chicago/9780226409665.001.0001
McGee, P., & Reis, A. (2012). Blended course design: A synthesis of best practices. Journalof Asynchronous Learning Networks, 16(4), 7–22.
https://doi.org/10.24059/olj.v16i4.239
Megahed, N., & Ghoneim, E. (2022). Blended learning: the new normal for post-Covid-19
pedagogy. International Journal of Mobile and Blended Learning (IJMBL), 14(1), 1–
15. https://doi.org/10.4018/IJMBL.291980
Mills, G. E. (2000). Action research: A guide for the teacher researcher. Prentice-Hall, Inc.,
One Lake Street, Upper Saddle River, New Jersey 07458.
Montgomery, S., Gregg, D. H., Somers, C. L., Pernice-Duca, F., Hoffman, A., & Beeghly, M.
(2019). Intrapersonal variables associated with academic adjustment in United Statescollege students. Current Psychology, 38(1), 40–49.
https://doi.org/10.1007/s12144-016-9533-0
Morrow, S. L. (2005). Quality and trustworthiness in qualitative research in counselingpsychology. Journal of Counseling Psychology, 52(2), 250–260.
https://doi.org/10.1037/0022-0167.52.2.250
National Academies of Sciences, Engineering, and Medicine. (2017). Supporting students’college success: The role of assessment of intrapersonal and interpersonal
competencies. Washington, DC: National Academies Press.
Nortvig, A. M., Petersen, A. K., & Balle, S. H. (2018). A Literature Review of the Factors
Influencing E-Learning and Blended Learning in Relation to Learning Outcome,Student Satisfaction and Engagement. Electronic Journal of E-learning, 16(1), 46–55.
Park, J. J., Park, M., Jackson, K., & Vanhoy, G. (2020). Remote Engineering Education
under COVID-19 Pandemic Environment. International Journal of Multidisciplinary
Perspectives in Higher Education, 5(1), 160–166.
https://doi.org/10.32674/jimphe.v5i1.2811
Patton, M. Q. (2002). Qualitative research and evaluation methods (3rd ed.). Thousand Oaks,
CA: Sage.
Ponterotto, J. G. (2005). Qualitative research in counseling psychology: A primer on researchparadigms and philosophy of science. Journal of Counseling Psychology, 52(2), 126.
https://doi.org/10.1037/0022-0167.52.2.126
Powell, A., Watson, J., Staley, P., Patrick, S., Horn, M., Fetzer, L., ... & Verma, S. (2015).
Blending Learning: The Evolution of Online and Face-to-Face Education from 20082015. Promising Practices in Blended and Online Learning Series. International
association for K-12 online learning.

147

IAFOR Journal of Education: Technology in Education

Volume 12 – Issue 2 – 2024

Reddy, K. J., Menon, K. R., & Thattil, A. (2018). Academic stress and its sources among
university students. Biomedical and Pharmacology Journal, 11(1), 531–537.
https://doi.org/10.13005/bpj/1404
Sahin, S., Arseven, Z., & Kiliç, A. (2016). Causes of Student Absenteeism and School
Dropouts. International Journal of Instruction, 9(1), 195–210.
https://doi.org/10.12973/iji.2016.9115a
Shim, S. S., & Ryan, A. M. (2019). Social achievement goals. In Social Goals in theClassroom (pp. 53–73). Routledge. https://doi.org/10.4324/9780429468452-4
Sindabi, A.M. (1992). An Analysis of The Guidance and Counselling Program in Selected
Kenyan Secondary Schools. Unpublished Ed. Dissertation, Virginia Polytechnic andState University, Falls Church Virginia.
Singh, J., Matthees, B., & Odetunde, A. (2021). Leaning online education during COVID-19
pandemic–attitudes and perceptions of non-traditional adult learners. Quality
Assurance in Education, 29(4), 408–421. https://doi.org/10.1108/QAE-12-2020-0147
Stephens, N. M., Brannon, T. N., Markus, H. R., & Nelson, J. E. (2015). Feeling at home incollege: Fortifying school‐relevant selves to reduce social class disparities in higher
education. Social Issues and Policy Review, 9(1), 1–24.
https://doi.org/10.1111/sipr.12008
Terenzini, P. T., Rendon, L. I., Lee Upcraft, M., Millar, S. B., Allison, K. W., Gregg, P. L., &Jalomo, R. (1994). The transition to college: Diverse students, diverse
stories. Research in Higher Education, 35(1), 57–73.
https://doi.org/10.1007/BF02496662
Tindle, R., Hemi, A., & Moustafa, A. A. (2022). Social support, psychological flexibility andcoping mediate the association between COVID-19 related stress exposure andpsychological distress. Scientific Reports, 12(1), 8688.
https://doi.org/10.1038/s41598-022-12262-w
Thomas, L., MacMillan, J., McColl, E., Hale, C., & Bond, S. (1995). Comparison of focus
group and individual interview methodology in examining patient satisfaction withnursing care. Social Sciences in Health, 1(4), 206–220.
Ulla, M. B., & Perales, W. F. (2022). Hybrid teaching: Conceptualization through practice forthe post COVID19 pandemic education. Front. Educ, 7, 924594.
https://doi.org/10.3389/feduc.2022.924594
Ulla, M. B., & Perales, W. F. (2021). Emergency Remote Teaching During COVID19: The
Role of Teachers' Online Community of Practice (CoP) in Times of Crisis. Journal ofInteractive Media in Education, 2021(1). https://doi.org/10.5334/jime.617
Van Viet, V. (2021). Student’s adjustment to university and its relation to gender, residence
and family factors. Kasetsart Journal of Social Sciences, 42(1), 81–88.
https://doi.org/10.34044/j.kjss.2021.42.1.13
Wilson, S., Tan, S., Knox, M., Ong, A., Crawford, J., & Rudolph, J. (2020). Enabling crosscultural student voice during COVID-19: A collective autoethnography. Journal of
University Teaching and Learning Practice, 17(5). https://doi.org/10.53761/1.17.5.3
Corresponding author: Maria Cristina Firmante
Email: maria.cristina.firmante@dlsu.edu.ph148

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The Influence of E-Comics on English Lexical Competence in Virtual
Higher Education
Norma Flores-González
Universidad Autónoma de Puebla, México
Vianey Castelán Flores
Universidad Autónoma de Puebla, México
Mónica Zamora Hernández
Universidad Autónoma de Puebla, México

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Abstract
The development of lexical competence in foreign languages is one of the skills that presents
difficulties in the teaching-learning process, as it requires stimulation and retention on the partof the student and creativity from the teacher. In this sense, digital resources emerge as aconducive means to promote new knowledge and consolidate acquired vocabulary. In thiscontext, the present research aimed to determine if digital comics influence the developmentof lexical competence in English in virtual environments at the higher education level.
Methodologically, an experimental design divided into three phases (pre-treatment, treatment,
and post-treatment) took place with a sample of 60 students during the autumn of 2023. The
results demonstrated an association between digital comics and lexical competencedevelopment variables, influencing lexicon acquisition, experiencing creativity, dynamism,
and language involvement. Besides, comics supported by Canva, Makebeliefscomix, andPixton applications contributed to students' cultural, linguistic, and communicative repertoire.
Concurrently, users' confidence increased through gradual and systematic recovery, use, andinventive writing activities. Supports such as images, dialogues, characters, and colors
encouraged the retrieval of words for subsequent use. In this way, the cognitive process ofrecall ceased to be merely memorising to transition to a level of long-term significant
comprehension. In conclusion, digital comics were plausible for encouraging practical,
flexible, and playful vocabulary improvement in a virtual environment.
Keywords: lexical competence, e-comics, virtual learning, applications, foreign language

