iafor

journal of education

technology in education
Volume 11 – Issue 2 – 2023
Editor: Michael P. Menchaca

ISSN: 2187-0594

iafor
IAFOR Journal of Education: Language Learning in Education
Volume 11 – Issue 2 – 2023

IAFOR Publications
The International Academic ForumIAFOR Journal of Education: Technology in Education
EditorMichael P. Menchaca
University of Hawaii at Manoa
Associate Editor
Daniel L. Hoffman
University of Hawaii at Manoa
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 Journals Administrator: Mark Kenneth Camiling
Publications Manager: Nick Potts
IAFOR Journal of Education: Technology in Education
Volume 11– Issue 2 – 2023
Published August, 2023
IAFOR Publications © Copyright 2023
ISSN: 2187-0594
ije.iafor.org

IAFOR Journal of Education: Technology in Education
Volume 11 – Issue 2 – 2023
Edited by Michael P. Menchaca
Associate Editor: Daniel L. Hoffman
Associate Editor: Devayani Tirthali

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

1

Notes on Contributors

3

A Phenomenological Exploration of Students’ Perceptions of AI Chatbots
in Higher Education
Soniya Antony
R. Ramnath7

Post-COVID-19 Higher Learning: Towards Telagogy, A Web-Based
Learning Experience
Teboho Pitso

39

Teaching Avatars on Controversial Issues: Lessons Learned
Per-Olof Hansson
Marcus Samuelsson
Marie-Louise Höög

61

Moroccan Teachers’ Perceptions and Concerns about ICT Integration
El Mustapha Baytar
Abdelaziz Ettourouri
Nadia Saqri
Lynda Ouchaouka

79

Rethinking Education: An In-Depth Examination of Modern Technologies
and Pedagogic Recommendations
Murielle El Hajj
Hiba Harb

97

Reviewers

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IAFOR Journal of Education: Technology in Education

Volume 11 – Issue 2 – 2023

From the Editors
Another year, another opportunity to explore the complex relationships between technology,
teaching, and learning. And what a year it was! Propelled by lessons learned from the COVID19 pandemic, researchers and practitioners from around the world continue to wrestle with
important matters related to online and blended learning, the emergence of generative artificial
intelligence (AI), and how we scaffold and support learners and teachers. How should we makesense of this ever-changing educational landscape? In some ways, it seems our efforts toanswer this question (and others) feel more pressing and important than ever.
As in year’s past, the current issue of the IAFOR Journal of Education is proud to share fivearticles with readers. Each article probes the possibilities and implications of today’s
educational technologies in their own way and with different target populations. They explore
a range of topics including AI chatbots, virtual simulations, technology integration, andcollaborative and remote education. Together these manuscripts continue the journal’s ongoingcommitment to international scholarship with authors from India, Morocco, South Africa,
Sweden, and Qatar.
What follows is a quick overview of the articles in this year’s technology-focused issue.
The first article is interested in university students’ perceptions of AI chatbots. Do chatbots
have a place in higher education? And, if so, where? In this article, Antony and Ramnath begin
by defining chatbots and then elaborating on their surprisingly long history. From there, theydescribe a qualitative study aimed at understanding the factors that influence student
engagement and support with AI chatbots. Guided by the Unified Theory of Acceptance andUse of Technology, the authors share eight themes revealing the potential of “virtual assistants”
to facilitate communication, enhance engagement, and offer timely support.
Our second article offers readers a South African perspective on what higher education mightlearn about remote learning in the wake of COVID-19. Authored by Pitso, this article describes
a study that used participatory evaluation design to develop a deeper understanding of remotelearning as the “new normal” and to explore its advanced potentialities. Working with
undergraduates (n = 15), the paper promotes a “technology-as-essence” framework, arguingthat remote learning can, and should, lead to greater student control of the learning process.
The study addresses the positive impact asynchronous learning models coupled with advanced
technologies can have on institutions of higher education.
The third article by Hansson, Samuelsson, and Höög explores the impact of using virtual
simulations to help pre-service teachers feel more comfortable teaching controversial issues.
Using conspiracy theories as controversial issues, the authors worked with student teachers (n= 43) in Sweden who were tasked with teaching these theories and critical thinking to virtual
learners in a simulation. The purpose was to create a safe and authentic training situation forpreservice teachers to improve their practice. The results support existing evidence that1

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simulation teaching is a cost-effective and powerful way to provide student teachers
experiences that help them integrate different forms of knowledge (e.g., content, pedagogical).
The fourth article by Baytar, Ettourouri, Saqri, and Ouchaouka examines Moroccan teachers’
concerns about integrating information and communications technology into their teachingpractices. The study used the Concerns-Based Adoption Model with 382 teachers to generate
an overall profile of the teachers’ stages of concern. The results found many teachers fit a
profile of “reluctant” non-users. However, the findings highlighted important relationships
between the teachers’ concerns about technology integration, their technology training, andtheir pandemic experience. Taken together, these findings have important implications for thedesign of interventions meant to address teachers’ individual technology integration concerns.
The final article in this issue is a conceptual piece encouraging readers to think about today’s
educational technology trends and teaching requirements. It challenges all of us to think about
what these suggest about the future of the education enterprise. With these big ideas
established, the authors argue that collaboration should be a central component of how learners
acquire digital skills in an era of AI. They go on to make the case that advanced educational
technologies can be integrated with engaging pedagogical approaches to make it easier thanever for students and teachers to interact and engage collaboratively.
In closing, we hope you find the articles in the current issue enlightening and inspiring as thefield continues its earnest efforts to make high-quality and meaningful education the standard
for all.
Daniel L. Hoffman, Associate Editor, Michael P. Menchaca, Editor, and Devayani Tirthali,
Associate Editor
IAFOR Journal of Education: Technology in Educationtech.editor.joe@iafor.org

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Notes on Contributors
Article 1:
A Phenomenological Exploration of Students’ Perceptions of AI Chatbots in Higher
Education
Ms Soniya Antony
Soniya Antony is a doctoral researcher in education at Alagappa University, Tamil Nadu,
India. Having earned her MEd in Educational Technology from Pondicherry University, shebrings a wealth of knowledge from this esteemed central institution. With the prestigious UGC
doctoral fellowship supporting her studies, she passionately revolves around theidentification, support, and early-stage education of twice-exceptional children, coupled witha keen interest in technology for exceptional learners and gifted education. Her resoluteambition is to contribute impactful research towards the betterment of twice-exceptionalchildren in India, reflecting her commitment to fostering inclusive and effective educational
environments.
Email: soniyaantony4@gmail.com
Dr R. Ramnath
R. Ramnath is an Associate Professor in the field of education at Alagappa University, Tamil
Nadu, India. Having earned his doctoral degree from the same institution, his expertise spans
educational technology. He holds the distinction of being a renowned educational technology
resource person in Tamil Nadu, with a remarkable 15 years of dedicated research experiencein the realm of education. His scholarly pursuits focus on Instructional Technology,
particularly constructivism and pedagogical strategies. His interests also extend to
Educational Technology and Science Education, specifically focusing on science processes.
Notably, he has successfully completed 13 externally funded research projects, further
solidifying his contributions to the field.
Article 2:
Post-COVID-19 Higher Learning: Towards Telagogy, A Web-Based Learning
Experience
Dr Teboho Pitso
Teboho Pitso is a Senior Project Manager in the Centre for Innovation and Entrepreneurship at
Vaal University of Technology, South Africa. He has been a member of various international
associations on teaching, learning and innovation and has published widely in those areas. He
is currently the Quest Editor of the International Journal of Artificial Intelligence, Computer
Science and Robotics Technologies as well as contributes in research projects that advance the
Scholarship of Teaching and Learning and the International Institutes of Informatics andSystematics which now includes infusion of AI systems and innovation in students learning.
Dr Pitso is also working in research projects that seek to contribute to decolonisation ofuniversities in Africa. His latest book is titled The Forgotten: Reconstructing and Reclaiming
African Spirituality in the Post-Truth Era, Sun Press 2023. He is also an editor of the bookContextualised Critical Reflections on Academic Development Practices: Towards
Professional Learning, Sun Press 2021.
Email: biki@vut.ac.za

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Article 3:
Teaching Avatars on Controversial Issues: Lessons Learned
Dr Per-Olof Hansson
Per-Olof Hansson is a senior lecturer at the Department of Management & Engineering,
Linköping University, Sweden. P-O holds a PhD in Educational Research. He is a teacher
educator at undergraduate to master level in Social Science and arrange field studies in Eastern
Africa and Asia for teacher trainees. P-O has been an exchange lecturer at University of
Nairobi several times. His research areas of interests include mobile learning, online teaching
methods, simulation teaching, and artificial intelligence in education.
Email: per-olof.hansson@liu.se
Dr Marcus Samuelsson
Marcus Samuelsson works as Associate Professor of Education at Linköping University. He
is interested in research and development work of relevance to teacher students and in-service
teachers. His research mainly focuses on didactics and classroom management. He has overthe last teen years constructed and conducted research about simulations and simulation
training as a way to support the development of skills and abilities among pre- and in-service
teachers. Marcus is a member of the scientific council of the National Research School invisual pedagogy and crafts pedagogy, FOBOS and member of the School Research Institute's
scientific council, SKOLFI.
Email: marcus.samuelsson@liu.se
Marie-Louise Höög
Marie-Louise Höög holds a teachers’ degree for lower and upper secondary school from
Linköping University, and has also studied acting at Dell’Arte International School ofPhysical Theatre in California. She has 20 years of teaching experience in the subjects of
Swedish, English as a second language, theatre and drama. Currently, she’s a lecturer at thedivision of academic Swedish and working as a trained simulation specialist within a virtual
reality platform used in the Programme for Teacher Education at LiU and other universities
in Sweden as a means to help preservice teachers practice and develop skills and strategies
for handling a complex reality while teaching students.
Email: marie-louise.hoog@liu.se
Article 4:
Moroccan Teachers' Perceptions and Concerns about ICT Integration
El Mustapha Baytar
El Mustapha Baytar is a PhD student in Educational Sciences within the Multidisciplinary
Laboratory in Educational Sciences and Training Engineering, Higher Normal School, Hassan
II University of Casablanca, Morocco.
Email: elmustapha.baytar@enscasa.ma
Abdelaziz Ettourouri
Abdelaziz Ettourouri is a PhD student in Educational Sciences within the Multidisciplinary
Laboratory in Educational Sciences and Training Engineering, Higher Normal School, Hassan
II University of Casablanca, Morocco.
Email: abdelaziz.ettourouri@enscasa.ma

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Dr Nadia Saqri
Nadia Saqri is a Professor accredited to supervise research in the Higher Normal School at
Hassan II University of Casablanca, Morocco. She serves as the Head of the Languages andEducational Sciences Department. She is a Master coordinator and a coach.
Email: n.saqri@enscasa.ma
Dr Lynda Ouchaouka
Lynda Ouchaouka is an Assistant Professor in the Higher Normal School at Hassan II
University of Casablanca, Morocco. She is a Digital Learning Consultant. She is a member ofthe Multidisciplinary Laboratory in Educational Sciences and Training Engineering in the same
institution.
Email: l.ouchaouka@enscasa.ma
Article 5:
Rethinking Education: An In-Depth Examination of Modern Technologies
and Pedagogic Recommendations
Dr Murielle El Hajj
Murielle El Hajj holds a PhD in French Language and Literature from the Lebanese University,
Lebanon. She is currently Assistant Professor and Director of Lusail University Press (LU
Press) at Lusail University, Qatar. She is also Associate Editor of the IAFOR Journal of
Literature & Librarianship and the IAFOR Journal of Education (Language Learning in
Education issues), The International Academic Forum (IAFOR), Japan, as well as Editorial
Board Member of In Analysis, revue transdisciplinaire de psychanalyse et sciences, Elsevier
Masson SAS, France. Her domain of research focuses on psychoanalysis of literature, theperspective on the unconscious in literary study, the roles of the instances involved inanalytical/critical praxis, and the relation between literature and psychoanalysis. She has
published book chapters, original articles, academic interviews, translated articles, bookreviews and analyses, as well as poems. She has also authored a contemporary theater booktitled “Fragments chaotiques” and published by Les impliqués Éditeur, France.
Email: mnahas@lu.edu.qa
Dr Hiba Harb
Hiba Harb holds a PhD in Language Sciences from the Artois University in France, and amaster’s degree in education and training from the Lebanese University in Beirut. She iscurrently Assistant Professor and Director of the Training and Continuing Education Center at
Lusail University. She is also President of the Association of French Language Teachers in
Qatar. She has more than ten years of teaching experience at a university level, she has taught
English and French courses at Qatar University, the Lebanese University, the University ofDoha for Science and Technology, and the Community College of Qatar. Her research interests
focus on educational technologies, pedagogical approaches, language teaching, course designand development, professional development, and communication.
Email: hharb@lu.edu.qa

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A Phenomenological Exploration of Students’ Perceptions of AI Chatbots
in Higher Education
Soniya Antony
Department of Education, Alagappa University, Karaikudi, India
R. RamnathDepartment of Education, Alagappa University, Karaikudi, India

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Abstract
This study examines the impact of AI chatbots as a communication medium on student
engagement and support in higher education. The qualitative method and Interpretative
Phenomenological Analysis (IPA) were employed as the research approach, utilizing in-depthsemi-structured interviews. Purposive sampling was used to select 11 participants from thestate of Kerala, India, in higher education. Data analysis followed the Systematic Text
Consideration (STC), a five-step process, including framing meaning units, condensing
meaning units, coding, creating sub-themes, and deriving themes. By exploring themes alignedwith the UTAUT2 constructs, a comprehensive understanding of the factors influencing
student engagement and support was achieved. A total of eight themes were identified,encompassing “Effectiveness and Limitations,” “Beyond,” “Enrichment,” “Optimization,”“Synergize,” “Streamlining Communication,” “Engage+AI,” and “Refine.” These themes
provided compelling evidence of the transformative potential of AI chatbots in facilitating
effective communication, enhancing engagement, and offering timely support. The study’s
results carry significant practical implications for higher education institutions. Embracing AI
chatbots, universities and institutions can enhance student engagement and support throughefficient communication, personalized recommendations, and streamlined interactions. These
chatbots offer a balance between quick assistance and human expertise, optimizing both routine
tasks and complex inquiries. Additionally, addressing security and privacy concerns is crucial
to fostering trust and successful integration. Overall, embracing AI chatbots can transform theeducational experience, making it more efficient, engaging, and supportive for students inhigher education.
Keywords: chatbots, AI chatbots, higher education, Interpretative Phenomenological Analysis,
UTAUT28