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Learning a foreign language involves the development of various linguistic, cultural, and social
skills related to the language, as it is the means through which a speaker interacts and connectswith the outside world. Among the skills required for authentic communication is vocabularyacquisition because it allows access to a wide range of information and generates fluid
dialogue. In this context, the Faculty of Languages from the Benemérita Universidad
Autónoma de Puebla in the area of English aims for students to primarily develop
communicative competence, as one of the most frequent limitations is the reduced lexicalrepertoire for expression, which limits the possibilities of effective oral interaction. Besides,
after adding the virtual component, students' participation decreases due to a lack of word
knowledge, contextual understanding, and confidence to speak in public. In theseenvironments, the need to produce didactic resources and active environments leads toinnovative and meaningful activities for the participants. Thus, facing various educational
modalities, the aim is to address lexical weaknesses to benefit foreign language students.
From this context, the purpose of this paper is to identify whether the development of lexicalcompetence is influenced by the use of e-comics in virtual environments in higher
education. In foreign language didactics, the desire to innovate and address this educational
issue gives rise to the design, development, and use of various strategies and resources thatprovide the necessary stimulus for students to understand and appropriate new knowledge.
Therefore, digital resources emerge as a means to meet these teaching-learning needs of thetarget language. The use of digital comics with applications such as Canva, Makebeliefscomix,
and Pixton is proposed to enable gamified learning with a communicative approach, allowing
contact with the foreign language in an appealing, motivating, and flexible manner, generating
contextualized language practice for the user. In this sense, such tools promote activities witha communicative focus to appropriate the lexicon. Along with this dynamic resource, the comic
strip is incorporated as one of the educational supports with high stimulation and creativity.
Indeed, it represents one of the cultural elements of contemporary society and Generation Z.
Considering the organization of this research, the sections include 1) Theoretical Framework,where the main conceptual foundations are presented. 2) Methodology, which analyzes lexicalcompetence. 3) Results, which describe the evaluation of the proposed pedagogicalintervention with data. 4) Discussion, 5) Recommendations, derived from the study, 6)
Conclusions, and 7) References.
Theoretical Framework
Origins of lexical competence in the foreign language didactics, is found in the works oflinguists such as Chomsky with his semantic analysis of corpus (Barón et al., 2013), and Hymes
with his ethnographic studies of language, framing the lexicon from its context, usage, andintention (Roselló, 2021). However, it was not until the publication of Michael Lewis's bookin 1993, The Lexical Approach: The State of ELT and a Way Forward, that importance wasgiven to the study of the lexicon in foreign language didactics, as this work highlights it as an
essential component in language learning, especially in the development of communicativecompetence. Following Lewis's contribution, studies have focused on viewing the lexicon not
as a series or list of words learned randomly but rather as a competence, involving the speaker's

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knowledge and skills that make them competent to interact effectively in oral or written
communication (De la Maya & López-Pérez, 2021; Castillo et al., 2022; Marin-Marin &Hernández-Romero, 2022).
In more recent studies, the most researched topics are those that explain what linguisticcompetence is and how it develops, such as the works of Alejos (2017), Rodríguez-Montes(2020), and Pasuy & Agudelo (2022), which focus on analyzing the development of lexicalcompetence through linguistic, semantic, and sociolinguistic approaches to understand theimportance of word usage in authentic contexts. Other works, such as those by Onieva-Palomar(2019) and Robles (2018), highlight the importance of mastering the lexical factor as a didactic
strategy for effective foreign language learning. However, there are also more specific studies,such as those by Cáceres et al. (2018), Valdés-León (2021), and Zambrano & García (2022),
that explain how the effective development of lexical competence supports the enhancementof literacy skills. In the particular case of this research, the situation that prompted the study
deals with a very recurrent problem in the development of communicative competence.
The knowledge of the lexicon has three dimensions: form, meaning, and use (Sanhueza et al.,2018). In the Common European Framework of Reference (CEFR), the lexical component
associated in the first instance with linguistic competence links it to both pragmatic andsociolinguistic ones. It emphasizes the speaker's capacity in terms of two dimensions: richness
and mastery of vocabulary. Richness is the size of vocabulary acquired in the language learning
process through stimulus (input) and production (output) from daily interactions of each
communicative activity. Besides, the mastery of the lexicon refers to the assimilation andassociation processes presented at each of the syntactic and semantic levels that occur whenstructuring words to form messages (Consejo de Europa, 2020). For his part, Robles (2018)
mentions that the lexicon is an essential element for the mastery and linguistic development ofa language since it allows the effective development of comprehension and oral expression
skills.
Regarding its teaching, Torres & Aristu (2021) assert that beyond understanding cognitive
processes, word dimensions, and strategies for learning and usage, it is essential to consider
the speaker's identity, focusing specifically on the learner's areas of knowledge, interests, andpersonal relationships; thus, contextualized vocabulary learning is unavoidable. In his study,
Beghadid (2022) classifies the vocabulary: 1. Technical vocabulary that deals with a particulardisciplinary area, including its idioms and technical terms. 2. General vocabulary that consistsof words used to name everyday activities commonly known by everyone. 3. Potential
vocabulary is a set of words usually unknown to the student but whose meanings can beinferred or understood through their relationship with other known words or by context. 4.
Passive vocabulary that is known but not frequently used in routine interactions. 5. Active or
productive vocabulary is the lexicon a student constantly employs in their oral and written
productions.

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Strategies for Learning Vocabulary
Mastery of a language must adhere to both theoretical and practical aspects of vocabulary usage(Guerrero et al., 2022). For this reason, it is of utmost importance to have didactic strategies
that are cognitive and socio-affective, including memorization, organizational, andmotivational aspects (Gómez et al., 2021) to help learners acquire new vocabulary. Beghadid(2022) indicates that students often use three memorization skills to learn new vocabulary:visual, auditory, and kinesthetic. These strategies enhance the integration and recall of newwords and their association (Berthely et al., 2023). Moreover, organizational and motivational
strategies act as triggers for meaningful vocabulary learning. From a communicative-functional
approach, reading comprehension tasks and academic and creative writing are used, as thesecontextual references stimulate the need to acquire vocabulary to interact correctly in a realcommunication task (García et al., 2020).
However, when discussing vocabulary learning strategies, it is impossible to overlook theclassification by Levin & Pressley (cited in García-López, 2000) because most research
dedicated to identifying strategy types has its basis in their studies. According to these authors,
there are four groups of strategies: 1. Repetition Strategies are for students to repeat a list ofwords orally or in writing. 2. Sensory Strategies can be visual, tactile, or audiovisual, supported
by materials such as vocabulary cards, images, and videos, to mention a few. 3. Semantic
Strategies that focus on associating new vocabulary with previously learned words
and analyzing their relationships for word memorization. Known semantic strategies include
association, contextualization, imagery, and the morphosyntactic analysis of the word. 4.
Mnemonic Strategies refer to all the resources or techniques students use to memorize a word.
García et al. (2020) classify vocabulary-learning strategies into three types: 1. Cognitive
strategies. These are conscious activities to understand or learn vocabulary, such as repetition,reference materials, translation, grouping, note-taking, deduction, imagery, and keywords. 2.
Communicative or compensatory strategies. These encompass all resources or techniques that
aid in understanding the meaning of words, such as guessing, inference, association,
exemplification, and questioning, among others. 3. Memorization strategies. These activities
assist the student in remembering, understanding, and appropriating vocabulary, as well ascomprehending meanings and recognizing new concepts within discourse to store them in theirmental structure.
Technology-Mediated Vocabulary Learning
Technological advancements have opened a new way of interacting with knowledge, whereimages become a relevant element for knowledge transmission and communication within an
educational context governed by telecommunications (Hernández et al., 2020; Flores, 2023).
Furthermore, students are seen as digital natives who use mobile devices and digitalapplications for everything, meaning they see, know, and interact with their world through theweb. Therefore, in teaching, it is fundamental to implement these digital tools to facilitate
learning since cyberspace offers countless pedagogical and non-pedagogical resources inwhich any type of educational task can be developed significantly (Flores-González, 2020;

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Ccoa & Alvites, 2021). Furthermore, Vargas-Murillo (2020) presents a classification thatspecifies educational strategies developed with digital technological tools used in virtualenvironments to work on the cognitive part of lexicon learning, which are:•••Conceptual-graphic-organizer strategies. They include concept maps for information
retention and concept development. Mind maps for the construction and information
management, creativity for taking notes and planning thoughts, and the semantic
networks in which the meanings and interrelationships of words are for learning andenriching the lexicon. Some tools for this type of strategy are Draw.io, CmapTools,
Microsoft Visio, Lucidchart, MindMeister, XMind, Creately, GitMind, ATLAS ti, andInspiration.
Visual-representation strategy. For example, infographics and illustrations thatsynthesize and encode information through texts, images, diagrams, and symbols. The
information resources used for its design are Google Drawing, Piktochart, Canva
Genially, Prezi, RealWorld Paint, and GIMP.
Processing information strategies. They refer to three elements: (a) Interspersed
questions, focused on feedback, resolution of doubts, and self-assessment; (b)summary, used in abstraction, synthesis, and reformulation to identify key ideas andconcepts; and (c) narrative texts, whose function is the construction of mental andsituational models through textual narrative, as well as literary creativity. Some digital
tools for these strategies are Google Forms, Survery Monkey, Microsoft Forms,
Resoomer, Text Summary, Google Docs, iA Writer, Penultimate, Celtx,
Makebeliefscomix, and Pixton.

One of the disciplines that has most benefited from technology is foreign language didactics,
as technological resources have been present in methodologies developed over time. In thissense, Borromeo et al. (2018) mention this in their study on the use and evolution of technology
in foreign language teaching, stating that it was from the audio-lingual method onwards thattechnological resources and tools began to be exploited, even more so with the communicative
method or approach, where the development of both communicative and digital competencies
allows students to have experiences of ubiquitous, immersive, autonomous, and recreational
learning (Trujillo et al., 2019).
The E-Comic Strip
The e-comic strip is a student-centered activity defined as a textual technique within the
narrative genre, where stories are told through texts and images intentionally structured to
evoke an aesthetic response in readers upon reading. This characteristic of combining text withimages is one of the most outstanding features of comic strips, as it allows for the retention of
information conveyed in each part of the narrative in a more substantial way (Mosquera &Rendón, 2021). For some authors, the origin of comic strips dates back to prehistory, with cave
paintings, while others associate it with the invention of the printing press. However, it wasn't
until 1894 that the comic strip as we know it today was born. The first comic strip was
published in the New York World newspaper in 1876, featuring The Yellow Kid, where anarrative in panels depicting the life and actions of the characters, as well as descriptions of154

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their expressions, was presented (Maza, 2012). In 1920, comic strips transitioned fromnewspaper to formal publications. In 1938, the first standalone issue appeared featuring
Superman. With this event, the true origin of comic strips, as we know them today, isestablished (Salinas, 2022).
The comic strip is considered a multimodal text where visually constructed experiences enable
the understanding of culture (Pickel, 2018). Most authors agree that comic strips handle textual
multimodality with elements such as panels, kinetic lines, visual metaphors, text, speech
bubbles, images, perspectives, layout, and chromatic codes. Regarding the textual style, comic
strips use satirical, lyrical, and aesthetic language to transmit the values and lifestyles of aparticular society. They implement the structure of literary narrative where sequentiality inchapters or episodes is employed to give progression to the stories told (Cordero & Mejía,2021). Besides, rhetorical figures predominate, enriching the texts with aesthetic resources tocapture attention and interest (Vilches-Fuentes, 2019).
The e-comic strip can be employed as a didactic support tool in communication and culture.
However, it is essential to develop narrative competence and reading ability in students basedon the sociocultural elements of the language because its reading is not like any other. The
decoding and interpreting process involves knowledge of the cultural-linguistic expressions
and codes in which the comic strip was created (Flantrmsky, 2022). In foreign language
teaching, the comic strip is considered a discursive technique that significantly enhances lexicallearning and dynamizes the development of communicative competencies (Jiménez-Arriagada
et al., 2020).
Moreover, in works such as those by Soto-Cano (2019) and Córdoba-Baldrich & Maturana(2022), the use of the comic strip is a resource for developing students' linguistic, pragmatic,
and sociolinguistic competencies in a foreign language. They also see it as a mediator toencourage reading due to its attractive nature. Additionally, Huerta & Ortega (2021) emphasize
its use to work on vocabulary and its retention through association and playful learning.
Moreover, it aids in working on communicative activities of reception and production, bothoral and written (Martínez, 2023). All these aspects together promote conscious vocabulary
learning in an everyday context.
Methodology
To ascertain how the e-comics supported by the Canva, Makebeliefscomix, and Pixton
applications influence the lexical competence development in a virtual environment at higher
education, the present study uses an experimental design aimed at manipulating theindependent variable (use of e-comics with three applications based on tasks) to observe its
possible effect on the dependent variable [development of lexical competence (LC)](Hernández-Sampieri & Mendoza, 2018). To this end, there is an experimental and a control
group. To register how e-comics influence the LC, a techno-pedagogical model was applied in
the experimental group to develop such LC using e-comics through Canva, Makebeliefscomix,
and Pixton with three tasks. In contrast, the control group developed their LC with traditional
methods, isolated from the proposed techno-pedagogical model. Finally, to test and verify the155

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plausibility of the proposal, pre-and post-treatment tests were administered to measure theeffect of using e-comics with the applications for vocabulary learning. Therefore, the study isa cross-sectional quantitative study with an associative design based on the following
hypothesis: The use of e-comics supported by Canva, Makebeliefscomix, and Pixton influences
the development of LC in a foreign language in virtual environments.
Instruments: Pre-treatment, Treatment, and Post-treatment
The following instruments were used in the three phases:••

Pre and post-treatment. A standardized test was administered to measure the students'
lexical repertoire, taking the CEFR levels as a reference to record the possible influenceof e-comics after working with the techno-pedagogical design (treatment). It had amultiple-choice format, including filling in the blanks, unscrambling, and selecting theword that best fits a context.
Treatment. A technology-mediated model was designed to teach vocabulary through ecomics using Canva, Makebeliefscomix, and Pixton according to three tasks based on
the Technique Features Analysis (TFA) Model (see Table 3). It is hosted on the Moodle
platform with hyperlinks to the Canva, Makebeliefscomix, and Pixton applications sothat students can create comic strips based on three tasks and a set of 300 pre-specified
words determined from the CEFR for Languages and according to the level
corresponding to the critical path of the English Teaching Bachelor program. Table 1
shows the design of the techno-pedagogical model.

Table 1
Design of the Techno-Pedagogical Model
Tasks withapplications
Canva

Targetwords
100 different
words for
Makebeliefscomix eachapplication(40 nouns, 40
Pixtonverbs, and 20
adjectives)

Implementation
25 words were
worked per week in
3 two-hour sessions
during a month foreach application.

Instruments to evaluate thisphase
18 components Productivefrom the TFA
and receptivemodel toacquisition
measure thetests tolexicalmeasureacquisition
students’ LC
with apps
with eachbased on tasks application

Tasks for Each Application
Canva
1. Students carried out individual recreational activities for the presentation of keywords, theirrecovery, and memory (word search and crossword puzzles).