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In contemporary society, the pervasive presence of chatbot technology is evident across various
domains, ranging from smart home speakers to workplace messaging applications. Theseadvanced Artificial Intelligence (AI) chatbots, often denoted as “virtual assistants” or “virtual
agents,” can operate through diverse mediums. They can be engaged through audio input, as
exemplified by Apple’s Siri, Google Assistant, and Amazon Alexa, or facilitate interactions
via text-based platforms such as SMS, ChatGPT, and Google Bard. This accessibility
empowers individuals to engage in natural conversations with chatbots, eliciting information
and refining inquiries through interactive responses and follow-up interactions. Amid the risingprominence of AI technologies, attention has shifted towards their potential to elevate student
engagement and support within higher education. A specific application of AI that showcases
substantial promise is AI chatbot communication. AI chatbots offer personalized and prompt
assistance, enabling students to efficiently access information and resources. However, theeffective implementation of AI chatbots in higher education necessitates a profoundunderstanding of how students perceive and interact with such communication tools. The
integration of AI chatbots into higher education has gained considerable traction as amechanism to enhance student engagement and academic achievement (Pérez et al., 2020;Studente et al., 2020). The literature demonstrates an increasing interest in leveraging chatbots
to deliver efficient services to students (Pérez et al., 2020) while concurrently bolstering their
engagement (Studente et al., 2020). These trends underline the broader acknowledgment of AI
chatbots’ potential in education, fostering personalized support and facilitating interactive andresponsive learning experiences. Moreover, students perceived chatbots and instant-messaging
services as valuable for communicating with course directors and obtaining necessary support(Abbas et al., 2022).
This research is guided by two main research questions. These questions delve into students’
perceptions and preferences regarding AI chatbots in higher education: RQ1: How do students
perceive the role of AI chatbots in enhancing student engagement and support in higher
education? What factors contribute to their opinions about the helpfulness and effectiveness of
AI chatbots communication? RQ2: What key factors influence students’ preferences for using
AI chatbots as a means of communication in higher education? How do concerns about
security, privacy, and other factors impact their decision to adopt AI chatbots for addressing
inquiries related to admissions, courses, academics, and library references? The first question
scrutinizes students’ perspectives on AI chatbots in higher education, specifically examining
their contributions to student engagement and support. It aims to unearth the factors shaping
students’ views on the effectiveness and utility of communicating with AI chatbots in
educational contexts. This understanding is paramount for educational institutions to glean
insights into the advantages and limitations of incorporating chatbot technology, optimizing
both student engagement and support. The second question focuses on the factors influencing
students’ inclination towards using AI chatbots for communication in higher education. As AI
chatbots continue their ascendancy, comprehending students’ perspectives becomes pivotal for
their effective integration and alignment with students’ needs. The significance of this study
lies in its potential to enrich the realm of higher education by providing insights into the impact
of AI chatbots on student engagement and support. However, it’s important to acknowledge
that students’ perceptions of AI chatbots can vary, and concerns encompassing privacy,

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reliability, and impersonal interactions have emerged. In bridging these research gaps, thisstudy employs qualitative interpretive phenomenology (Smith et al., 2009) to delve into
students’ experiences, perceptions, and attitudes, when interacting with AI chatbots in higher
education. Through the analysis of interview transcripts, this research aims to unveil thesophisticated factors that influence students’ perceptions, experiences, and attitudes towards
AI chatbots. These findings will offer valuable insights to guide the design and implementationof AI chatbots, optimizing their potential to amplify student engagement and support in higher
education.
The structure of this paper is organized as follows: Commencing with a succinct overview ofthe historical evolution of chatbots, emphasizing their relevance in higher education, thesubsequent section embarks on a comprehensive literature review. This review encapsulates
the existing research on AI chatbots in higher education, traversing their potential advantages
and attendant concerns. Additionally, the theoretical framework underpinning this study, theUnified Theory of Acceptance and Use of Technology (UTAUT2), is introduced (Venkatesh
et al., 2003, 2012). The methodology section delineates the research design, participant
selection process, interview procedures, ethical considerations, and the qualitative data analysis
approach Systematic Text Condensation (STC). The results and discussion sections
encapsulate the emergent themes and codes drawn from students’ experiences and perceptionsof AI chatbot communication within higher education. These themes are compiled with the
UTAUT2 model constructs. Ultimately, the conclusion synthesizes key findings,
acknowledges the study’s limitations, and furnishes recommendations to optimize AI chatbots’
deployment for enhancing students’ engagement and support. This study aspires to augment
current understanding of AI chatbot communication within higher education through an indepth exploration of the student experience. Through this exploration of students’ perceptions,attitudes, and lived experienced, this research yields valuable insights into the potential impact
of AI chatbots on student engagement and support. These insights will pave the way for theformulation of effective strategies and guidelines for the seamless integration of AI chatbots,
ultimately enhancing the educational experience and academic attainment of university
students.
Review of LiteratureThis literature review encompasses three main areas: the history of chatbots, AI chatbots usedin education, and AI chatbots as a communication medium in higher education. It explores theexisting literature and empirical evidence surrounding these topics, as well as sorts out thepotential benefits and concerns associated with the use of AI chatbots in educational settings.
History of Chatbots
A chatbot is a computer program that engages in text or voice-based interactions, simulating
human-like conversation and comprehending multiple human languages through theapplication of Natural Language Processing (NLP) (Khanna et al., 2015). According to Lexico
Dictionaries, a chatbot is described as “A computer program designed to simulate conversation

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with human users, especially over the Internet.” These versatile entities are also recognized byvarious terms, including smart bots, interactive agents, digital assistants, or artificial
conversation entities. The concept of chatbots dates to the mid-20th century when Alan Turing
proposed the Turing test to evaluate machine intelligence (Turing, 1950). In 1966, theMassachusetts Institute of Technology (MIT) developed the first known chatbot called ELIZA,
which used a pattern matching rule system to generate responses based on specific keywords(Mekni et al., 2020). ELIZA simulated a non-directional psychotherapist and employedtemplate-based responses (Mekni et al., 2020). Another significant chatbot was PARRY, which
was developed in 1972 and simulated a patient with schizophrenia (Colby et al., 1972). PARRY
exhibited a personality and responded based on assumptions and emotional reactions triggeredby user input (Colby et al., 1972). Artificial Intelligence (AI) entered the chatbot domain withthe creation of Jabberwacky in 1988. This chatbot utilized Clever Script and contextual patternmatching. The term “Chatterbot” was first used in 1991 to refer to an artificial player in the
TINYMUD virtual world (Mauldin, 1994). In 1995, Wallace introduced the Artificial
Linguistic Internet Computer Entity (A.L.I.C.E) architecture, which distinctly separates the“chatbot engine” and the “language knowledge model.” This separation allowed for theseamless integration of different language knowledge models providing a plug-and-playcapability (AbuShawar & Atwell, 2015). A.L.I.C.E gained popularity in 2001, utilizing theArtificial Intelligence Mark-up Language (AIML) and featuring a substantial knowledge base.
Advancements in NLP and AI technologies led to the emergence of rule-based chatbots like
A.L.I.C.E. in the 1990s (Powton, 2018; Dale, 2016).
In 2001, SmarterChild revolutionized chatbots technology by providing practical assistance tousers by retrieving information from databases (Molnár & Zoltán, 2018). In more recent years,
voice-activated personal assistants like Siri, Watson Assistant, Google Assistant, Cortana, andAlexa gained popularity for managing tasks and engaging in conversation (Powton, 2018; Dale,
2016). Social media platforms facilitated the development of chatbots for various purposes,
and by the end of 2016, around 34,000 chatbots existed across different fields (Powton, 2018).
Integrating chatbots with the Internet of Things (IoT) improved communication between
connected smart objects (Kar & Haldar, 2016). Generative Pre-trained Transformers (GPT) are
advanced neural network models with a transformer architecture, revolutionizing AI for tasks
like ChatGPT. These models create human-like text, images, and music, excelling in Q&A,
summarization, content creation, and search across industries. The GPT series includes GPT1 (2018), GPT-2 (2019), GPT-3 (2020), and GPT-3.5 (2022), each with enhanced capabilities.
GPT-4 (2023) further expands with text prediction and reinforcement learning from human
feedback, accepting text and images (Vincent, 2019; Tom et al., 2020).
AI Chatbots in Education
An AI chatbot, also referred to as a virtual assistant or virtual agent, is a sophisticated computer
program specifically designed to take advantage of AI and advanced NLP techniques. Its
primary function is to understand and interpret customer queries, and subsequently provide
relevant and contextually accurate responses. By mimicking human conversation patterns, an
AI chatbot engages in interactive and dynamic dialogues, offering effective communication

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and assistance to users. These advanced chatbots can engage in conversations through text orvoice, and they are designed to interact with users in a conversational manner, similar to how
a human would communicate (Caldarini et al., 2022). Powered by machine learning and natural
language processing, AI chatbots are capable of learning from interactions and adapting their
responses over time (Google AI, 2022). In the past, chatbots were primarily text-based andprogrammed to provide answers to specific questions predetermined by developers,
functioning as interactive FAQs. However, modern AI chatbots go beyond this limitation,
maintaining dynamic conversations, and handling complex queries by utilizing deep learningand natural language processing techniques. These chatbots serve various applications,
including customer service, information retrieval, and assisting users in a conversationalmanner (Russell & Norvig, 2010). AI chatbots serve as intelligent software applications thatemulate human-like conversations through text or voice interactions, leveraging AI
technologies to enhance user engagement and provide accurate and contextually relevant
responses (Caldarini et al., 2022; Google AI, 2022; Russell & Norvig, 2010).
The application of AI in the field of education is expanding drastically. Chatbot systems standout among the prevalent AI technologies used to support teaching and learning (Okonkwo &Ade-Ibijola, 2020). Evidence that they can enhance student interaction is on the rise (Okonkwo& Ade-Ibijola, 2021). Chatbots are regarded as valuable educational instruments for enhancing
the teaching and learning process (Clarizia et al., 2018) in higher education by providing
personalized and efficient support to students (Cunningham-Nelson et al., 2019). Given theprevalence of smart phones among students in higher education, chatbot systems can be
deployed effectively as mobile web applications to facilitate learning. These chatbots offer
instantaneous access to standardised information such as efficient and timely services (Pérez
et al., 2020), course content (Cunningham-Nelson et al., 2019), study materials (Okonkwo &Ade-Ibijola, 2021), practice questions and answers (Okonkwo & Ade-Ibijola, 2021; Ranoliya
et al., 2017; Sinha et al., 2020),evaluation criteria (Okonkwo & Ade-Ibijola, 2021), assignment
due dates, and advice (Ismail & Ade-Ibijola, 2019; Okonkwo & Ade-Ibijola, 2021). They canalso help streamline administrative tasks (Studente et al., 2020), provide campus path direction(Mabunda & Ade-Ibijola, 2019; Okonkwo & Ade-Ibijola, 2021) and augment student
engagement (Studente et al., 2020). Some institutions also introduce chatbots to alleviate costs
associated with student administration (Abbas et al., 2022). By providing such comprehensive
support, these systems not only improve student engagement and academic support but also
substantially reduce the administrative workload and burden of lecturers, allowing them toconcentrate on curriculum advancement as well as research (Cunningham-Nelson et al., 2019).
Moreover, AI chatbots can streamline administrative tasks in educational institutions. The
integration of chatbots in university admissions processes found that they efficiently handledinquiries related to admissions requirements, deadlines, and application status. This freed
administrative staff to focus on more complex tasks (Green & Johnson, 2021). While AI
chatbots in education offer numerous benefits, further research is necessary to address ethicalconsiderations, understand their impact on student motivation, and develop more advanced
conversational capabilities. Addressing these challenges, educational institutions can leverage
the potentials of AI chatbots to create more engaging and efficient learning environments forstudents (Green & Johnson, 2021).

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AI Chatbots as a Communication Medium in Higher Education
AI chatbots have emerged as a promising communication medium in higher education (Kooli,2023), providing personalized assistance and support to students (Cunningham-Nelson et al.,2019). These chatbots emulate human-like conversations, employing natural language
structures (Pham et al., 2018) through text messages on websites or mobile applications, voicebased interactions (Alexa or Siri), or a combination of both (Pereira et al., 2019). Functioning
as automated conversational agents, they have gained significant popularity in replicating
student service interactions across various domains in higher education (Khan et al., 2019;
Wang et al., 2021). Tlili et al. (2023) explored how conversational AI, like ChatGPT, might
improve online learning. Due to the engagement and interactivity, students preferred AI
chatbots as conversational agents for learning activities. Kuhail et al. (2022) found that chatbots
might give students immediate feedback, personalized support, and self-directed learning
experiences, increasing engagement and motivation. Based on the study of Studente et al.
(2020), it is reported that the usage of chatbots in higher education, notably among first-year
students, eased university transitions, increased academic engagement, and encouraged peer
connection. Chatbots also helped students contact course directors to seek the required support
on time.
Employing chatbots as communication tools, researchers bolster collaboration, enriching
information exchange and refining research quality. Chatbots amplify cooperative efforts,
streamlining information dissemination and enhancing synergy among researchers (Kooli,2023). Notably, Kooli (2023) meticulously examined the design and integration of a chatbots,
specifically tailored for student-teacher interaction within an online university platform.
Remarkably, this chatbot seamlessly aided students in comprehending course content andfulfilling assignments, garnering widespread approval and recognition as an invaluable meansof facilitating teacher-student communication. The research highlighted the chatbots’ latentcapacity to elevate student-teacher interaction, consequently augmenting the broader realm of
the learning experience (Dwivedi et al., 2023; Mendoza et al., 2020). This illuminates chatbots’
pivotal role as a transformative communication medium, poised to revolutionize collaborative
dynamics and learning outcomes (Kooli, 2023). The use of AI chatbots in delivering mental
health support has also been shown to effectively reduce student stress levels (Liu et al., 2022).
Additionally, AI chatbots have demonstrated value in providing academic advising andcounselling services, acting as virtual tutors to offer personalized guidance and feedback.
Implementation of AI chatbots, such as “Laurie” at Georgia State University, has improved
student engagement and facilitated peer-to-peer communication (Watson et al., 2022). These
chatbots have primarily focused on providing course-related information support, serving as
online tutors and reducing teachers’ workload (Lee et al., 2020). They can also assist in
identifying at-risk indicators among students and offer university-related information support,
acting as intelligent assistants to improve university services and reduce labour costs (Mekni
et.al. 2020; Touimi et al., 2020; Hien et al., 2018). In addition, AI chatbots can extract
information from university knowledge bases and provide assistance with admissions-related
queries, supporting the academic admissions process. The integration of AI chatbots as a

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communication medium in higher education offers numerous advantages. These intelligent
agents can provide quick and accurate responses to student queries, support the delivery ofcourse materials, offer academic advising, and enhance campus engagement. Moreover, they
have the potential to reduce the workload of teachers and administrative staff, enabling them
to focus on more complex tasks (Lee et al., 2020; & Touimi et al., 2020).
Theoretical Framework
The Unified Theory of Acceptance and Use of Technology 2
The Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework developed
by Venkatesh et al. (2012) is an extension of the original UTAUT model developed by
Venkatesh et al. (2003). UTAUT2 incorporates additional constructs to further enhance theunderstanding of technology acceptance and usage behavior. It also considers the complex
interplay of individual, social, and contextual factors that influence technology adoption. This
framework can be applied to investigate the impact of AI chatbots as a communication medium
for student engagement and support in higher education. The UTAUT combines eightfoundational models, enhancing technology acceptance and understanding (Venkatesh et al.,2003). Evolving from diverse fields’ theories, UTAUT casts historical illumination ontechnology adoption’s user intentions (Yu, 2012). The UTAUT framework incorporates core
constructs: performance expectancy, effort expectancy, facilitating conditions, and social
influence. However, an expanded UTAUT2 version emerged in 2012, integrating three new
constructs: hedonic motivation, habit, and price value (Venkatesh et al., 2012). The important
constructs of UTAUT2 for the present study are described below.
Performance Expectancy
The Performance Expectancy (PE) construct has been described as the degree to which
employing a technology yields benefits in task execution (Venkatesh et al., 2003). Hence, it
signifies the extent to which individuals gauge an AI chatbot’s potential to amplify
performance and productivity. PE encapsulates the anticipated outcome or extrinsic motivation
linked with usage. Studies acknowledge the impact of AI chatbot adoption as a communication
medium in higher education (Vimalkumar et al., 2021).
Effort Expectancy
Effort Expectancy (EE) is defined as a degree of ease (Venkatesh et al., 2003), which reflects
the perceived ease of using chatbots. EE predicts technology adoption in education (Wirtz etal., 2019). The aim of any new technology is a favorable user perception of ease (Venkatesh etal., 2012), where self-confidence in technical competence impacts usage intent. EE gauges user
friendliness, convenience, and confidence when interacting with AI chatbots. Ease and minimal
cognitive effort influence students’ technological intent. EE crucially shapes AI chatbots’
adoption, aligning user-friendly interfaces with positive intent (Wirtz et al., 2019; Venkatesh
et al., 2012).