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2. Subsequently, they used collaborative graphic organizers to categorize ideas, working on
spaced recovery and the form-meaning union with the main ideas of the dialogues for the
comic.
3. Finally, they elaborated the narrative outline individually, using the dialogues created in theprevious classes to match them with the pre-designed images in Canva, allowing productive
recovery and recognition of the mental representation of the concept. Thus, the students
became aware of learning the new lexicon, stimulating their motivation for learning.
Makebeliefscomix
1. A collaborative reading of instructions was carried out, establishing the target lexicallearning and the exemplification to elaborate on each part of the comic.
2. Each student developed their e-comic, choosing characters and settings to associate images
with their stories to promote form-meaning identification, recovery, and memory.
3. Finally, the story is built by adding dialogue balloons in which attention to the keywords andspaced and productive recovery took place for the narrative thread, contributing to theawareness of learning new lexis and motivation towards the process.
Pixton1. Students' attention was encouraged through an individual activity by highlighting the targetwords and clarifying the lexical learning objective to associate them with images toremember their meaning through the mental-lexical image. After that, students select the
keywords for vocabulary recovery, representing their understanding through the formmeaning union.
2. Participants did a group activity called the use of context that consisted of multiple recoveriesof the keywords in different exercises provided by the teacher like the construction andcombination of sentences with exemplification, spaced recovery with gap filling, andproductive recovery with the generation of simple texts
3. As a final activity, they individually wrote the narrative of the e-comic, taking into accountthe productive generation of the dialogues and the marking of keywords to be aware of the
new vocabulary. Then, the students read aloud to identify the key ideas in their narratives
and negotiated the correct use of words in the dialogues to avoid interference or confusion,
contemplating the high degree of generation in the final evidence of their learning,enhancing their motivation in this task.
The activities were both individual and collaborative, based on active methodologies,
gamification, and the use of technological applications. Besides, to evaluate the participants'
LC in each implementation period, two instruments were used:•

Receptive and productive written tests were used to evaluate the three applications where
every 100 words have a ten-weighted score in both instruments. The receptive test consistsof a list of 100 target words for each application in English and asks the student to enter
their meaning in Spanish. In contrast, the productive test contains the same target words in
Spanish and asks the student to write them in English.

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In each task, the TFA model was used to identify which of the three applications promotes
the most vocabulary learning, based on the premise that 1 means presence and 0 absence ofeach element belonging to five categories: motivation, notation, recovery, generation, andretention with a maximum of 18 points [see Table 3] (Nation & Webb, 2011).

SampleSixty first-semester students, the entire population of the English Teaching Bachelor of the
Benemérita Universidad Autónoma de Puebla, who signed the informed consent form,
participated in this research. Some of their characteristics are low proficiency level incommunicative situations, lack of lexical background to interact in varied situations orcontexts, and enrolled in virtual classes for six hours a week. Half of the students wererandomly assigned to the control group and the rest to the experimental group.
Data Collection and Analysis
Data collection was done in three phases. Phase 1: Pre-treatment. This was administered at the
beginning of the study and before the treatment to both the control and experimental groups toassess their level of lexical competence. Phase 2: Treatment. Data was collected only from theexperimental group at the end of each implementation of the applications (Canva,
Makebeliefscomix, and Pixton). First, the receptive test was administered individually.
Subsequently, the teacher administered the TFA instrument as a group so that each student
recognized the absence or presence of the 18 elements in each application task. Finally, eachparticipant answered the productive test. Phase 3: Post-treatment. This was administered after
the treatment to both groups and at the end of the 2023 semester to obtain a reference for
evaluating the proposal and to corroborate the hypothesis. Table 2 presents the data analysis
model used to display the results.
Table 2Analysis Model. Own Elaboration
PhasesPre-treatment
Treatment

Post-treatment

PurposeMeasure initial lexical competence
Measure vocabulary learning from e-comic
strips using the Canva, Makebeliefscomix, andPixton applications
Measure final lexical competence
Results

The following section presents findings related to participants' baseline LC, the results of LC
according to the TFA model, the receptive and productive tests, and the post-treatment results
to analyze and verify the influence of e-comics on LC.

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Figure 1
Pre-Treatment Stage Results

Students' vocabulary level
A126

A1
24

6

4
Control group

Experimental group

As seen in Figure 1, in both groups, a majority of students are at level A1- (26 in the control
and 24 in the experimental group), with only a few students at level A2 (4 in the control and 6
in the experimental group. The graph corroborates the background information on theparticipants, as it shows a lack of vocabulary diversity, resulting in the use of simple words andphrases that do not align with the level demanded by the course. It has led to poor
communication, which can even cause misunderstandings. The results suggest a limited
development of higher-level linguistic skills, impacting reading comprehension and written
production. It underscores that a limited vocabulary hinders the successful expression andcommunication of ideas, affecting the student's confidence and motivation to communicate in
the target language, in this case, English.
Treatment
Table 3Components and Criteria of the Technique Feature Analysis (TFA) Model. Source: Nationand Webb, 2011.

Criterion

Motivation
1. Objective:
lexical learning
2. Motivation
toward learning

Number of students who perceived presence of element
Explanation
Week 1-4
Week 5-8
Week 9-12
Canva
Makebeliefs
Pixton
comix
N=30
N=30
N=30
Is there a clear objective
for lexical learning?
Do they motivate
students?

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2

29

29

29

28

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3. Student word
selection
Noticing
4. Attention to
keywords
5. Awareness of
new lexicallearning
6. Negotiation

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Do students choose thewords they will learn?

1

2

30

Does it encourage
students to pay attention
to keywords?
Does the activity makestudents aware they arelearning new
vocabulary?
Does the activity offer
negotiation
opportunities?

3

27

30

28

29

30

1

2

29

Does the activity
provide retrieval
opportunities?
Is there productive
retrieval?
Does it involve recall?
Does the activity
involve multiple
retrieval opportunities
for each keyword?
Does the activity lead tospaced retrievals?

28

29

30

29

29

30

28
1

29
1

30

27

29

30

Does the activity
promote generation?
Does the activity
involve productive
generation?
Does the activity
involve a high degree ofgeneration?

2

29

30

2

3

30

0

1

29

Does the activity
provide opportunities tosuccessfully link form
and meaning?

28

29

30

Retrievaal
7. Retrieval

8. Productive
retrieval
9. Recall
10. Multiple
retrievals

11. Spaced
retrievals
Generation
12. Generation
13. Productive
generation
14. High degreeof generation
Retention
15. Retention

30

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16.
Exemplification

Does the activity
promoteexemplification?
17. Mental image Does the activityof the concept
promote a mental imageof the concept?
18. Avoiding
Does the activity avoid
interference
interference or
confussion between
words?

1

30

30

30

1

30

0

0

30

In the first month when using Canva, majority of participants distinguished 8 out of 18
components of the TFA model: one component for the motivation dimension (motivation
towards learning), one component for notoriety (awareness of learning new lexicon) with thegeneration of the outline of its narrative, four for recovery (recovery, productive recovery,
remembering, spaced recovery), and two of retention (form-meaning union and mental imageof the concept) with recreational activities, use of graphic organizers and writing the narrative.
For the generation dimension with the Canva application, they did not identify any of its threecomponents.
During the second month, while working with the Makebeliefscomix application, students
pointed out 11 of the 18 components of the TFA model. In each dimension, they observed thelack of one or two components. Considering the motivation category, the clarity of the lexicallearning objectives and motivation towards learning through construction activities in the
comic stood out. For noticing, they consider attention to keywords and awareness of the new
lexicon in the comic construction. In the recovery component, the evocation and form-meaning
union through associated images with meanings stood out, as well as productive and spaced
recoveries. Finally, through reading instructions aloud, they perceived the receptive generativeuse of the word and exemplification.
In the third month with Pixton, participants recognized the development of the 18 componentsof the 5-dimensional TFA model in the task. With the activity of using or reusing the keyword,
they identified lexical learning objectives, a motivation towards learning, and an appropriate
selection of words. In the dimension of noticing, the reading activity with marking allowed
them to highlight the attention to the keywords, become aware of learning new lexis, andmanage negotiation. The mental-lexical image activity helped them perform the productive,
multiple, and spaced retrieval tasks. In the case of the generation component, reading alouddetonated productive generation and a high degree of generation through creativity andappropriate use of words. Finally, the mental-lexical image task promoted retention bystimulating the form-meaning union, exemplification, and mental concept image, avoiding
interference.

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At the end of the four weeks of working with each application, receptive and productive
acquisition tests were administered to identify the average lexical knowledge of participants'
target words after the treatment.
Table 4
Results of the Receptive Acquisition Test

Canva
Makebeliefscomix
PixtonSum of grades

Average

Variance

243
262
283

8.10
8.73
9.43

0.67
0.76
0.58

TFA
elements
8
11
18

Pixton reflects the highest average in receptive acquisition (Table 4) and less variability inratings (Table 3). These data indicate that the majority of students have a consistent andpositive opinion about the application to learn vocabulary. Besides, Makebeliefscomix has ahigh average but presents dispersion in the grades, so the students' perceptions vary. Finally,
Canva shows the lowest average and an intermediate variance. Then, students evaluated it more
uniformly but less favorably than the other applications.
Table 5Results of the Productive Acquisition Test

Canva
Makebeliefscomix
PixtonSum of grades
242
261
281

Average
8.07
8.70
9.37

Variance
0.26
0.22
0.18

Pixton recorded the highest average along with the lowest variance in the productive aquisition
test, making it the best-evaluated tool, with strong consensus for user satisfaction.
Makebeliefscomix has a lower average than Pixton and a lower variance than Canva, however,satisfaction with this tool is more consistent despite the lower average. Last but not the least,
Canva reports the lowest average but high variance, which means that opinions about this tool
are discrepant.

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Post-Treatment
Figure 2
Results of the Post-Diagnostic Stage

Students' vocabulary level
after the treatment
Control group

Experimental group

22

21

9

8

A1-

A1

A2

With a radical change in experimental group and minimal change in control group, the results
highlight the relevance of implementing e-comics to develop lexical competence. Posttreatment, 21 students are at level A1, and nine students at A2. In contrast, only four more
students from the control group reached level A2 compared to the pre-treatment stage, where
eight students were at this level and 22 at level A1-. This phase demonstrates a positive
association between e-comics and LC, asserting that digital co mics with three applications
influence the development of students' lexical competence.
Discussion
The findings suggest that the use of e-comics as a pedagogical tool promotes lexicalcompetence in English as a foreign language, which was achieved from ludic, creative, anddynamic activities based on the components of the TFA, exposing the student to keywords
within dialogues and texts for subsequent recovery and use, improving the acquisition of new
vocabulary. These indicators coincide with the studies by Hidalgo (2020), Breda (2021), Aristu& Torres (2021), and Verano & Carrillo (2023).
In the process of using Canva to create the first e-comic, the individual recreational activities
proved to be plausible for lexical learning due to the activation of 8 TFA components such as
motivation towards learning from the tasks, awareness of learning new vocabulary, recovery,
productive and spaced recoveries, memory retrievals, form-meaning union and mental imageof the concept. The findings indicate that the presentation and retrieval of target words throughexercises such as word searches, crossword puzzles, and e-comics facilitated the memorizationand learning of target words. This data matches Mayer's (2017) assertion that learning based

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on interactive activities significantly improves information retention by involving students inactive and continuous learning.
Furthermore, the development of collaborative graphic organizers with Canva allowed thecategorization of ideas and spaced retrievals, techniques that enabled the visual organizationof information, contributing to the connection between form and meaning, especially when
they integrated their dialogues into digital narratives in the e-comic template. This supports
Johnson & Johnson’s (2019) assertion that collaborative learning leads to deeper and more
retentive understanding. Finally, individual narratives using dialogues and clipart images in
Canva favored productive recovery and recognition of the mental image, a process thatreinforces the learned vocabulary, stimulating creativity and metacognitive awareness. In thisway, visual applications and techniques enhance retention and understanding by creating a
dynamic environment.
When creating the second e-comic with the application of Makebeliesfscomix, the 11
components identified by students for each dimension were: in the motivation dimension,clarity of the objectives of lexical learning and motivation towards learning generated byconstruction of the e-comics, seen as a unique experience to learn vocabulary that goes beyond
memorization. As it was based on a design already established by the application itself, it
promoted a didactic sequence guided through the construction of dialogues, scenes, andnarratives in their e-comics. This coincides with the study by Sanhueza (2018), which alludes
to the idea that narrative activities promote the generation process that deals with thereconceptualization processing. Thus, it consists of generating or creatively using the
vocabulary acquired in a different context within e-comics.
In the noticing dimension, the results showed that with this application, students focused theirattention on word marking and becoming aware of the new lexicon, which led them to focus
on the target word connotation, being aware of their lexical growth and learning. Similarly,
Gohar et al. (2018) and Sánchez et al. (2019) also found that input in the comic helps highlightand capture students' curiosity towards a target word, improving lexical memory.
In the recovery dimension, productive and spaced recovery was highlighted and remembered
due to image-meaning association, making students creative and facilitating the assimilationof the new lexicon. This is in line with studies by Palacios & Plazas (2019) and Morales (2021),
who mention that comic illustrations generate emotions, positively influencing the student's
attitude toward learning and stimulating their imagination, facilitating conceptual assimilation
due to the visual-lexical association process in multiple scenarios. Besides, images helped
generate a playful environment that favors understanding the communicative situation, as
Marin's (2022) study shows.
Considering retention, form-meaning union, and exemplification were identified with thepractice of writing the e-comic sentences taking into account the contextualization and correct
use of the words in combination and sentence construction activities as mentioned by Olagbaju& Popoola (2020) in their study. Finally, in the generation dimension, receptive generative use

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was achieved by reading instructions aloud and writing their e-comics, stimulating theircreativity, and increasing lexicon, coinciding with Jiménez's work (2023).
Regarding the third e-comic design with the Pixton application, the students developed themost vocabulary acquisition according to the receptive and productive tests and a posttreatment test. This is because of the activation of the 18 components of the dimensions fromthe TFA model thanks to the ludic, dynamic, and creative activities. Students perceived the
motivation of lexical learning through experimenting with activities that focused on the clarityof the lexical learning objective and an appropriate selection of words. Findings by Caiza(2023) and Jiménez (2023) similarly affirm that activities using target words in sentence
replacement favor retention.
In noticing, students could highlight attention to keywords, be aware of learning the new
lexicon, and manage negotiation of meanings with activities such as reading and marking thewords within their productions. Olagbaju & Popoola (2020) and Tovar & Pineda (2021) havealso demonstrated that target-word noticing in narrative texts allows students to become awareof vocabulary use, developing lexical competence. Furthermore, mental-lexical image activity,
sentence combination and construction, gap filling, and image-to-lexical association in Pixton
performed productive, multiple, and spaced retrieval tasks, similar to Chinga & Pérez’s study(2023), where sentence creation allows students to understand the use of keyword in different
writing situations.
In the case of the retention dimension, four elements are present for the treatement group: theform-meaning union, exemplification, mental image of the concept, and interference avoided,
thanks to the mental-lexical image task that took place at the same time when the students
designed the characters, settings, and images they used in their e-comics. This confirms thefindings by Alcaraz-Mármol (2021) and Hidalgo (2020) that the mental image of the word
stimulates creativity in writing dialogues in a comic strip and avoids word confusion.
Finally, in the generation dimension, the students focused on writing their e-comic and readingit aloud to develop a productive and high-grade generation that evidenced creativity andappropriate use of words. Similar studies by Sanhueza (2018), Sánchez et al. (2019), and Nadal& Thome (2021) affirm that the implementation of reading aloud as a generative activity
promotes clarity of meaning of words, expanding lexical knowledge.
It follows that using the e-comic strip based on intentional activities is more conducive toimproving vocabulary learning in English, primarily promoting the retrieval, creation, andextension of the lexicon. Moreover, the results show that the e-comic strip stimulates students'
creativity through visual, sensory, and graphic elements that capture their attention, promote
active participation, and increase L2 literacy. To achieve this, the teacher selects specificlexical content and segments the comic strip's themes into semantic fields that categorize thewords for easy assimilation (Caiza, 2023). In linguistic terms, the comic strip emphasizes
deducing lexical meaning through its illustrations and situational content. In summary, the e-