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Social Influence
Social Influence (SI) is defined as the “extent to which students perceive significant others
believe they should use a particular technology” (Venkatesh et al., 2003). SI-driven variable
models associate beliefs and behaviour with compliance, internalization, identification(Moriuchi, 2021). Peers, teachers, and institutional norms impact individuals’ intentions to use
AI chatbots through social influence. Social norms and favourable opinions affect the adoptionof AI chatbots in education. According to Venkatesh et al. (2003), social influence is how
students view significant others. Moriuchi (2021) explained social influence on technology
usage through compliance, internalisation, and identification. They also highlighted theimportance of SI in educational technology acceptance.
Facilitating Conditions
Facilitating Conditions (FC) refer to the resources that are accessible for engaging in aparticular behaviour (Venkatesh et al., 2003). These resources are associated with acceptance,
which is determined through self-assessment (Wang et al., 2021). The presence of a strong
support infrastructure is crucial for the effective utilisation of technology, particularly in thecase of AI-based tools such as chatbots. The study conducted by Vimalkumar et al. (2021)
provided further evidence supporting the significance of facilitating conditions in the adoptionof digital voice assistants. The adoption of AI chatbots is influenced by various factors, suchas technical support, availability of resources, and institutional policies (Vimalkumar et al.,2021).
Hedonic Motivation
Hedonic Motivation (HM) refers to the inclination towards engaging in technology-related
activities purely for the enjoyment they provide, without any explicit utilitarian benefits(Venkatesh et al., 2012). HM utilises enthusiasm and optimism to enhance AI chatbot
interactions (Brown & Venkatesh, 2005). The experience of users is influenced by factors suchas novelty, interactivity, and entertainment. According to Venkatesh et al. (2012), within thecontext of higher education, the engagement and motivation of chatbots are stimulated by HM.
Price Value
The concept of Price Value (PV) is used to assess the net benefits derived from technology, as
discussed by Venkatesh et al. (2012). Price value pertains to the assessment of costeffectiveness and the value for money when utilizing AI chatbots (Moorthy et al. 2019). It
considers the perceived usefulness relative to the financial investment or effort required,helping individuals evaluate the benefits and drawbacks of using AI chatbots (Palau-Saumell
et al., 2019).

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Habit
Habits are automatic, learned actions (Venkatesh et al., 2012). Habitual use of AI chatbots
refers to the automatic and routine engagement with these communication mediums, driven bypast behaviours and without conscious decision-making (Jacucci et al., 2014). Habits represent
a pattern of regular and ingrained utilization of AI chatbots (Perez-Vega et al., 2021).
The UTAUT2 framework provides a comprehensive understanding of technology acceptanceand usage behavior by considering these seven key constructs. It recognizes the influence ofindividual beliefs, social factors, and contextual conditions in shaping users’ acceptance andadoption of technology. Empirical evidence from various studies supports the validity andeffectiveness of the UTAUT2 framework. Researchers have applied UTAUT2 in diverse
settings, including e-learning, mobile learning, and AI-based systems, and have consistently
found significant relationships between the constructs and technology acceptance. Studies
provide empirical support for the UTAUT2 framework, reinforcing its utility in understanding
individuals’ technology acceptance and usage behavior in different educational contexts.
Methodology
This study employed a qualitative methodology to examine the influence of an AI chatbot on
student engagement and support within higher education. Specifically, an Interpretive
Phenomenological Analysis (IPA) was employed to analyze collected data and derive findings
in response to the research questions. IPA, which has gained prominence across diverse
academic fields, is recognized for its value in investigating existential experiences (Finlay,
2011). Offering a versatile and adaptable approach, IPA serves as a promising method forcomprehending individuals’ lived experiences (Smith et al., 2009). IPA has the potential tounderstand and interpret people’s experiences, facilitated by its practical and accessible
guidelines (Shinebourne & Smith, 2009a, 2010; Smith & Osborn, 2003). It is essential,
however, to acknowledge IPA’s methodological limitations and carefully consider them in its
application.
Participant Selection, Sample Size and Techniques
The present study examined the influence of an AI chatbot on student engagement and support
within higher education. The study had a specific focus on the perceptions of students from
Kerala, India. This decision was made to gather comprehensive data from this population andgain a nuanced understanding of the phenomenon under investigation. The target populationconsisted of students who were interviewed or approached to respond to open-ended selfadministered questions. A purposive sampling technique was used to collect data, resulting ina sample of 11 participants. The sample included seven (64%) males and four (36%) females.
Additionally, the sample encompassed a diverse range of educational backgrounds, including
five (45%) students who were pursuing postgraduate studies and six (55%) individuals who
were engaged in university and doctoral research in education, management, and science. The

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selected participants possessed relevant knowledge pertaining to the phenomenon underinvestigation, making them well-suited for the study.
Prior to conducting the interviews, the research team obtained informed consent and assessed
the participants’ willingness to participate in the study. Permission was also sought from theuniversity dean to conduct the research. The researcher adhered to the Committee on
Publication Ethics guidelines throughout the research process. Participants were assured of theconfidentiality of their personal information, and steps were taken to ensure the privacy andsecurity of their data.
Interview Process
In conducting the interviews for this study, it was important to maintain a focus on listening to
the participants and understanding the meanings associated with their experiences, rather thansimply seeking direct answers to predetermined questions (Roberts, 2020). The interviews
were conducted face-to-face between mid-March and the first week of April 2023. Eachinterview lasted approximately 40 to 50 minutes. Eight semi-structured questions wereprepared beforehand, covering topics related to general chatbot usage, chatbot usage at their
institution or university, and expectations of AI chatbot functionality. Additionally, personalquestions such as the participant’s course of study, type of institution, and years of experience
using AI chatbot were included. Some spontaneous questions were also asked during theinterviews to clarify responses.
To maintain structure during the interviews, the list of questions was used as a reference, but
the researcher allowed for flexibility and deviation from the list to explore important topics
further (Roberts, 2020). While specific probes were not written down, the researcher employed
them during the interviews to keep the participants engaged, summarize the topics, manage theflow of the conversation, and ensure understanding. Probes such as “Please continue,” “That’s
intriguing, could you provide further details?” or “Could you revisit and explain more about?”
were used. The purpose of the probes was to maintain engagement, summarize key points,
ensure a smooth conversation flow, and check for comprehension, all in accordance with
Roberts’ (2020) suggestions. By following these interview techniques, the researcher aimed toelicit rich and detailed responses from participants, allowing them to share their expertise andknowledge of their experiences using AI chatbots. This approach aligned with Roberts’ (2020)
recommendations regarding the importance of listening to participants and viewing them as
experts in answering research questions.
Data Analysis
The study employed the Systematic Text Condensation (STC) method, a widely utilized
approach within the framework of IPA, to analyse interview data. The application of the STC
method involved a structured five-step process: identifying and extracting distinct segments oftext as Meaning Units, distilling and paraphrasing these into Condensed Meaning Units,assigning codes for organization, aggregating related codes into Sub Themes that highlight17

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patterns, and ultimately synthesizing these into overarching Themes (Malterud, 2012). A
comprehensive explanation of this method, complete with illustrative details, is available in the
Appendices section of the paper.
Results
The exploration of students’ perspectives of AI chatbots uncovered eight distinct themes. These
themes included Dual (Effectiveness and Limitations), Beyond, Enrichment, Optimization,
Synergize, Streamlining Communication, Engage+AI, and Refine. Each theme provided
insight into AI chatbots’ multifaceted functions within the realm of higher education andbeyond. These themes provide valuable insight into the dynamic interplay between AI chatbots
and the educational landscape.
The theme of “Dual Effectiveness and Limitations” explores the contrasting aspects of AI
chatbots, specifically their ability to provide quick assistance and their limitations in addressing
complex issues. For example, Respondent 3 stated, “Personally, I find chatbots quite helpful.
They offer convenience by eliminating the need to wait for customer service representatives.
Chatbots provide instant assistance and guide me through various processes, like my PhD
admission process, making interactions efficient and hassle-free.” Respondent perspectives
connected to the theme by exemplifying the contrasting aspects of chatbots’ effectiveness inproviding quick assistance and their limitations in addressing more intricate matters, such as
those encountered during a PhD admissions process. This duality highlighted the necessity of
adopting a well-balanced approach that maximizes the advantages of both elements while
recognizing and addressing their respective drawbacks, thereby fostering a strategic andeffective utilization strategy (Green & Johnson, 2021; Okonkwo & Ade-Ibijola, 2021).
Transitioning to the theme of “Beyond,” a deeper examination revealed the extended influenceof AI chatbots beyond the traditional boundaries of education. The subthemes of “Smooth User
Experience” and “Communication Challenges” provided perspective on the effortlessinteractions encountered by students, contrasted with potential obstacles in communication(Kooli, 2023; Pereira et al., 2019). For instance, Respondent eight explained, “Absolutely, AI
chatbots have gone beyond just assisting with education. They contribute to a much broader
impact. For instance, the Smooth User Experience they provide is remarkable. I find it easy tointeract with them, accessing information and resources seamlessly. This makes my academic
journey smoother.” This participant recognized AI chatbots’ influence beyond education. Thisenhancement in the academic journey signified the “Beyond” theme, where chatbots go beyond
norms to elevate user experiences. Another illustration comes from Respondent 11 who
explained, “Of course, while the experience is generally smooth, there are times when theCommunication Challenges emerge. AI Chatbots might struggle to understand complex
medical queries or interpret specific nuances. This can lead to misunderstandings or incomplete
responses. So, while they excel in many ways, there’s room for improvement in certain areas.”
The respondent pointed out that despite the overall smoothness of their interaction with AI
chatbots, they’ve encountered instances of “Communication Challenges.” These challenges
arose when chatbots face difficulty comprehending intricate medical queries or grasping

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specific nuances. Consequently, this can result in misunderstandings or responses that lackcompleteness.
While AI chatbots have strengths, the participants highlighted their limitations, indicating
potential for enhancement in specific aspects. The theme explored the transformative potentialof AI chatbots to surpass traditional roles and expand the scope of engagement. The focus of
this discussion centered on the concept of “Enrichment,” with particular attention given tocomprehensive improvements AI chatbots offer to individuals’ daily experiences. This led tothe subtheme of “Personalized Recommendations.” This subtheme was acknowledged by one
respondent who stated, “Let’s say I’m looking for research materials. The chatbots not only
help me find relevant resources but also suggest related readings based on my past searches.
It’s like having a study partner who knows my needs.” Another subtheme of “Real-Time
Updates and Navigation” enhanced the personalized and timely assistance offered, enhancing
both recreational activities and regular tasks (Cunningham-Nelson et al., 2019). This theme
encompasses the ability of AI chatbots to enhance user experiences by providing personalized
and dynamic assistance.
Advancing to the theme of “Optimization,” attention is drawn to the refined communication
processes facilitated by AI chatbots. The subthemes of “Enhanced Communication
Experience” and “Streamlined Efficiency” are indicative of heightened engagement andprompt response times, as evidenced by Kuhail et al. (2022) and Lee et al. (2020). This theme
reflected AI chatbots’ potential to optimize communication, ultimately heightening user
satisfaction and experiences.
The theme of “Synergize” emphasized the harmonious integration of AI chatbots with humanelements. Addressing concerns about “Data Security and Confidentiality” and “Reliability and
Accuracy,” this theme advocated for a collaborative utilization approach (Touimi et al., 2020;
Wang et al., 2021). “Human Interaction” and “Personalization” contributed to a holistic
interaction paradigm, emphasizing mutual reinforcement and effectiveness. Respondent 7 for
instance, noted, “Well, it’s a delicate balance. AI chatbots can streamline tasks and provide
quick assistance, but when it comes to personal or confidential matters, human expertise is
crucial. We need to ensure that sensitive data is handled securely and that there’s a humantouch for situations that require empathy and understanding.”
The theme of “Streamlining Communication” explored the symbiotic relationship between AI
chatbots and human engagement. While “Efficient and Accurate Responses” enhanced
interactions, “Limitations in Complex Inquiries” underscored areas where human judgment
remains vital. “Complementary Role with Human Support” suggested that AI chatbots canenhance human resources for specialized tasks, emphasizing seamless coexistence (Khan et al.,2019). The central focus of the “Engage + AI” theme accentuated collaborative interactions
between students and AI chatbots, illustrated by a Respondent 5 who said, “From myexperience, I’ve noticed how students and AI, like ChatGPT, team up. For example, when I
needed insights for financial analysis, ChatGPT quickly gave me relevant information, making
my work more effective.”