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comic strip offers a new, easily readable experience with simple and accessible syntax, creating
enriching experiences with varied linguistic and lexical content.
It is worth mentioning that previous studies have focused on activities using physical materials
and are conducted in face-to-face settings (Hu & Nassaji, 2016; Zou & Xie, 2018 and Nakata& Webb, 2016). The contribution of this study lies in its analysis of the development of lexicalcompetence through technology-mediated activities in a virtual environment.
Recommendations
The didactic applications of e-comics go beyond simple language exposure making it afavorable means for learning vocabulary in a foreign language. However, it is worth notingthat one of the main limitations of this research is the sample size, as it only works with English
language learning. There is also variation in the participants' performance with each app,prompting reflection on the possibility of considering the specific characteristics of theapplications used in activities. Thus, exposing students to different resources may lead toenriched data to interpret individual perceptions (Hidalgo, 2020). Secondly, before replicating
the research in favor of innovating teaching practices in hybrid or virtual environments, wesuggest verifying that participants have access to the Internet and possess a device for work, aswell as minimal digital skills to carry out their activities. Teachers need to design a technopedagogical model framed within an active methodology and aligned with their program,containing activities with explanatory tutorials and evaluation instruments. Finally, we
recommend collecting qualitative data to complement quantitative findings for a more
complete understanding of the phenomenon.
It is crucial to consider these limitations for future research and expose the same groups todifferent treatments. Thus, future research could explore the influence of grammaticalcategories and their relationships for vocabulary learning, implications of incidental
vocabulary teaching mediated by technology, the articulation between writing and reading in
L2 through comics, creativity in discursive areas, the digitalization of comic strips as a technopedagogical tool, the development of cultural and colloquial language in L2 through narrative
comics, cultural analysis through idiomatic expressions in English, among others.
Conclusion
As a pedagogical resource for vocabulary learning in English, the comic strip has proven to bea creative medium to develop receptive and productive lexical skills. The activities developedaround its application generated motivation, creativity, and student engagement in newlearning. This process of interest contributed to the cognitive processing of reflective recall,reducing lexical forgetting. The characteristics of the comic strip, such as visibility, graphics,
and color, generated confidence and enthusiasm for actively participating in constructive
exercises. Likewise, these properties stimulated the student's memory through the formmeaning association of words, reinforcing lexical recall. Additionally, the search for word
meanings encouraged participants to expand their tools and sources of information. They166

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utilized dialogue, negotiation, internal memory, and various means to diversify their resources,
thus demonstrating a more active and committed approach to their learning.
At the same time, negotiation promoted the development of communicative skills in thelanguage, significantly improving the student's oral and written expression. Regarding form
retrieval, it was also observed that using definitions or derivations mitigated confusion or
guessing of word connotations. The tasks of filling in gaps, creating sentences, and writing a
comic strip did not merely involve using notable words but also activated the cognitive basefor lexical learning through decontextualization exercises. These activities stimulated the needfor appropriate use of the learned words, which in the long term led to their satisfactory
application in different communicative contexts. Similarly, enhanced analysis and synthesis
skills offered by ICTs, created innovative spaces for activities and fostering creativity in task
design. The digitalization of exercises facilitated the development of technologicalcompetencies related to learning English vocabulary.
Finally, it is essential to highlight that our hypothesis was confirmed, demonstrating that the ecomic strips as a pedagogical tool supported by technological mediation (using applications
like Canva, Makebeliefscomix, and Pixton) with well-designed activities is effective forlearning vocabulary and even developing reading and writing skills in an innovative andmotivational way from the student's perspective; a model that addresses the needs and demandsof today's educational modalities.

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References
Alcaraz-Mármol, G. (2021). Aprendizaje De Vocabulario En Segunda Lengua En Educación
Primaria: Poniendo A Prueba El Modelo Technique Feature Analysis. RLA. Revistade lingüística teórica y aplicada, 59(2), 43–62. http://dx.doi.org/10.29393/rla5910avga10010
Alejos, E. M. (2017). Competencia léxica, un pilar fundamental para los procesos de lectura yescritura universitaria. Acción Pedagógica, 26(1), 60–68.
Aristu, A., y Torres, L. (2021). El mapeado de textos: un recurso para el aprendizaje delléxico. Marco ELE. Revista de Didáctica Español Lengua Extranjera, (33).
Barón, L., Müller, O., y Labos, E. (2013). Los conceptos innatos en la obra de Chomsky:
Definición y propuesta de un método empírico para su estudio. Avances en Psicología
Latinoamericana, 31(2), 324–343.
Beghadid, H. (2022). La enseñanza del vocabulario: Hacia una competencia léxica.
Hispanical, 1(1), 25–33.
Berthely, J. C., Esquivel, I., y Aguirre, G. (2023). Recursos multimedia para el aprendizaje
autónomo de vocabulario del inglés como lengua extranjera: Multimedia resources forautonomous learning of English vocabulary as a foreign language. Estudios λambda.
Teoría y práctica de la didáctica en lengua y literatura., 8(2).
https://doi.org/10.36799/el.v8i2.126
Borromeo, C. A., Fernández, J. A., y Ramírez, A. R. (2018). La tecnología en la enseñanza
de idiomas: Evolución a través de los métodos. REencuentro. Análisis de Problemas
Universitarios, 29(76), 133–154.
Breda, D. (2021). ¿Las actividades en las lecturas graduadas promueven el aprendizaje devocabulario? Un estudio de Technique Feature Analysis. Revista Nebrija deLingüística aplicada a la enseñanza de Lenguas, 15(30), 103-117. DOI:
https://doi.org/10.26378/rnlael1530430
Cáceres, M. F., Ramos, M. J., Díaz, D. C., y Chamorro, Y. C. (2018). Vocabulario receptivo
en estudiantes de preescolar en la comunidad de Talca, Chile. Innovación Educativa,
18(78), 193–208.
Caiza, J. (2022). Comic como recurso digital para el fomento de la lectura en los estudiantes
de 3ro de bachillerato técnico. Tesis de Maestría, Universidad Tecnológica
Indoamérica, Ecuador. http://repositorio.uti.edu.ec//handle/123456789/2750
Castillo, Y., Zumeta, E., y Rodríguez, N. (2022). La competencia léxica en la enseñanza delidioma inglés como Lengua Extranjera. (Ensayo). Roca. Revista científicoeducacional de la provincia Granma, 18(1), 275–288.
Ccoa, M. del P., y Alvites, C. G. (2021). Herramientas Digitales para Entornos Educativos
Virtuales. Lex: Revista de la Facultad de Derecho y Ciencia Política de la
Universidad Alas Peruanas, 19(27), 315-330.
https://dx.doi.org/10.21503/lex.v19i27.2265
Chinga, P. y Pérez, D. (2023). El guion de cómic como estrategia para ampliar el
vocabulario. Bachelor's thesis, Universidad de Guayaquil. Facultad de Filosofía,
Letras y Ciencias de la Educación. https://repositorio.ug.edu.ec/items/824b60be-5e6d491f-b6c8-398e70d77ca5