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Subthemes like “Convenience and Efficiency” and “Personalization and Recommendations”
exemplified AI chatbots’ ability to stimulate student engagement (Kuhail et al., 2022; Tlili etal., 2023). Additional subthemes, such as “Trust and Social Influence” and the role of being a“Reliable and Knowledgeable Resource” were exemplified by Respondent 7 who explained,
This whole interaction between scholars and AI is interesting. For instance, when I needed
advice for survey design, ChatGPT’s reliable insights boosted my engagement and helped
improve my research quality.” These elements depicted AI chatbots as catalysts for creating
immersive user experiences.
Finally, the theme of “Refine” encompassed the iterative process of enhancing AI chatbots.
Subthemes such as “Natural Language Understanding and Adaptability” and “User Experienceand Interface Design” underlined ongoing improvements. “Speed and Accuracy” highlighted
the importance of promptness. This theme signified the continuous refinement of AI chatbots’capabilities, ensuring sustained user satisfaction and usability (Tlili et al., 2023).
To summarize, the presence and potential of AI chatbots in higher education are made clear byeight themes, together they portray AI chatbots as dynamic agents of transformation andinnovation. Through theoretical consistency and conceptual alignment, the UTAUT2 model’s
constructs with identified themes and subthemes provided a comprehensive framework forunderstanding AI chatbots’ multifaceted contributions and how they might shape the future forstudents in higher education.
Study Themes Collated with the UTAUT2 Constructs
The connection between the identified themes and the UTAUT2 (Venkatesh et al., 2003;
Venkatesh et al. 2012) constructs was established by a meticulous assessment applying two
rigorous criteria: theoretical consistency and conceptual alignment. The theoretical consistencyof the UTAUT2 model was demonstrated through its integration of established theories fromdiverse It aligns with principles of technology acceptance and adoption, ensuring logicalsoundness and relevance (Lee & Rho, 2013; Venkatesh et al., 2003 & 2012). The UTAUT2
model achieves robust conceptual alignment by seamlessly integrating and extending corecomponents from established technology adoption theories such as Technology Acceptance
Model (Davis, 1989), the Theory of Planned Behavior (Ajzen, 1991), and the Innovation
Diffusion Theory (Rogers, 1961). Through this synthesis of constructs, UTAUT2 offered acomprehensive framework for understanding the multifaceted factors shaping technology
acceptance, while also accommodating contextual intricacies. This alignment enhanced its
practical relevance and theoretical robustness, contributing to its prominence in contemporary
research (Lee & Rho, 2013; Venkatesh et al., 2003; Venkatesh et al., 2012).
Integration of the UTAUT2 Constructs with Identified Themes
The analysis of AI chatbots themes and subthemes in the context of UTAUT2 (Venkatesh etal., 2003; Venkatesh et al. 2012) constructs revealed a strong theoretical consistency andconceptual alignment. The “Dual (Effectiveness and Limitations)” theme, including subthemes

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“Efficiency and Time-saving”, “Skepticism and Limitations” and “Convenience andGuidance” aligned with Performance Expectancy (PE), capturing users’ expectations ofbenefits and concerns. The “Beyond” theme, with subthemes “Smooth User Experience” and“Communication Challenges,” corresponded to Effort Expectancy (EE), highlighting
convenience and potential difficulties. “Enrichment” subthemes like “Personalized
Recommendations” related to Hedonic Motivation (HM), focusing on enjoyment andenhancement. “Optimization” subthemes such as “Enhanced Communication Experience,”aligned with Performance Expectancy (PE) by emphasizing improved task execution. The“Synergize” theme, featuring subthemes like “Data Security and Confidentiality,” aligned withSocial Influence (SI), reflecting the influence of norms. The “Streamlining Communication”
subtheme tied to Performance Expectancy (PE) and Social Influence (SI), while the“Engage+AI” subthemes, including “Trust and Social Influence,” aligned with Performance
Expectancy (PE) and Social Influence (SI). Finally, the “Refine” theme, with subthemes“Natural Language Understanding and Adaptability,” aligned with Effort Expectancy (EE).
This analysis demonstrated the UTAUT2 model’s robustness in capturing AI chatbots’
adoption complexities.

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Figure 1
Integration of the UTAUT2 Constructs with Identified Themes

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Discussion
The discussion section comprehensively addresses the main research questions, which are
divided into two distinct segments. The first segment examines into the perceptions
surrounding the augmentation of student engagement and support in higher education throughthe utilization of AI chatbots. The second segment critically examines the multifaceted factorsthat intricately mold student preferences concerning the deployment of AI chatbots forcommunication within higher education. In particular, the second segment focuses on the
intricate interplay between security, privacy considerations, and the decision-making processes
that support the adoption of AI chatbots for addressing a spectrum of academic inquiries,
encompassing admissions, course-related matters, academic affairs, and library resources.
Perceptions of AI Chatbots in Enhancing Student Engagement and Support in Higher
Education
AI chatbots play a diverse role in higher education, revealing different aspects that enhance
student engagement and academic support (Pérez et al., 2020; Abbas et al., 2022; Studente etal., 2020). They have both strengths and limitations, efficiently providing quick answers tocommon queries, but struggling with more complex issues. These chatbots are handy forroutine tasks, like giving timely information and helping with administrative matters, but
doubts remain about their ability to handle intricate problems. Striking a balance between theirstrengths and weaknesses is crucial (Green & Johnson, 2021). However, chatbots also go
beyond education, improving user experiences by offering smooth interactions and addressing
communication challenges (Kooli, 2023; Tlili et al., 2023). While they might not excel at
handling complex interactions, they enrich experiences in various ways, such as easing
university transitions and promoting peer connections (Kooli, 2023; Tlili et al., 2023; Studente
et al., 2020). Moreover, they provide personalized recommendations and real-time updates thatenhance daily routines, making them convenient and supportive (Kuhail et al., 2022). These
chatbots don’t just assist in education but also streamline administrative tasks, allowing
teachers and staff to focus on more intricate matters (Lee et al., 2020; Okonkwo & Ade-Ibijola,2021).
AI chatbots optimize communication, enhancing the overall experience. They improveengagement, offer spot responses, and suggest personalized recommendations, leading tohigher satisfaction (Lee et al., 2020). Availability around the clock is also important forconsistent support (Green & Johnson, 2021). Chatbots work in synergy with human interaction,balancing concerns about data security and confidentiality. They contribute to a comprehensive
approach and show potential for beneficial collaboration between humans and technology(Watson et al., 2022; Khan et al., 2019). AI chatbots and human engagement complement eachother, making interactions efficient and accurate. While they shine in straightforward
responses, human judgment is irreplaceable for complex inquiries (Green & Johnson, 2021).
They have the potential to enhance specialized tasks, acting as valuable partners alongsidehumans (Touimi et al., 2020). They also engage users effectively by offering convenientinteractions and personalized suggestions (Kuhail et al., 2022; Studente et al., 2020). There’s
an ongoing effort to refine AI chatbots. Improving their natural language understanding,adaptability, user experience, interface design, speed, and accuracy are important for ensuring
user satisfaction over time (Li et al. 2021). Based on the current research findings, students
view AI chatbots as valuable tools in higher education, offering rapid assistance, boostingengagement, and simplifying communication, all while aiming for ongoing enhancement.

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Factors Shaping Student Preferences for AI Chatbots in Higher Education: Addressing
Security, Privacy, and Decision-Making
The influence of AI chatbots as a means of communication in higher education is examinedthrough the UTAUT2 framework (Venkatesh et al., 2012). This framework provided valuable
insight into the factors that shape students’ preferences for using AI chatbots and how these
preferences can impact engagement and support. One key factor that affected participants’views on AI chatbots communication was “Performance Expectancy,” an aspect of UTAUT2.
This factor related to the belief that AI chatbots can enhance educational outcomes and improve
the overall student experience (Liu et al., 2019). This aligned with the idea highlighted in the“Dual (Effectiveness and Limitations)” theme, which emphasized the efficiency of AI chatbots
in delivering timely and consistent information (Pérez et al., 2020; Abbas et al., 2022). Such a
positive perception can significantly influence students’ inclination towards AI chatbots andimpact their preferences. This factor referred to how user-friendly and easy it was to interact
with AI chatbots (Johannsen et al., 2018). This aligned with the “Engage+AI” theme, which
illustrated how chatbots enhance user engagement through convenience, efficiency, andpersonalized recommendations (Kuhail et al., 2022). When students perceive AI chatbots aseffortless to use, they are more likely to adopt them for communication.
The influence of peers, instructors, and institutional norms, known as “Social Influence,”
corresponded with the “Synergize” theme. Positive feedback and recommendations from peers
and instructors significantly shaped participants’ willingness to use AI chatbots (Watson et al.,2022; Khan et al., 2019). This is consistent with UTAUT2’s emphasis on social factors
impacting technology adoption (Wang et al., 2021). Additionally, the “Facilitating Conditions”
aspect of UTAUT2 is vital. This factor highlighted the necessity for a supportive environment,
including reliable internet access and technical assistance (Li et al., 2021). This aligned withthe “Optimization” theme, where AI chatbots were refined to provide enhanced communication
experiences (Lee et al., 2020). Furthermore, concerns related to security, privacy, and otherfactors influenced participants’ decisions regarding AI chatbot adoption. These concerns wereaddressed within the “Synergize” theme, which emphasized the importance of finding abalance between data security, confidentiality, and reliability (Kooli, 2023). Addressing theseconcerns is crucial for building student trust in AI chatbot communication. The combined
insights from this study’s themes and the UTAUT2 constructs elucidate how students perceive
the role of AI chatbots in improving engagement and support in higher education. These
discussions offer a comprehensive understanding of the factors that influence students’preferences for AI chatbot communication and how these preferences can impact theireducational experiences.
Limitations and Future Research Directions
The present study’s scope and generalizability are rooted in a specific higher education context,
potentially limiting the transferability of the findings to more diverse environments or student
populations. In addition, the small sample size (n = 11) might not fully encompass the spectrumof student perceptions, potentially restricting a comprehensive understanding. While thephenomenological approach provides rich insights, subjectivity in data interpretation,
influenced by the researcher’s perspective, could introduce bias. The cross-sectional design
might miss temporal shifts, suggesting the value of longitudinal studies for dynamic insights.
Differing AI familiarity levels among participants could have led to varied interpretations.
Despite efforts for objectivity, researcher biases could impact design, data collection, andanalysis. Unaccounted external influences like media portrayal might have affected24

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participants’ perceptions. Prioritizing depth over breadth, the study might limit a holistic
understanding of AI chatbots perceptions. Sole reliance on phenomenological analysis could
be enriched by multiple methodologies. Ethical concerns arise around personal perceptions andprivacy. These limitations call for cautious interpretation, while future research paths offer
exciting potential for enhancing AI chatbot communication in education. Longitudinal studies
might track AI chatbots’ impact over time, while comparative studies offer context-specificinsights. A blend of qualitative and quantitative research can uncover user experiences, andethical considerations ensure responsible practices. Professional development for staff can
optimize integration, enriching AI chatbot communication and elevating the educationallandscape through enhanced engagement and support.
Conclusion
This study has significantly advanced the field’s comprehension of AI chatbot communication
within higher education, spotlighting its substantial role in bolstering student engagement andsupport. Employing a meticulous analysis of thematic patterns and subthemes through the lensof UTAUT2 constructs, it has propelled the field forward by furnishing a comprehensive
outlook on the adoption and embrace of AI chatbots. The main findings (results) of the study
fit well with previous literature that also talk about numerous benefits of AI chatbots used inhigher education. These benefits include giving personalized help, making administrative taskssmoother, and increasing interactions between students and others (Okonkwo & Ade-Ibijola,
2020; Cunningham-Nelson et al., 2019; Green & Johnson, 2021).
It is also important to know about the limits in how this study was done, which makes sure theresearch is truthful and strong. The way the study used phenomenological questions and IPA
gave the researchers a good sense of the participants’ real experiences, but it is also important
to know that these ideas can’t show why things happen or how students think in every situation(Tuffour, 2017; Smith et al., 2009). This study adds to what we know by not only showing
what AI chatbots can do, but also their limitations and challenges. It talks about how important
it is to think about the things that make them work well, like what helps them, how students
think, and making them better over time. At the same time, it knows that there are some things
it couldn’t look at, like only studying specific parts and using what students already know. The
next step is to look at new things, like studying more ideas, using new discoveries, and looking
at different level of education. Even though this study only looks at colleges and university, its
ideas can help other levels of education, too. In a world where AI chatbots are changing how
we talk and learn in higher education, this research is like a strong start, giving us ideas formore research and ways to make them even better (Kooli, 2023; Dwivedi et al., 2023; Mendoza
et al., 2020; Watson et al., 2022). Knowing about its limits, facing its challenges, and giving
helpful advice, this study builds a strong base for us to learn more about AI chatbots in higher
education.

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References
AbuShawar, B., & Atwell, E. (2015). ALICE chatbot: Trials and outputs. Computación ySistemas, 19(4), 625–632. https://doi.org/10.13053/cys-19-4-2326
Abbas, N., Whitfield, J., Atwell, E., Bowman, H., Pickard, T., & Walker, A. (2022). Online
chat and chatbots to enhance mature student engagement in higher education.
International Journal of Lifelong Education, 41(1).
https://doi.org/10.1080/02601370.2022.2066213
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human
Decision Processes, 50(2), 179– 211. https://doi.org/10.1016/0749-5978(91)90020-T
Brown, S. A., & Venkatesh, V. (2005). Model of adoption of technology in households: A
baseline model test and extension incorporating household life cycle. MIS Quarterly,29(3), 399426. https://doi.org/10.2307/25148690
Caldarini, G., Jaf, S., & McGarry, K. (2022). A literature survey of recent advances inchatbots. Information, 13(1), 41. https://doi.org/10.3390/info13010041
Clarizia, F., Colace, F., Lombardi, M., Pascale, F., & Santaniello, D. (2018). Chatbot: An
education support system for student. In Cyberspace Safety and Security: 10th
International Symposium, CSS 2018, Amalfi, Italy, October 29–31, 2018,
Proceedings 10 (pp. 291–302). Springer International Publishing.
https://doi.org/10.1007/978-3-030-01689-0_23
Colby, K. M., Hilf, F. D., Weber, S., & Kraemer, H. C. (1972). Turing-like
indistinguishability tests for the validation of a computer simulation of paranoid
processes. Artificial Intelligence, 3, 199–221.
http://dx.doi.org/10.1016/0004-3702(72)90049-5
Cunningham-Nelson, S., Boles, W., Trouton, L., & Margerison, E. (2019). A review ofchatbots in education: practical steps forward. In 30th annual conference for theAustralasian association for engineering education (AAEE 2019): educators
becoming agents of change: innovate, integrate, motivate (pp. 299-306). Engineers
Australia.
Dale, R. (2016). The return of the chatbots. Natural Language Engineering, 22, 811–817.
http://dx.doi.org/10.1017/S1351324916000243
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of
information technology. MIS Quarterly, 13, 319–340. https://doi.org/10.2307/249008
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., SlBusaidi, A. S., Balakrishman, J., Barlette, Y., Basu, S., Bose, I., Brooks, L., Buhalis,D., Carter, L, & 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, 102642.
https://doi.org/10.1016/j.ijinfomgt.2023.102642
Finlay, L. (2011). Phenomenology for psychotherapists: Researching the lived world. WileyBlackwell. https://doi.org/10.1002/9781119975144

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Green, S., & Johnson, M. (2021). Enhancing administrative efficiency with AI Chatbots inuniversity admissions. Journal of Educational Administration, 54(3), 345–367.
https://doi.org/10.1108/JEA-2020-1234
Hien, H. T., Cuong, P. N., Nam, L. N. H., Nhung, H. L. T. K., & Thang, L. D. (2018,
December). Intelligent assistants in higher-education environments: the FIT-EBot, achatbot for administrative and learning support. In Proceedings of the 9thInternational Symposium on Information and Communication Technology.
Association for Computing Machinery. https://doi.org/10.1145/3287921.3287937
Ismail, M., & Ade-Ibijola, A. (2019, November). Lecturer’s apprentice: A chatbot forassisting novice programmers. In 2019 international multidisciplinary information
technology and engineering conference (IMITEC) (pp. 1–8). IEEE.
https://doi.org/10.1109/IMITEC45504.2019.9015857
Jacucci, G., Spagnolli, A., Freeman, J., & Gamberini, L. (2014). Symbiotic interaction: A
critical definition and comparison to other human-computer paradigms. In Symbiotic
Interaction: Third International Workshop, Symbiotic 2014, Helsinki, Finland,
October 30-31, 2014, Proceedings 3 (pp. 3-20). Springer International Publishing.
https://doi.org/10.1007/978-3-319-13500-7_1
Johannsen, F., Leist, S., Konadl, D., & Basche, M. (2018). Comparison of commercial
chatbot solutions for supporting customer interaction. Research Papers, 158.
https://aisel.aisnet.org/ecis2018_rp/158
Kar, R., & Haldar, R. (2016). Applying chatbots to the internet of things: Opportunities andarchitectural elements. International Journal of Advanced Computer Science andApplications, 7(11), 147–154. http://dx.doi.org/10.14569/IJACSA.2016.071119
Khan, A., Ranka, S., Khakare, C., & Karve, S. (2019). NEEV: An education informational
chatbot. International Research Journal of Engineering and Technology, 6(4), 492–
495. https://www.irjet.net/archives/V6/i4/IRJET-V6I4109.pdf
Khanna, A., Pandey, B., Vashishta, K., Kalia, K., Bhale, P., & Das, T. (2015). A study oftoday’s A.I. through chatbots and rediscovery of machine intelligence. International
Journal of u- and e-Service, Science and Technology, 8, 277–284.
https://doi.org/10.14257/ijunesst.2015.8.7.28
Kooli, C. (2023). Chatbots in Education and Research: A Critical Examination of EthicalImplications and Solutions. Sustainability, 15, 5614.
https://doi.org/10.3390/su15075614
Kuhail, M. A., Al Katheeri, H., Negreiros, J., Seffah, A., & Alfandi, O. (2022). Engaging
students with a chatbot-based academic advising system. Int. J. Hum. Comput. Int. 1–
27. https://doi.oreg/10.1080/10447318.2022.2074645
Lee, J., & Rho, M. J. (2013). Perception of influencing factors on acceptance of mobile healthmonitoring service: a comparison between users and non-users. Healthcare
Informatics Research, 19(3), 167–176. https://doi.org/10.4258/hir.2013.19.3.167
Lee, Y.-H., Yang, X., Huang, C.-Y., & Wu, C.-Y. (2020). Design and application of AI
chatbot in a flipped classroom: A case study in higher education. Computers &Education, 144, 103701. https://doi.org/10.1016/j.compedu.2019.103701
Li, L., Lee, K. Y., Emokpae, E., & Yang, S. B. (2021). What makes you continuously use
chatbot services? Evidence from Chinese online travel agencies. Electronic Markets,
31(3), 575–599. https://doi.org/10.1007/s12525-020-00454-z

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Liu, X., Gao, J., Zhang, R., Li, M., & Shi, Y. (2019). Adoption of AI-based learningtechnologies in China: A UTAUT model. Computers & Education, 142, 103641.
https://doi.org/10.1016/j.compedu.2019.103641
Liu, H., Peng, H., Song, X., Xu, C., & Zhang, M. (2022). Using AI chatbots to provide selfhelp depression interventions for university students: A randomized trial ofeffectiveness. Internet interventions, 27, 100495.
https://doi.org/10.1016/j.invent.2022.100495
Mabunda, K., & Ade-Ibijola, A. (2019, November). Pathbot: An intelligent chatbot for
guiding visitors and locating venues. In 2019 6th International Conference on Soft
Computing & Machine Intelligence (ISCMI) (pp. 160-168). IEEE.
https://doi.org/10.1109/ISCMI47871.2019.9004411
Malterud, K. (2012). Systematic text condensation: A strategy for qualitative analysis.
Scandinavian Journal of Public Health, 40(8), 795–805.
https://doi.org/10.1177/1403494812465030
Mauldin, M. L. (1994, August). Chatterbots, tinymuds, and the turing test: Entering the
Loebner prize competition. In the Proceedings of the Association for the Advancementof Artificial Intelligence (pp. 16–21). AAAI.
Mekni, M., Baani, Z., & Sulieman, D. (2020, January). A smart virtual assistant for students.
In Proceedings of the 3rd International Conference on Applications of Intelligent
Systems (pp. 1–6). Association for Computing Machinery.
https://doi.org/10.1145/3378184.3378199
Mendoza, S., Hernández-León, M., Sánchez-Adame, L. M., Rodríguez, J., Decouchant, D., &Meneses-Viveros, A. (2020). Supporting student-teacher interaction through achatbot. In Learning and Collaboration Technologies. Human and Technology
Ecosystems: 7th International Conference, LCT 2020, Proceedings, Part II (pp. 93–
107). Springer International Publishing. https://doi.org/10.1007/978-3-030-50506-6_8
Molnár, G., & Szüts, Z. (2018). The Role of Chatbots in Formal Education. In the 16th
International Symposium on Intelligent Systems and Informatics (SISY) (pp. 000197000202). Subotica, Serbia. https://doi.org/10.1109/SISY.2018.8524609
Moorthy, K., Yee, T. T., T’ing, L. C., & Kumaran, V. V. (2019). Habit and hedonic
motivation are the strongest influences in mobile learning behaviours among higher
education students in Malaysia. Australasian Journal of Educational Technology,
35(4). https://doi.org/10.14742/ajet.4432
Moriuchi, E. (2021). An empirical study on anthropomorphism and engagement withdisembodied AIs and consumers’ re-use behavior. Psychol. Market. 38, 21–42.
https://doi.org/10.1002/mar.21407
Okonkwo, C. W., & Ade-Ibijola, A. (2020). Python-Bot: A chatbot for teaching python
programming. Engineering Letters, 29(1), 25–34.
Okonkwo, C. W., & Ade-Ibijola, A. (2021). Chatbots applications in education: A systematic
review. Computers and Education: Artificial Intelligence.
https://doi.org/10.1016/j.caeai.2021.100033
Palau-Saumell, R., Forgas-Coll, S., Sánchez-García, J., & Robres, E. (2019). User
Acceptance of Mobile Apps for Restaurants: An Expanded and Extended UTAUT-2.
Sustainability, 11(4), 1210. https://doi.org/10.3390/su11041210

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Pereira, J., Fernández-Raga, M., Osuna-Acedo, S., Roura-Redondo, M., Almazán-López, O.,& Buldón-Olalla, A. (2019). Promoting learners’ voice productions using chatbots asa tool for improving the learning process in a MOOC. Technology, Knowledge andLearning, 24(4), 545–565. https://doi.org/10.1007/s10758-019-09414-9
Pérez, J. Q., Daradoumis, T., & Puig, J. M. M. (2020). Rediscovering the use of chatbots in
education: A systematic literature review. Computer Applications in Engineering
Education, 28(6), 1549–1565. https://doi.org/10.1002/cae.22326
Perez-Vega, R., Kaartemo, V., Lages, C. R., Razavi, N. B., & Männistö, J. (2021). Reshaping
the contexts of online customer engagement behavior via artificial intelligence: A
conceptual framework. Journal of Business Research, 129, 902-910.
https://doi.org/10.1016/j.jbusres.2020.11.002
Pham, X. L., Pham, T., Nguyen, Q. M., Nguyen, T. H., & Cao, T. T. H. (2018, November).
Chatbot as an intelligent personal assistant for mobile language learning. In
Proceedings of the 2018 2nd International Conference on Education and E-Learning,(pp. 16–21). https://doi.org/10.1145/3291078.3291115
Powton, M. (2018, Feb. 28). A Visual History of Chatbots. Medium.com. Retrieved fromhttps://chatbotsmagazine.com/a-visual-history-of-chatbots-8bf3b31dbfb2
Ranoliya, B. R., Raghuwanshi, N., & Singh, S. (2017, September). Chatbot for university
related FAQs. In 2017 International Conference on Advances in Computing,Communications, and Informatics (ICACCI) (pp. 1525-1530). IEEE.
https://doi.org/10.1109/ICACCI.2017.8126057
Roberts, L. M. (2020). Interviewing: The art of science. In C. E. Jorgensen, K. M. R. Tackett,& J. L. Suizzo (Eds.), Handbook of Research Methods in Human Development (2nded., pp. 107–128). Guilford Press.
Rogers, E. M. (1961). Bibliography on the diffusion of innovations. Internet Things, 13, 100344.
Russell, S., & Norvig, P. (2010) Artificial Intelligence: A Modern Approach. 3rd Edition,
Prentice-Hall, Upper Saddle River.
Shinebourne, P., & Smith, J. A. (2009). Alcohol and the self: An interpretative
phenomenological analysis of the experience of addiction and its impact on the senseof self and identity. Addiction Research and Theory, 17(2), 152–167.
https://doi.org/10.1080/16066350802245650
Shinebourne, P., & Smith, J. A. (2009). Exploring the experience of self‐injury: A qualitative
study of women with a history of self‐injury. Journal of Community and AppliedSocial Psychology, 19(2), 112–126. https://doi.org/10.3390%2Fijerph18073361
Shinebourne, P., & Smith, J. A. (2010). The communicative power of metaphors: An analysis
and interpretation of metaphors in accounts of the experience of addiction.
Psychology and Psychotherapy, 83(1), 59–73.
https://doi.org/10.1348/147608309X468077
Sinha, S., Basak, S., Dey, Y., & Mondal, A. (2020). An educational chatbot for answering queries.
In Emerging Technology in Modelling and Graphics: Proceedings of IEM Graph 2018 (pp.
55–60). Springer Singapore. https://doi.org/10.1007/978-981-13-7403-6_7
Smith, J. A., Flower, P., & Larkin, M. (2009). Interpretative phenomenological analysis:
Theory, method and research. Sage Publishing. https://uk.sagepub.com/engb/eur/interpretative-phenomenological-analysis/book250130.

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Smith, J. A., & Osborn, M. (2003). Qualitative psychology: A practical guide to researchmethods (2nd ed.). Sage Publications. https://psycnet.apa.org/record/2003-06442-000.
Studente, S., Ellis, S., & Garivaldis, S. F. (2020). Exploring the potential of chatbots inhigher education: a preliminary study. International Journal of Educational andPedagogical Sciences, 14(9), 768–771.
Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., &Agyemang, B. (2023). What if the devil is my guardian angel: ChatGPT as a casestudy of using chatbots in education. Smart Learning Environments, 10, 51.
https://doi.org/10.1186/s40561-023-00237-x
Touimi, M., Kadmiry, B., Al Marzouqi, A. H., & Al Falasi, A. M. (2020). Evaluating the useof chatbot in UAE higher education. Education and Information Technologies, 25(5),4263–4280. https://doi.org/10.1007/s10639-020-10193-9
Tuffour, I. (2017). A Critical Overview of Interpretative Phenomenological Analysis: A
Contemporary Qualitative Research Approach. J Healthc Commun. 2:52.
https://doi.org/10.4172/2472-1654.100093
Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59(236), 433–460.
https://doi.org/10.1093/mind/LIX.236.433
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User Acceptance ofInformation Technology: Toward a Unified View. MIS Quarterly, 27(3), 425–478.
https://doi.org/10.2307/30036540
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer Acceptance and Use ofInformation Technology: Extending the Unified Theory of Acceptance and Use ofTechnology. MIS Quarterly, 36(1), 157–178. https://doi.org/10.2307/41410412
Vimalkumar, M., Sharma, S. K., Singh, J. B., & Dwivedi, Y. K. (2021). ‘Okay google, what
about my privacy?’: User’s privacy perceptions and acceptance of voice based digitalassistants. Computers in Human Behavior, 120, 106763.
https://doi.org/10.1016/j.chb.2021.106763
Vincent, J. (2019, Nov. 7). OpenAI has published the text-generating AI it said was too
dangerous to share. The Verge. Retrieved fromhttps://www.theverge.com/2019/11/7/20953040/openai-text-generation-ai-gpt-2-fullmodel-release-1-5b-parameters
Wang, F., Zhang, Y., Xu, J., Yu, C. S., & Li, Z. (2021). Understanding user acceptance ofAI-based education chatbots: An extended UTAUT model. International Journal of
Human-Computer Studies, 149, 102582. https://doi.org/10.1016/j.ijhcs.2020.102582
Wirtz, B. W., Weyerer, J. C., & Geyer, C. (2019). Artificial Intelligence and the Public
Sector—Applications and Challenges. International Journal of Public Administration,
42(7), 1–20. https://doi.org/10.1080/01900692.2018.1498103
Yu, C. S. (2012). Factors affecting individuals to adopt mobile banking: empirical evidence
from the UTAUT model. Journal of Electronic Commerce Research, 13, 104.
http://www.jecr.org/sites/default/files/13_3_p01_0.pdf
Corresponding author: Soniya Antony
Email: soniyaantony4@gmail.com

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Appendixes
Table 1
STC Table, Model of Data Analysis Arriving Codes and Theme
Meaning Unit
1. What is youropinion on AI
chatbots and do
you find them
helpful? Pleaseprovide a
detailed
description ofyour viewpoint.
Respondent (R) 1: I
believe chatbots are
incredibly helpful.
They provide quick
and accurate
responses, saving
time and effort infinding information
or resolving queries.
Their availability
24/7 ensures
immediate support,
making them avaluable tool intoday’s fast-paced
world.
R 2: Personally, I
find chatbots quite
helpful. They offer
convenience byeliminating the need
to wait for customer
service
representatives.
Chatbots provide
instant assistance andguide me throughvarious processes,
making myinteractions efficientand hassle-free.

Condensed
Meaning Unit

Chatbots arehelpful inproviding quick
and accurate
responses,
saving time andeffort.

Code

Chatbots arehelpful inprovidingquick andaccurate
responses,
saving timeand effort forusers.