168

IAFOR Journal of Education: Technology in Education

Volume 12 – Issue 2 – 2024

Consejo de Europa. (2020). El volumen complementario del Marco Común Europeo deReferencia de las Lenguas, ahora en español. Recuperado 1 de enero de 2024, dehttps://www.educacionyfp.gob.es/mc/accion-exterior/mira-lo-quehacemos/actualidad/mcer-presentacion.html
Cordero, M., y Mejía, M. A. (2021). El Comic como Recurso Didáctico para Fortalecer los
Procesos de Comprensión Lectora [Tesis de pregrado, Universidad Santo Tomas].
https://repository.usta.edu.co/bitstream/handle/11634/38207/2021mariancorderomaria
mejia.pdf?sequence=1&isAllowed=yCórdoba-Baldrich, H. Y., y Maturana, Y. (2022). El uso de la historieta como estrategia
metodológica para fortalecer competencias comunicativas en el aprendizaje delinglés. Revista Electrónica en Educación y Pedagogía, 6(10), 167–182.
https://doi.org/10.15658/rev.electron.educ.pedagog22.04061011
De la Maya, G., y López-Pérez, M. (2021). Hábitos lectores y competencia léxica de futuros
profesores de inglés em Educación Primaria. Educ. Form., 6(1).
https://doi.org/10.25053/redufor.v6i1.3504
Flantrmsky, O. G. (2022). Cómic y novela gráfica como literatura menor: Debates y validez
como producto cultural. Folios, 56, 3–6. https://doi.org/10.17227/folios.56-13477
Flores-González, E. (2020). El proceso de aprendizaje de la asignatura de Biología enmodalidad blended learning. Journal of Technical Education/Revista de Educación
Técnica, 4(13).
Flores, E. (2023). El aprendizaje de la Biología en un entorno virtual: Learning biology in a
virtual environment. Journal of Behavior, Health & Social
Issues, 15(1). https://doi.org/10.22201/fesi.20070780e.2023.15.1.83464
García-López, M. (2000). Estrategias de aprendizaje de vocabulario de inglés utilizadas por
los estudiantes de Secundaria. Lenguaje y textos, 15, 61–70.
García, D. J., Delgado, A. A., González, E. A., y Swiggers, G. (2020). Estrategias yactividades para aprendizaje del léxico en dos manuales escolares de educación
primaria. Horizontes. Revista de Investigación en Ciencias de la Educación, 4(13),15-29. https://doi.org/10.33996/revistahorizontes.v4i13.88
Gohar, M., Rahmanian, M. y Soleimani, H. (2018). Technique Feature Analysis or
Involvement Load Hypothesis: Estimating their predictive power in vocabulary
learning. Journal of Psycholinguistic Research, 47(4), 859–869.
https://doi.org/10.1007/s10936-018-9568-5
Gómez, J. F., Díaz, C., y Gómez, W. D. (2021). Estrategias de aprendizaje y creencias sobre
el idioma inglés: Una aproximación correlacional en estudiantes universitarios. Práxis
Educativa, 16, 1-17. https://doi.org/10.5212/PraxEduc.v.16.16572.024
Guerrero, L. E., Pérez, M., Dajer, R., Villalobos, M., y Méndez, Y. (2022). Estrategias deAprendizaje en inglés empleadas por estudiantes universitarios de Pedagogía de una
universidad mexicana. Ciencia y Educación, 6(3), 35–51.
https://doi.org/10.22206/cyed.2022.v6i3.pp35-51
Hernández-Sampieri, R. & Mendoza, C. P. (2018). Metodología de la investigación: las rutas
cuantitativa, cualitativa y mixta. México: McGRAW-HILL. 774 Pp.

169

IAFOR Journal of Education: Technology in Education

Volume 12 – Issue 2 – 2024

Hernández, J., Torres, D., y Camargo, E. (2020). Era digital en tiempos de pandemia:
Educación, color, conocimiento y comunicación. Utopía y Praxis Latinoamericana,25(8), 216–230. https://doi.org/10.5281/zenodo.4087421
Hidalgo Gallardo, M. (2020). Efectos del método de presentación léxica en la recuperación yel reconocimiento del vocabulario en español LE/L2 para italófonos. RILEX. Revista
Sobre Investigaciones léxicas, 3(1), 47–73. https://doi.org/10.17561/rilex.3.1.5377
Hu, Ma., & Nassaji, H. (2016). Effective vocabulary learning tasks: Involvement Load
Hypothesis versus Technique Feature Analysis. System, 56, 28–39.
Huerta, S. M., y Ortega, N. (2021). Incorporación de historietas en el aprendizaje del inglésbásico en el Centro de Idiomas, Universidad Nacional Santiago Antúnez De Mayolo.
Llamkasun, 2(4), 110–123. https://doi.org/10.47797/llamkasun.v2i4.67
Jiménez-Arriagada, V., Bañales-Faz, G., y Lobos-Sepúlveda, M. T. (2020). Investigacionesdel cómic en el área de la didáctica de la lengua y la literatura en Hispanoamérica.
Revista mexicana de investigación educativa, 25(85), 375–393.
Jiménez, I. (2023). Implementing the creation of comics using information andcommunication technologies to enhance reading and writing skills strengthening theautonomous learning of the 11-2 section at marco tulio salazar nocturnal high school
during the second Quarter of. Ciencia Latina Revista Científica Multidisciplinar, 7(1),9294–9307. https://doi.org/10.37811/cl_rcm.v7i1.5050
Johnson, D. W., y Johnson, R. T. (2019). Cooperation and the Use of Technology.
Cambridge University Press.
Marin-Marin, A., y Hernández-Romero, M. I. (2022). La competencia léxica en inglés deestudiantes universitarios en México. Revista Lengua y Cultura, 4(7), 15–25.
DOI: https://doi.org/10.29057/lc.v4i7.8726
Marin, L. C. (2022). Los cómics para mejorar la comprensión de textos en el área de inglés
en estudiantes del VI ciclo de la IE “32075 de Tambogán”, Churubamba-Huánuco
2020. Tesis de Pregrado, Universidad de Huánuco, Perú.
http://repositorio.udh.edu.pe/123456789/3232
Martínez, M. I. (2023). El mundo del comic: una propuesta didáctica para el aula de ELE.
redELE. Revista electrónica de didáctica del español lengua extranjera, 35, 1-32.
Mayer, R. E. (2017). Multimedia Learning. Cambridge University Press.
Maza, A. E. (2012). Un acercamiento al cómic: Origen, desarrollo y potencialidades.
Perspectivas docentes, 50, 12–16.
Morales, M. (2021). Estrategia del uso del cómic y organizadores visuales para fortalecerla
expresión escrita en el aprendizaje del idioma inglés desde la perspectiva de losestudiantes de nivel A2 de una universidad privada de Lima 2020-2. Tesis deMaestría, Universidad de Perú, Perú. https://hdl.handle.net/20.500.12867/4929
Mosquera, D. M., y Rendón, Á. R. (2021). Dibujando para impulsar el desarrollo: Una
historieta para compartir los aprendizajes del proyecto InnovAcción Cauca. Signo yPensamiento, XL(79), 1–21. https://doi.org/10.11144/Javeriana.syp40-79.didh
Nadal, L., y Thome, S. (2021). Mediación y aprendizaje de lenguas en contextos de no
inmersión: un análisis experimental. RLA. Revista de lingüística teórica y aplicada,59(2), 111–132. http://dx.doi.org/10.29393/rla59-13mals20013