Chatbots offer
convenience by
providing instantassistance andguiding users

31

Sub theme

Efficient andTime-saving

Theme

IAFOR Journal of Education: Technology in Education

R 3: I’m skeptical
about chatbots’effectiveness. Whilethey can handle basic
queries, complex
issues often require
human intervention.
Chatbots sometimes
fail to understandnuanced questions,leading to frustration
and a need forhuman support.
R 4: Chatbots are amixed bag for me. In
some cases, they
provide useful
information
promptly. However,when faced withmore specific or
personalized queries,
chatbots often fallshort and fail todeliver the level ofassistance I require.

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through various
processes.
Skepticism
exists regarding

Chatbots’effectiveness,
especially forhandling
complex issues
that may require
humanintervention.

Skepticism
surrounding
chatbots’effectiveness,particularly inhandling
complexissues that
may require
humanintervention

Dual(Effectiveness
and
Limitations)

R 5: I find chatbots
helpful in certain
contexts. For simple
tasks like checking
order statuses orgetting basic
information, they
excel. However,when it comes tomore complex
discussions or
problem-solving,
human interaction
remains
irreplaceable.
R 6: Chatbots are agreat concept, but
their execution needs
improvement. Whilethey offer quick
responses, the lack of

Skepticism
andLimitations

Chatbots areuseful for simple
tasks but may
fall short when

32

IAFOR Journal of Education: Technology in Education

human touch can be
frustrating at times. I
appreciate theiravailability, butthere’s still room forenhancing theircapabilities.

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faced withspecific or
personalized
queries.

R7: Personally, I
haven’t foundchatbots to beparticularly helpful.
Their preprogrammed
responses often failto address my
specific needs. I
prefer direct humaninteraction for amore personalized
and tailoredexperience.
R 8: I see thepotential of chatbots
and their usefulness.
As technology
advances, they
continue to improve,
providing moreaccurate andcomprehensive
assistance. Whilethey may not be
perfect, theirconvenience andaccessibility
outweigh any
drawbacks.

Offer
convenience
It has potential
by providing
and continues to instantimprove, but
assistance andthere is a need to guiding users
enhance theirthroughcapabilities andvariousaddress the lack processes.
of human touch.

R 9: Chatbots are avaluable tool incertain situations.
Their ability toprovide quickanswers and support
can be beneficial,
especially for routine
inquiries. However,when it comes to33

Convenienceand Guidance

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complex or sensitive
matters, humaninteraction remains
essential.
R 10: I find chatbots
helpful, especially in
scenarios where
human assistance islimited or
unavailable. They
provide instantresponses, reduce
waiting time, andoffer guidance in aself-service manner.
However, for more
complex issues, I
prefer interactingwith a humanrepresentative.
R11: My opinion on
chatbots is neutral.
While they can be
helpful in providing
basic information, I
believe that humaninteraction brings apersonal touch andbetter understanding.
Table2Items wise Codes and Theme
Items
2. How has been yourexperience when using
a chatbots outside ofthe education field?
How would you
describe yourrecollection of it andthe overall user
experience?

Code“The positive aspectof the chatbots
outside of education,emphasizing its easy
navigation, quick
responses, andoverall seamless user
experience”.

Sub theme“Frustration
experienced when
the chatbots failed tounderstand queries
and provided

Communication
Challenges

34

Theme

Smooth User
Experience

BeyondIAFOR Journal of Education: Technology in Education

Volume 11 – Issue 2 – 2023

irrelevant responses,
leading to wasted
time and a frustratinguser experience”.

3. Do you believe
chatbots are capableof delivering usefulservices orinformation? If so,
what specific types ofservices would you
find acceptable for achatbots to provide,other than basic
course and University
information?

4. What were theprimary factors that
influenced yourdecision to choose AI
chatbots as a means ofcommunication withan
institution/university?

5. Do you have any
specific reasons or“Its ability to quickly
resolve queries andprovide personalized
responses, resulting
in a satisfactory user
experience”,“Chatbots providing
tailored suggestions
for books, movies,
and other interests”.

LimitedComplexity
Handling

Personalized
Recommendations“Chatbots deliveringreal-time traffic
updates andalternative routesuggestions”.

Real-Time
Updates andNavigation“Schedulingappointments,
sending reminders,and providing
financial advice orbudgetingassistance”.
“AI chatbots offer
easy access to
information andprovide convenient
communication withthe university”.

“Convenience andAssistance”.

Enrichment“Streamline
communication
processes, resulting
in faster responses
and reduced waitingtimes”.
“Ensuring assistanceis accessible at any
time, including
outside regular officehours”.
“Concerns about thesafety and protection

35

Enhanced
Communication
Experience

Streamlined
Efficiency

Optimization
Continuous
Availability

Data Security andConfidentiality

IAFOR Journal of Education: Technology in Education

concerns that would deter
you from choosing AI
chatbots as acommunication channel
with your institution/
university particularly interms of security, privacy,
or any other factors?

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of personal
information,
including worries
about data breaches
and unauthorizedaccess”.
“Desire for private
conversations and a
Reliability andpreference for human Accuracy
representatives overautomated systems toensure confidentiality
and privacy”.
“Incorrect
information or
misinterpret queries,
leading tomisunderstandings orincorrect decisions”.
“The value of humantouch andpersonalizedinteraction, which
may be lacking in AI
chatbots
communication”.

6. Do you believe AI
chatbots have thecapability to serve as acommunication medium
for addressing yourinquiries with theinstitution/ university
regarding admissions,course and academic
related, and libraryreference?

Synergize

HumanInteraction

Personalization“The ability of AI
chatbots tounderstand individual
needs and provide
tailored solutions,
resulting in a lack ofcustomization andpersonalization”.
“Quick and accurate Efficient andresponses, saving
Accuratetime for both users
Responses
and institutions”.
“To handle complex
inquiries that
requires humanjudgment andempathy”.

36

Limitations inComplex
Inquiries

Streamlining
Communication

IAFOR Journal of Education: Technology in Education

7. What factors would
motivate you torepeatedly use an AI
chatbots at theinstitution/university
instead of email, creatinga regular usage pattern?
Would a recommendationfrom a friend, or peer
influence your decision touse it?

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“Handling basicinquiries and freeing
up human resources
for more specialized
or complex tasks”.
“AI chatbots as amotivating factor for
repeated usage. Users
appreciate the quick
access to
information, prompt
responses, andstreamlined
communication
process”.
“Users are motivatedto use chatbots
repeatedly when they
can understand theirpreferences andprovide relevantsuggestions”.
“Users are more
likely to adopt
regular usage
patterns if theyreceive positive
feedback or
endorsements fromtrusted sources”.
“When they can relyon them as a reliableand knowledgeable
resource”.

8. What feature do you
consider the most“The integration of
AI chatbots withother university
systems andplatforms and they
can seamlessly
connect with various
tools and enhancetheir overall
experience.
“Chatbots ability tounderstand and

37

Complementary
Role with Human
SupportConvenience andEfficiency

Personalization
andRecommendations

Trust and Social
Influence

Enlighten
Engage+AI

Integration and
Seamless
ExperienceIAFOR Journal of Education: Technology in Education

important in an AI
chatbots? Please explain
your expectations andwalk me through the key
aspects you prioritize.

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interpret queries innatural language”.
“The significance oftailoring the chatbots
responses andinteractions to theuser’s preferences”.

Natural Language
Understanding
and Adaptability
User Experienceand Interface
Design“The user’s
expectations forprompt and accurate Speed andresponses from the
Accuracy
chatbots,emphasizing the needfor efficient andreliable interactions”.

38

RefineIAFOR Journal of Education: Technology in Education

Volume 11 – Issue 2 – 2023

Post-COVID-19 Higher Learning: Towards Telagogy, A Web-Based
Learning Experience
Teboho Pitso
Vaal University of Technology, South Africa

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Abstract
Institutes of higher education (IHEs) have to consider benefits of remote learning postpandemic. Retrogression to physical contact is counterproductive. The hasty implementationof remote learning during the pandemic deprived IHEs of opportunities to efficiently enact andtheorise about it. Post-pandemic, IHEs have opportunities to theorise about remote learning
hence the questions; a) what type of learning emerges when asynchronous and technology-asessence framework undergirds students learning? b) What benefits accrue when chatGenerative Pre-training Transformer (chat-GPT) is infused into students learning? Use ofsynchronous learning and technology-as-utility framework to underpin remote learning during
the pandemic was intended to retain most of physical contact learning traditions. Teachers andstudents met synchronously and simultaneously online for learning to occur. IHEs safeguarded
their operational efficiency to minimise the disruptive nature of remote learning. The purposeof the study was to theoretically examine effects of asynchronous learning and “technologyas-essence framework on students learning. Asynchronous learning occurs when students
registered on the same course learn online on their own schedule without any real-time
interactions with teachers. This phenomenon occurs when remote learning develops throughtechnological advances that, beyond 2030, would most likely stream educational courses
similar to Netflix. One such technological advance is chat-GPT. A study was undertaken tobetter understand it. 15 multi-disciplinary advanced undergraduates tested out chat-GPT on
their assignments and a concrete problem. Chat-GPT lessened the time of doing assignments
and improves students’ problem solving abilities. AI systems advances have a positive effecton students learning. The study addresses the positive impact of asynchronous learning andadvances in technology on IHEs.
Keywords: artificial intelligence, chat-GPT, remote learning, technology, Post-COVID-19
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The post-pandemic era provides Institutes of Higher Education (IHEs) with opportunities toreimagine learning within the ambit of advanced remote learning. Remote learning refers to amode of learning that occurs online via scheduled Zoom classes and Bright-space activities
because in-person traditional classroom learning was not possible during the Covid-19
pandemic (Burke and Locmele, 2022; Neuwirth, 2021; Torres and Ortega-dela Cruz, 2022). It
thrives on synchronous learning where students and teachers meet in live online classes in order
for learning and teaching to take place. While occurring online, synchronous remote learning
tends to mimic in-person contact sessions. I argue that remote learning that assumes anasynchronous learning approach is likely to help IHEs to reimagine learning in the digital age.
An asynchronous approach refers to any mode of online learning undertaken at the behest of
students’ own schedule and has no need for live online interactions between students andteachers. It shares similarities with heutagogy (Blasche, 2012) where students access learning
content as and when they are ready to do so, at their own pace, anywhere and anytime. The
brick-and-mortar traditional classrooms are becoming increasingly outdated and would
struggle to survive outside online learning. The asynchronous student-centred learning
approach with a strong self-determination character that builds students’ capabilities to beautonomous, self-reliant, and technologically savvy represents a way forward for IHEs. I referto this type of online, web-based learning as telagogy. I also forecast that beyond 2030,
telagogy would become an asynchronous, online learning experience that would be streamed
like Netflix and controlled fully by students as Wright (2020) suggests. Telagogy would alsoenable students to curate their own personal curriculum drawing educational courses fromuniversities across the entire world. This would compel IHEs to develop universal accreditation
systems. It is these meanings of telagogy that make it an advanced form of remote learning. It
is anticipated that governments may consider funding students instead of universities.
Another crucial feature of telagogy would be to fundamentally shift the work of human teachers
away from textbooks and study guides towards new role demands. Sterling (2020) suggeststhat university teachers ought to stop trying to write down knowledge that is already available
in many AI systems such as chat-GPT and refocus their energies on how to better incorporate
these AI and advanced technology capabilities in student learning. Asynchronous approach tolearning, in particular, reconfigures human teachers’ role in advanced remote learning and iscritical in theorising about learning and teaching in the digital age. Options of asynchronous
learning for human teachers include posting, developing text or multimedia resources,
preparing online polls/quizzes, students’ contribution to collectively authored resources andco-creating educational course blogs, creating educational You-Tube videos (Buxton, 2014;
Watts, 2016; National Forum, 2020; Butler, 2020). Asynchronous online learning opens spaces
to determine future imaginings of remote learning that include online streaming of educationalcourses, digital accreditation of personal curricula of students, digital assessment, use of new
digital learning techniques such as chat-GPT. This would turn higher education into a diverse,interactive, open, engaging activity (Bayne et al, 2020) that fosters self-directed learning, peer
learning, and student agency (Blasche, 2012; Junco, 2010; Halupa, 2015). Assessment can bereimagined beyond text-based assessments to include audio-clips, videos with evidence ofpractical projects that solve community problems, animation, and image making (National
Forum, 2020). Another key consideration in seeking advanced remote learning features

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includes virtual learning environments (VLEs) that have a proven record of use in online
learning particularly on content delivery, digital assessment, lecturer-student engagement, andbig data analytics (Anderson, 2016). However, VLEs are not without its critics. The platform
is seen as constraining pedagogical practices and undermining digital/web literacy practices(Williams, 2023; National forum, 2020). In this study, concerns about remote learning beyond
VLEs and synchronous offerings relate to how technology was viewed during the pandemic.
Two main approaches that guided the use of technology in IHEs during COVID-19 pandemic
were synchronous learning and technology-as-utility anthropological conception. As alreadystated, these two approaches restricted remote learning to operational efficiency rather thancreating conditions for new theorisations about learning and teaching. The implementation of
remote learning during the pandemic relied on synchronous, technology-as-utility framework.
The post-pandemic technological framework that is likely to help IHEs to reimagine student
learning would thus entail asynchronous, technology-as-essence framework (Heidegger, 1954;
1977 Translation) and is the focus of this study.
The purpose of the study is to better understand students’ learning post-pandemic undertechnological conditions of asynchronicity and technology-as-essence framework (Heidegger,
1954; 1977 Translation) that are key constructs in theorising telagogy as an advanced stage of
remote learning. Furthermore, the study seeks a qualitative enquiry of process of using one of
the latest AI systems called chat-GPT on students’ assignments and their attempts to resolve a
concrete problem. Two questions that framed the study were thus:••

What type of learning emerges when asynchronous and technology-as-essence
framework undergirds students’ learning?
What benefits accrue when chat-GPT is infused into students’ learning?

In the next section, the theoretical framework based in literature review is developed in orderto ground the systematic investigation undertaken to better understand students’ learning postpandemic. A conceptual framework that shows technological variables necessary to be infused
to shape students learning in the digital age follows the description of the theoreticalframework. The research that tested out chat-GPT for student learning is also described
including its methods, findings, and discussion.
Theoretical Framework
Theories for online learning such as that of Anderson’s Online Learning Model (Anderson, 2011)
and Picciano’s Integrated Online Learning Model (Picciano, 2017) have evolved over time.
These models tend to assume a pedagogical framing within a synchronous and technology-asutility framework. I argue that this theoretical conception of online learning would not lead toadvances in remote learning that could culminate in what I call telagogy, or streaming online
educational courses. Telagogy is understood as a method and practice of students’ web-basedlearning that draws from remote learning but goes beyond its pandemic conception assynchronous utility. Remote learning was conceptualised and implemented force majeure during
the pandemic in a hasty way without any meaningful consideration of new theorisations about

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student learning. Almutairi et al, (2021) developed an emergency remote learning frameworkthat is mostly synchronous and utility-driven as a response to the learning and teaching crisis of
the pandemic era. This crisis was created by the pandemic and the results of Almutairi et al,(2021) show that multiple pedagogical approaches were used by IHEs human teachers to enable
students learning online. The use of traditional methods and practices of teaching such as
pedagogy in online learning is increasingly considered as obsolete. Studies on remote learning
beyond the pandemic such as those of Alenezi et al, (2022), Ndlovu et al, (2022) and Tzimiris etal, (2023) continue to theorise about remote learning within the emergency, crisis framework
dominated by synchronous learning conception and utility-mode use of technology in education.
In this article, I argue that post-pandemic era provides IHEs with opportunities to link anddevelop it beyond traditional methods and practices of teaching such as those maintained in postpandemic research on learning and teaching. Traditional meanings of teaching come for sharp
scrutiny in this article as relics of the past that needs jettisoning. The effort is intended to muster
new theorisations about learning and teaching within asynchronous and technology-as-essence
framework (Heidegger, 1954, 1977 Translation). Each traditional teaching mode consists of atarget (Skelton, 1995), that is, the learning participants and how they are treated in each learningand teaching encounter. This affects the degree of autonomy, self-determination, andindependence of students in the learning encounters (Blasche, 2012). The pedagogical models
that focus on instruction based on hard-coded knowledge, signify the role of a teacher as anexpert, thrive on passive students learning and strict class attendance remain entrenched in mostundergraduate studies (Soare, 2012). When remote learning was imposed, IHEs sought to retain
most of the features of pedagogy, such as, delivering content as close to in-person learning as
possible, making sure that students and teachers met online at the same time, students undertake
e-assessment as close as possible to in-person assessment techniques. These pedagogicalinterpretations of remote learning are retrogressive and infra dignitatem, below the dignity ofstudents in IHEs. Heidegger (1954, 1977 Translation) suggests that for technology to be allowed
to perform to its optimal level it would enable humans to progress to what Harari (2012) calls astate of homo deus, when humans become deities. There are concerns that such optimisation oftechnology would turn it into a harmful tool. The first concern relates to superintelligence whenadvanced technologies would possess an intelligence greater than the one possessed by a geniuslevel human being (Bostrom, 2014). This concern brings to sharp scrutiny unmitigated growthof technology beyond human comprehension and control. The concern, however, is impervious
to the role of IHEs in optimising human intelligence and smacks of scholasticism, narrow-mindedcommitment to tradition. Superintelligent technologies challenge humans to augment theirintelligence and abandon their debilitating traditions and religiosity. Telagogy provides anintellectual cleft to explore human intelligence beyond its current limitations. The basic missionof IHEs is to develop human intelligence and it cannot do so within their limiting traditions. IHEs
also need to revise their theoretical interpretation of formal learning as industrialisation ofteaching (performativity). Conceptualised as performativity, learning becomes enforcement ofindustrial rules and regulations, as well as replication of industrial processes with a strong techne’
episteme. Such learning still works within authority-subordinate relationships of pedagogy(Peters, 2006). This performativity learning reduces human intelligence to an industrial tool andcompromises the huge potential of human intelligence, a source of concern with43

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superintelligence. Psychologised notions of learning from Piaget (cognitivist) and Skinner(behaviourist) represent IHEs’ traditions that ought to be challenged in this century.
This study combines the theoretical interpretations of learning, collectively referred to as
theories of independence, self-determination, and autonomy (Keegan, 2013), and include
Freire’s (1970) critical theory of learning, Invitational learning theory drawn from rhetorics(Pitso et al, 2014), and critical pragmatism (Tjabane, 2021). It contributes the concept of andjustifies advanced remote learning. Telagogy seeks to empower students to deal with concrete
community problems hence use of Empowerment Evaluation research method. It is aboutdeveloping social justice pioneers that make an impact in communities. Telagogy shifts
learning not only from synchronous and technology-as-utility framework but also from theunderlying epistemology of mimesis towards poiesis, that is, towards developing students’productive thinking, creativity, and innovation. Wertheimer (2020) argues that productive
thinking is about gaining insights on a particular concrete problem and using reasoned logic
plus evidence to craft a solution. AI systems such as chat-GPT would be beneficial for
productive thinking and would reconfigure learning away from in-person contact sessions andteacher-centred approaches. Telagogy describes new roles for teachers (Table 1, below).
Table 1
Theoretical Interpretations of Teaching: Locating Telagogy
Traditional

Performativity

Psychologised

Critical

Invitational

TargetElite children

Meritocracy

Individuals

Informed
Citizenry

Enterprising
Individuals

CriticalPragmatism
Pragmatic
activists

Location

Discipline

Rules &
Regulations

TeacherLearner
Relationship

Material
Conditions

Material
Conditions

Material
Conditions

Epistemic
Position

Puristresearchtraditions
Subject
expertStrongpragmatism

Subjective
interpretation

SocialchangeStrongpragmatism

SocialidealismStandards
enforcer

Psychodiagnostic

Criticalintellectualism

Enabler &co-participant

Pragmatic
activist

Purpose

CulturalReproduction

Systemefficiency

Effective
teaching

Emancipati
onFreedom &independence

Just &
equitable
society

MethodLectureWork-based
Learning

Teamwork

ParticipatoryInquirydriven

Collective
problemsolving

TeacherRole

44

Telagogy
Social justicepioneers
OnlineCommunities& Global
Well-being
Socialidealism
Digitalmaterial &onlinelearning
techniquesdeveloper,
automateddigitalassessment
developer,
advisory
Global citizen+ strong
ontologicalreality
Streaming,
curation,cloudcollaboration,
immersive
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Towards Telagogy: Concepts, Contexts, and Content
The first computer-assisted learning started off in the early 1960s and was called Programmed
Logic for Automatic Teaching Operations (PLATO). It represented the initial prototype oflearning in a virtual space followed by blended learning, online learning and now remotelearning. These computer-based conceptions of learning would undergo, in the next decade,possibly their highest development in the form of online streaming of educational courses along
the lines of Spotify or Netflix making new demands on higher learning. Firstly, students are
more likely to curate their own curriculum drawing educational courses from universities
across the globe and in real time as earlier stated. Secondly, students would study at their own
pace, place of preference, anytime as well as anywhere as they undertake e-assessment
whenever they are ready. This learning becomes self-directed, self-determined, active, highly
flexible, and removes stress in learning. It is positioned at quadrant 3 (Diagram 3, below) thatmeans incorporating all three levels of learning – individual, collective, and local levels.
Bowden and Marton (1998) redefined IHEs mission of teaching, research, and community
engagement as learning at an individual level (traditionally called teaching), learning at acollective level (research) and learning at a local level (community level). At an individuallevel, students learn knowledge that is unknown to them but well known by others, experts,
and communities of practice. In Diagram 3 below, students move from unknown to known,that is unknown to them as individuals but broadly known knowledge to known, known, thatis, students reach a stage of knowing knowledge that is already well known through mostly
mimetic, replication learning epistemology. Telagogy does not seek to eliminate individual
learning but suggests that this knowledge already exists in AI systems such as chat-GPT. The
suggestion is that the first year of study could be dedicated to providing an overview of
disciplinary knowledge so that from advanced undergraduate level, students can begin to tackle
concrete community problems. Community problems compel for disciplinary knowledge
grounding (individual learning), research skills (collective learning) and tackling community
problems (local level learning). Once students learn this way then they develop “quality ofwill”, the scale of commitment to resolving community problems and “knowledge ofconsequences”, degree of knowing one’s complicity and abdication of responsibility when thecommunity problems remain unresolved. It is argued in this article that community problems
are known but ignored and thus remain vague and unattended. In resolving these community
problems, students develop productive thinking skills and technological savvy necessary as
globe-trotting individuals. It is expected that telagogy, at some point, would pursue blue sky
research in quadrant 4.

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Figure 1
Permutations of Telagogy

Telagogy is based on communities, both online and physical, thus its underlying essence is acommunity. This is based on the African concept of letsema/ilima. Letsema/ilima is a collective
assembling of community members driven by the spirit of ubuNtu (humanness, social justice)
to assist those less privileged in a community to stand up, rise and be able to do things forthemselves, that is, build a strong vukusenzele work ethic, rise and do it yourself. This explains
why telagogy needs an infusion of empowerment theory (Zimmermann, 2000). Empowerment
requires the following as described by the former Vice Chancellor of the South African
University of Cape Town and the co-founder of Black Consciousness Movement, Mamphela
Ramphele (2012):••

Substantial shift to global citizentry away from being treated as a subject. This meansthat one becomes a key agent of change in society who is inspired to tackle societal
challenges. In our African context, the journey to citizenry begins with a criticalawareness of woundedness from years of colonial ravages not only on land but also on
African collective dignity. Approaches to learning, in the African context and Global
South, have to take account of the deep-seated inequality and poverty hence the needfor the lestsema/ilima circle model that leverages firstly, the critical awareness ofexisting resources, knowledges and insights within a community that unleash citizens
fortitude to tackle their own problems. Secondly, generate ideas through ideational
bricolage that can be converted to real solutions of pressing community problems.
Ideational bricolage is the explicit identification of idle resources within a communitythat could help find solutions to pressing problems. Thirdly, apply solutions to address
identified community problems. Once the circle is completed then it can be iterated.
Develop a strong sense of autonomy, capabilities to do things on their own to gain
vukusenzele work ethic. This is similar to Zimmermann’s (2000) theory of
empowerment. Empowerment theory refers to a situation where communities or

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individuals overcome their psychological oppression, become increasingly aware ofcommunity problems and combine their capabilities to resolve these problems driven
by a strong letsema/ilima motif and the notion of vukusenzele work ethic. This
meaning of empowerment goes beyond the traditional psychological conception of
empowerment as increased self-esteem, self-efficacy, self-actualisation competences
and locus of control (Perkins, 1995, Zimmerman, 2000, Silverton, 2018).
Another area of telagogy worth our attention is the use of chat-GPT in influencing various
aspects of students’ learning. Studies have focused on student and teacher perceptions of chatGPT use, possible plagiarism and how it affects students’ motivation (Munoz et al, 2023). It isalso seen as critical in shifting students’ learning towards innovation and creative problem
solving. Advanced technologies have “ushered in a new era of innovation and creativity inseveral disciplines, including education” (Munoz, et al, 2023: 3). Studies show that when chatGPT is prompted for answers for assignment completion, it provided accurate responses to theprompted assignment question (Patel, 2023; Halaweh, 2023). Some universities are alreadydeveloping guidelines for use of chat-GPT in assignments. In this study, the relationships
between chat-GPT and student assignments was probed in terms of time of assignment
completion and how it assisted students to solve concrete problems. Figure 2 below describes
these relationships. In the first instance, students test chat-GPT for the assignments given tothem in their respective courses and as they do so, students measure the average time it takes
them to complete each item of the assignment and compare it with the average time of doing
the assignment manually. In the second instance, students prompt chat-GPT to assist them toframe a concrete problem in ways that helps them develop possible solutions. This includes
prompting for innovative solutions.
The starting point of the conceptual framework designed to investigate chat-GPT is an ecollaborative workspace assembled in similar fashion as letsema/ilima and applying the
vukusenzele work ethic. Letsena/Ilima e-collaborative workspaces are voluntary and selfdetermined, empowering community practices (Lebeloane and Baffour, 2008). In theseassemblages, students meet to resolve a theoretical or practical problem in a collaborativeenvironment employing the work ethic of vukuzenzele meaning arise and do it yourself in lieu
of being dependent on another person or conditioned to believe that another person has to be
in charge in order for a problem to be solved. In the context of students’ learning, it marks amajor shift towards students’ agentic power in the act of learning. Learning, under these
conditions, proceeds on the basis of either a theoretical or real problem. It involves further
prompts for chat-GPT on possible innovative solutions. Telagogy would be based mainly on
e-collaborative spaces that challenge real problems and less on theoretical problems. It has
strong shared ideas and shared solutions approaches to real problem solving. The next section
describes in detail a study using chat-GPT.

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Figure 2
Diagram of Telagogy

Methodology and Methods
The study is based on Empowerment Evaluation (EE) research method. It is an approach toresearch that helps participants to not only identify variables or gaps in literature thatcompromise ongoing project’s performance but also provides possible solutions to these
problems (Zimmermann, 2000; Fetterman, 1994; Fetterman, 2019). It makes use of qualitative,
quantitative, and even mixed methods approaches. The participants were drawn from a pool ofadvanced undergraduates from Chemical Engineering (N=5), Logistics (N=5), and Accounting(N=5) through use of a purposive sampling. Patton (2002) defines it as selection of data-richcases for effective use of limited resources. Teachers of second and third-year students wereapproached in each respective discipline to recommend students that could participate in the
study. It was emphasised that, in line with the ethical clearance, students would participate
voluntarily in the study and could leave anytime without any repercussions.
A qualitative EE method called Participatory Evaluation Design was used in this study which
consisted of the following phases: training, illumination, facilitation, advocacy, and liberation(Fetterman, 1994). Step 1 of the research design involves responsible planning which entails
involving research participants in planning the research. In this phase, students elaborate on
their understanding of chat-GPT, its use, and practical applications in their learning. For those
who had not been exposed to chat-GPT (about 30% of participants), basic training on use ofchat-GPT was conducted. It was downloaded on the laptops, provided to them during the
COVID-19 pandemic. Participants trained one another on the use of chat-GPT in their various
assignments. This training session was intended to forge collaboration and identify groupdynamics in team formation as per the framework developed by Pitso (2020) - pre-connectivity
connectivity, early superficial learning, intense interactivity, maturing, deep learning, andresolution. These team formation stages were also intended to serve as an analytical tool forinterpretation of collected data. The researcher intended to become a keen observer whointerviewed participants in each stage of team formation. Observation included carefully noting
participants’ interactions, particularly points of agreement, differences, and emerging themes

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of cooperation, democratisation of the collaboration space, inclusivity, openness, and degreeof flexibility and reflexivity. Interview questions covered issues relating to participants’solicitation of community views on the problem they sought to resolve, their experiences fromthis research and overall participation in the collaboration. Step 2 and step 3, involved findingout whether participants show a strong sense of owning the process, inclusivity, flexibility, andopenness to use and discuss issues relating to chat-GPT. Written semi-structured interviews
were conducted. Thereafter, participants were allowed to identify and select concrete problems
drawn from their own experiences and had to agree to tackle only one problem for the purposeof this research study. The problem had to emanate from a local community as one of therequirements of the study was for participants to solicit views of select community members.
Once such a consensus on a concrete problem was reached, participants prompted chat-GPT togain better understanding of the problem. Participants also visited a local non-profit
organisation to find out its views on the problem at hand. These interviews at the local nonprofit organisation were conducted once but ideally, it is suggested that more follow-up
interviews are necessary. These stages of research are called illumination and facilitation
respectively.
In step 4, students develop a solution prototype and showcase it (advocacy). In the last step,
students find a sense of satisfaction for resolving or creating conditions for the resolution of asocial problem, “quality of will” and the consequences of their actions are positive (liberation).
Written interview schedules were administered to students during the study and post-study.
Post-study interviews were intended to check lessons learned and whether participants wereready to embrace learning based on productive thinking, empowerment theory and AI systems.
Issues of validation of research are equally relevant in EE research methods and take the formof qualitative trustworthiness despite Fetterman’s (1995) argument that they are less importantin empowerment discourses. Truth-value and acknowledging inherent biases in qualitative
research designs and sampling techniques are essential in legitimising qualitative research(Noble & Smith, 2015). Truth-value refers to verifiability or falsity of a claim and triangulationor corroboration serves to make a determination of the veracity or mendacity of a claim. Useof two sources of data – observation and interviews – was considered adequate to corroborate
for verifiability. Accounting for possible biases was also done carefully, in addition toauditability meaning providing clearer and transparent description of the research process(Noble & Smith, 2015). Finally, there was a need to describe the context of the study and offer
thick as well as rich verbatim descriptions of participants’ views in support of findings.