170

IAFOR Journal of Education: Technology in Education

Volume 12 – Issue 2 – 2024

Nakata, T., y Webb, S. (2016). Vocabulary learning exercises: Evaluating a selection of
exercises commonly featured in language learning materials. En Tomlinson, Brian(ed.), Second Language Acquisition Research and Materials Development for
Language Learning, Oxon, Inglaterra: Taylor and Francis, 123–138.
Nation, I. S. P., & Webb, S. A. (2011). Researching and Analyzing Vocabulary. Heinle,
Cengage Learning.
Olagbaju, O.O. & Popoola, A.G. (2020). Effects of audio-visual social media resources
supported instruction on learning outcomes in reading. International Journal ofTechnology in Education (IJTE), 3(2), 92–104. https://doi.org/10.46328/ijte.v3i2.26
Onieva-Palomar, R. (2019). El aprendizaje del español como lengua extranjera: La
importancia de utilizar el diccionario adecuado. Foro de Profesores de E/LE, (15),147-160. https://doi.org/10.7203/foroele.0.15598
Palacios, D., y Plazas, J. (2019). Un cómic: una estrategia didáctica enmarcada en el
enfoque CLIL en inglés para fortalecer el proceso de aprendizaje de conceptos
propios de Science. Tesis de Pregrado, Universidad Libre de Colombia.
https://repository.unilibre.edu.co/handle/10901/18752
Pasuy, G. Y., y Agudelo, C. L. (2022). Competencia léxica y procesos de variación semántica
en los verbos del parlache*. Cuadernos de Lingüística Hispánica, 39, 1–20.
https://doi.org/10.19053/0121053x.n39.2022.13902
Pickel, A. (2018). La cultura como sistema semiótico: Una redefinición de la idea de cultura
desde la perspectiva sistemista. Cultura y representaciones sociales, 13(25), 9–47.
https://doi.org/10.28965/2018-25-01
Robles, P. (2018). Competencia léxica y oportunidades de aprendizaje léxico en el aula delenguas: Una aproximación teórica. Lengua y Habla, 23, 157–182.
Rodríguez-Montes, T. (2020). El desarrollo de la competencia léxico-semántica a través de lamorfología léxica. Revista de Filología de la Universidad de La Laguna, 40, 275–
288. https://doi.org/10.25145/j.refiull.2020.40.13
Roselló, J. (2021). Metodología sociolingüística y enseñanza de ELE. marcoELE. Revista deDidáctica Español Lengua Extranjera, 33.
Salinas, M. A. (2022). El cómic como herramienta para la enseñanza y la transmisión delpatrimonio. Argonautas. Revista de Educación y Ciencias Sociales, 12(19).
Sánchez, C., Pérez, M., y Robles, P. (2019). The effects of word frequency and typographicalenhancement on incidental vocabulary learning in reading. Journal of Spanish
Language Teaching, 6(1), 14–31. https://doi.org/10.1080/23247797.2019.1590000
Sanhueza , C. H. (2018). Desarrollo de la competencia léxica en aprendientes de inglés como
lengua extranjera de nivel intermedio. Tesis de Doctorado, Universidad deConcepción, Chile.
http://repositorio.udec.cl/bitstream/11594/3000/4/Tesis_Desarrollo_de_la_competencia_lexica.Image.Marked.pdf
Sanhueza, C., Ferreira, A., & Sáez, K. (2018). Desarrollo de la competencia léxica a través deestrategias de aprendizaje de vocabulario en aprendientes de inglés como lengua
extranjera. Lexis, 42(2), 273–326. https://doi.org/10.18800/lexis.201802.002
Soto-Cano, A. B. (2019). El uso del cómic en el aula de Francés como Lengua Extranjera(FLE). Citius Altius Fortius, 34, 139. https://doi.org/10.15366/tp2019.34.011

171

IAFOR Journal of Education: Technology in Education

Volume 12 – Issue 2 – 2024

Torres, L., y Aristu, A. (2021). La dimensión personal en el aprendizaje del léxico en la red.
MarcoELE. Revista de Didáctica Español Lengua Extranjera, 32.
Tovar, V., y Pineda, D. (2021). El cómic: una estrategia didáctica para el aprendizaje yfortalecimiento de vocabulario en inglés de los estudiantes del curso 203 de uncolegio privado. Tesis de Pregrado, Universidad Libre de Colombia.
https://repository.unilibre.edu.co/handle/10901/19440
Trujillo, F., Salvadores, C., y Gabarrón, Á. G. (2019). Tecnología para la enseñanza y elaprendizaje de lenguas extranjeras: Revisión de la literatura. RIED-Revista
Iberoamericana de Educación a Distancia, 22(1), 153–169.
https://doi.org/10.5944/ried.22.1.22257
Valdés-León, G. (2021). Competencia léxica y escritura académica: Analíticas de aprendizaje
en un curso de escritura universitaria. Texto Livre: Linguagem e Tecnologia, 14(1).
https://doi.org/10.35699/1983-3652.2021.24560
Vargas-Murillo, G. (2020). Estrategias educativas y tecnología digital en el proceso
enseñanza aprendizaje. Cuadernos Hospital de Clínicas, 61(1), 114–129.
Verano, V. y Carrillo, J. (2023). Estrategia pedagógica basada en la elaboración de un
Cómic Digital para la enseñanza del léxico en Inglés aplicado a los estudiantes degrado sexto de la Institución Educativa Juan Pablo I de Cúcuta. Tesis de Maestría,
Universidad de Cartagena, Colombia.
https://repositorio.unicartagena.edu.co/bitstream/handle/11227/16720/TGFVictor%20
Verano_Judith%20Carrillo.pdf?sequence=1&isAllowed=yVilches-Fuentes, G. (2019). El cómic: ¿un arte secuencial?: Una crítica al concepto desecuencia y la clasificación de las transiciones entre viñetas de Scott McCloud.
Neuróptica: Estudios sobre el cómic, 1, 203–217.
https://doi.org/10.26754/ojs_neuroptica/neuroptica.201914328
Zambrano, H. J., y García, M. A. (2022). El desarrollo lector en el nivel léxico de losestudiantes del nivel de básica superior. Dominio de las Ciencias, 8(3), 92.
Zou, D., & Xie, H. (2018). Personalized word-learning based on Technique Feature Analysis
and learning analytics. Journal of Educational Technology y Society, 21, 233–244
Corresponding author: Norma Flores-González
E-mail: norma-fg@hotmail.com

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Volume 12 – Issue 2 – 2024

Reviewers: Volume 13 – Issue 3
The editorial team would like to thank the following reviewers for their contributions to thepeer review for this issue of the journal. Their dedication and assistance are greatly appreciated.

Senior Reviewers
Dr Murielle El Hajj Nahas
Lusail University, Qatar
Email: murielle.elhajj@hotmail.com
Dr Mikel GarantBeijing Institute of Technology, China
Email: mikel.garant@helsinki.fi
Dr Urarat Parnrod
Songkhla Rajabhat University, Thailand
Email: urarat.parnrod@gmail.com
Dr Shwadin Sharma
California State University Monterey Bay, USA
Email: ssharma@csumb.edu
Reviewers
Dr Rena Alasgarova
Baku Oxford School, Azerbaijan
Email: rena.alasgarova@mtk.edu.az
Dr Philip Ajewole
Federal University Oye-Ekiti, Nigeria
Email: philip.ajewole@fuoye.edu.ng
Dr Azlan Abdul Aziz
Universiti Teknologi MARA Melaka, Malaysia
Email: azlan225@uitm.edu.my
Dr Shiao-Wei Chu
National Pingtung University, Taiwan
Email: shiaowei0819@gmail.com
Dr Alice Gasparini
University for Foreigners of Siena, Italy
Email: alice.gasparini@unistrasi.it

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Volume 12 – Issue 2 – 2024

Dr Heeseon Jang
Pyeongtaek University, Korea
hsjang@ptu.ac.kr
Dr Yuek Li Ker
Southern University College, Malaysia
Email: likavia2011@hotmail.com
Dr Sunny Nwakanma
Ignatius Ajuru University of Education, Nijeria
E-mail: sunny.nwakanma@iaue.edu.ng
Dr Queen Ogbomo
Tennessee Technological University, USA
qogbomo@tntech.edu
Dr Fei Ping Por
Tunku Abdul Rahman University of Management and Technology (TAR UMT), Malaysia
porchristine@gmail.com
Dr Sherwin B. Sapin
Laguna State Polytechnic University-Los Baños Campus, Philippines
sbsapin@lspu.edu.ph
Dr Miguel A. Varela
International Baccalaureate (IB) School Verification/Consultant/Reader/Workshop Leader,
IB Organization
Email: miguevarela@gmail.com

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journal of education

ISSN: 2187-0594