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Figure 3
EE Participatory Evaluation Design

Results
Data collected in a qualitative study of 15 students that participated in testing the efficacy ofchat-GPT in students’ assignments and its use in solving a concrete problem were analysed todetermine:••

The relationship between students’ duration of completing an assignment using chatGPT as compared to manual completion of an assignment plus potential for plagiarism.
The relationship between students’ use of chat-GPT in attempting to solve a concrete
problem and the time of finding solutions was qualitatively tested through interviews
with participants. The interviews also included perspectives on whether concrete
problem solving helped them become innovativeness.

The variables such as age, socio-economic status of students, level of experience in using AI
systems and students’ nominal-scale variables such as gender, ethnicity and race were notconsidered for any meaningful analysis. This was essentially a qualitative, exploratory study.
It was intended to demonstrate how AI systems and empowerment theory infused into learningand teaching could enhance better understanding of AI systems and their applications in IHEs
within the framework of technology-as-essence. This framing of technology could allow
remote learning to develop to its next level of online streaming and thus provide opportunities
to theorise about learning and teaching beyond its current limitations.
The interview data shows that 70% of the participants, although it was not a consideration
when recruiting, were already using chat-GPT for assignments and were aware of theplagiarism consequences so avoided copying answers from chat-GPT. Most participants
indicate that they have had to include in their answers to assignments, information from othersources and curate such knowledge to fit assignment demands. “you gotta realise and becomeaware of penalties linked to copied text so adjust your answers accordingly” commented oneof the participants. In the use of chat-GPT, participants showed a good understanding ofplagiarism rules and made effort not to copy answers given to them by chat-GPT. These
interviews also show that chat-GPT substantially reduces the time to complete assignments
based on existing knowledge and is also capable of suggesting innovative ideas when the50

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problem is sufficiently specific. When asked whether participants found chat-GPT helpful, one
participant commented “chat-GPT saves time and allows one to focus on other things. ”
The students found the first stage of the EE method quite useful in familiarising themselveswith chat-GPT particularly those who were using it for the first time. While chat-GPT did not
substantially reduce the time for completing projects based on a concrete problem, it was usefulin clarifying the problem at hand which assisted students’ discussions on framing of the
problem.
In another study, chat-GPT proved to be better than students at creative problem-solving (Webbet al, 2023). In this study emphasis was on productive, creative problem-solving. The meaningof a problem offered by engineering students as a gap between a current situation and adesirable one was quite useful to students’ analysis of a concrete problem. A number of factors
were identified that caused a current situation to be less than desirable and the ideal situation
was described prior to seeking solutions even before prompting chat-GPT. My further
observation of participants in attempting to do their project was analysed in terms of Pitso’s
revised group dynamics process as used for human-machine collaborations (Pitso, 2020). The
Smart Team Formation Process (Pitso, 2020) includes:
1. Pre-Connectivity
This stage involves students familiarising themselves with an AI system underinvestigation (chat-GPT) and its role in assignments or problem-solving. It also
indicates, in early formations of the collaborative team, some anxieties, scepticism,doubt and cynicism from participants concerned with whether a multi-disciplinary team
could work together given its diverse knowledge bases and experiences. My
observation of this team was that the use of chat-GPT for purposes of familiarisation,
stage one of the method, tends to bring the participants closer when they all participate
in using chat-GPT to find answers for assignments of one group. For instance, when allof them become involved in seeing how Chat-GPT answers those specific engineering
assignments it helps bridge epistemic distances between students and enhances theirco-operative spirit in the collaboration spaces. “I never thought I could be involved in
an assignment from other courses, this was interesting”, a comment from a participant.
When introduced to a real, concrete problem, students have already begun to create
some understandings of how their discussions should proceed. I observed a greater
understanding in using chat-GPT and the increased confidence in the answers promptedfrom chat-GPT and the realisation that chat-GPT contains terabytes of great content
that helps them better understand the problem at hand (Stirling, 2020).
2. Connecting/Connectivity
An increasing interest in the use of chat-GPT to solve a concrete problem helped toreduce apprehension and doubt in the interactions between participants and chat-GPT.
Pitso’s (2020) study on group dynamics suggests that teams function better and interact
with AI systems confidently when there is a clear and lucid project that students

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undertake. It is in this stage that students discussed various community problems that
could be their project. Participants settled on potholes that have become a major
problem in the South African context compromising logistics and general well-beingof communities. When prompting chat-GPT on the meaning of a pothole, the following
responses were given by chat-GPT: [A type of a road surface damage that usuallyappears as a depression or hole. It is usually caused by wear and tear, weather
conditions, or the deterioration of road surface. Potholes can vary in size and shaperanging from small, shallow depressions to larger, deeper holes. Potholes form whenmoisture seeps into the road’s surface weakening the underlying layers]. While roadmaintenance and repair falls under the local government purview, most of them are
dysfunctional leading to persistent potholes problems. The most important issue about
the project is that none of the participants are trained on the particular problem at handand have to test the limits of their knowledge and experience. “I found the explanationsof a pothole quite useful and easy to find in chat-GPT”, one of the participantscommented. There was also an increasing confidence that the problem at hand could be
solved given the role of chat-GPT in providing ready-made answers.
3. Early Superficial Learning
Participants further probed chat-GPT on possible solutions with the following
responses: [patching for smaller holes, it involves filling the hole with materials suchas asphalt. Potholes filling machine...to fill potholes with hot asphalt. Cold mix asphalt,
ready to use mixture. Resurfacing or overlaying, add new layer of asphalt on existing
pavement. Proper road maintenance. Advanced road construction techniques such as
durable materials, better drainage systems, and proper designs.]. While thesesuggestions were important, most of them were at a level of municipalities that requiredhuge resources. Participants felt constrained by these chat-GPT responses. This was a
crucial moment where participants were exposed to which also showed the limits not
only of chat-GPT but also of reproductive knowledge. The greater realisation of thesuperficiality of such information and inadequacies in helping participants to solve apressing problem meant that participants had to go beyond current solutions of potholefilling.
4. Intense Interactivity
Participants were increasingly becoming aware that chat-GPT had its own limitations
in responding to some of the questions: “Obviously chat-GPT is unable to give us all
answers”, commented a participant. Yet, this was a crucial moment when students
ought to interact more intensely with chat-GPT to find possible solutions. My
observations confirm, the problem with participants at this stage, was that they haverarely been involved in activities that transcend their reproductive thinking box. Intenseinteraction with chat-GPT was restricted not by capabilities of chat-GPT but by
participants’ mastery of known knowledge which now proceeded to the use of chat-52

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GPT within the same reproductive vein. There was a need for epistemic border crossing
not at a level of disciplines but at the level of participants’ mindset.
5. Maturing
There was, however, greater focus on the project at this stage and participants were able
to overcome personal concerns, anxieties, and cynicism. The interview questions
focused on how participants felt at this stage of the study. “We are frustrated because
chat-GPT answers are for the municipality and we are expected to try out our own
solutions”. It is important to note that the response relates to a project and no longer onparticipation and initial use of chat-GPT. This was an important development towards
participants’ sense of growth and learning.
6. Deep Learning
The frustration with chat-GPT presumed inadequacies which, in essence, emanatedfrom participants’ reliance on hard-coded knowledge, although there was a need to
explore possible solutions outside this knowledge. Deep learning works on
unsupervised forms of learning which is commonly called machine learning capability
where AI systems are employed to help resolve real world problems known for theircomplexity, uncertainty and unpredictability. This was an overwhelming experience forparticipants and I have had to, as an active netnographic lurker, suggest that participants
prompt chat-GPT for innovative solutions to potholes filling and one of the responses
included self-healing potholes. This idea fascinated participants who wanted to know
more about it. The participants then prompted chat-GPT on self-healing potholes.
[These innovative technologies allow potholes to repair themselves without the needfor human intervention. Researchers are working on engineering asphalt mixes thathave the ability to self-heal. Some studies explore the use of bacteria that produce
calcium carbonate]. Participants, in their discussions had a strong propensity to research
this area of potholes further and involve Chemistry students. This is how far the study
could go with a huge potential for participants to be at the forefront of crafting
innovative solutions.
Resolution
This is the stage where participants develop prototypes on the unique idea they haveestablished. In the case of the study, participants would have joined the exploration ofself-healing potholes but brainstormed alternative asphalt mixes that do not include theones suggested by chat-GPT. They could also have calculated the costs of the idea andmade their findings public through showcasing. However, participants were unable toreach this stage because of time constraints.

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Discussion
The purpose of this study was to develop better understanding of remote learning as a “newnormal” in learning and teaching post-pandemic as well as explore possibilities of its advanced
form. The data collected in this study is of a qualitative nature making for lack of generality
and precision. However, conclusions of this study are relevant and important to IHEs struggling
to progress to the next level of remote learning and consider in-person contact sessions as relicsof the past. The synchronous remote learning conceptualised within technology-as-utility
framework is not sustainable given the impact of AI systems on learning and teaching that
increasingly shifts learning control to students. In this study, there was strong indication of
students taking active control of their learning through deciding their own project, moments ofprompting chat-GPT, discussions, suggestions and probing possible solutions. Post-study
interviews with participants show gain in greater control of their own learning. This is the mostimportant and emerging advantage of remote learning. I argue that remote learning would leadto even greater student control when it is conceptualised as asynchronous and AI systems
development occurs within technology-as-essence framework. This would enable students todecide when to access educational courses and projects, including from any university, decideon when to take examinations and from anywhere. When questioned post-study on the meaning of
this possibility, participants were a bit skeptical. This shows that it takes more time to shift fromtradition to new spaces of development. My observations of confident students tackling a real
problem and post-study interviews showed a glaring gap between attempts of research andreality as well as the power of institutional cultures. There is a glaring gap in research on AI
systems that needs further investigation. For example, the slow embracing of AI systems byparticipants particularly in terms of infusing them into their practices and the benefits accruedby students when using AI systems.
Participants also showed a positive progression through different stages of team formation
process. My observation shows that participants, with minor variations, were able todemonstrate features of each stage of this process. Chat-GPT showed potential in finding
innovative solutions to a real problem. Students were also able to sidestep plagiarism by not
only rewriting chat-GPT prompts but also included additional information from the textbooks.
Conclusion
The traditional focus of undergraduate studies on mastery of universal hard-coded knowledge
mostly bereft of cognitive content of communities of origin or of interest to students require
serious attention (McGhie, 2012) given that hard-coded knowledge is already available in AI
systems such as chat-GPT. There is no point in trying to take students through this hard-codedknowledge over the stretch of three years when the use of chat-GPT in the first year ofundergraduates studies could cover it within a year. Advanced undergraduate studies could
then focus on the development of productive thinking through focus on concrete community
problems with a strong understanding that learning is socially situated and constructed (Akpan
et al, 2020). Productive thinking thrives on a Gestalt, holistic approach that concrete
community problems offer. Other studies on socially situated and constructed learning

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accentuate learning that is a social and cultural process occurring in the context of humanrelationships and activities. This view assumes that a socio-cultural context should be the basisof student participation in the affairs of their communities, first summon knowledge fromwithin these communities and supplement it with formal hard-coded knowledge (Lave andWenger, 1991; Akpan et al, 2020). The traditional undergraduate studies that focused on “inthe-head” learning that tended to reduce human mental potential to information-processing
machine and a storage device with a strong memory strength that contained highly developed
retrieval mechanisms has come under intense pressure in this era. AI systems have rendered
this approach to learning unnecessary as such knowledge can now be easily accessed via AI
systems. The undergraduates’ mind can be put to better use which, actually, could play apositive role in their communities. New learning that is suggested in this article referred to as
telagogy draws significantly from learning that emphasises students’ collaborative effort thatleverages their interactions, knowledge sharing, collective research, critical discussions anduse of AI systems in attempts to resolve concrete community problems. Telagogy is therefore,
an online streaming of educational courses sourced from various universities across the globe
and available for access anytime and from anywhere. It makes use of advanced AI systems
such as chat-GPT to curate and prepare knowledge for creative complex responsive processes
that include localised insights and resources for creative, complex responsive processes. It
needs further research and development.
Recommendations
The investigation undertaken in this article was twofold. It sought to better understand students
learning from the asynchronous and technology-as-essence framework. The investigation
sought to transcend remote learning research that tended to lock its debates on emergency/crisis
remote learning implementation during the pandemic. It is suggested that further research isnecessary to qualitatively and quantitatively investigate the efficacy of this framing of students’
online learning as asynchronous and open to being shaped by continual advances in technology.
There is also a need for IHEs to develop guidelines for use of AI systems such as chat-GPT on
enhancing students learning. Advanced remote learning offers opportunities for IHEs to shift
undergraduate studies away from mimetic epistemologies of replication to poiesis with a strong
motif of creativity, innovation and complex problem solving. This also shift students’ learningfrom developing reproductive thinking towards productive thinking. The suggestion is that
reproductive thinking can be developed in the first-year of students learning and advanced
undergraduates ought to increasingly focus on productive thinking. EE research methods alsoneed to be refined to fit into researching learning and teaching.

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References
Akpan, V., Iqwe,U., Mpamah, I., & Okoro, C. (2020). Social constructivism: Implications onteaching and learning. British Journal of Education.
Almatutairi, F., & Ghuloum, H. (2021). A novel framework for facilitating emergency
remote learning during the Covid-19 pandemic. International Education Studies
14(5), 121–134. https://doi.org/10.5539/ies.v14n5p121
Appiah, K. (2005). The Ethics of Identity. Princeton. Princeton University Press.
Alavi, S., Abuja, V., & Medury, Y. (2010). Building participation, reciprocity & trust:
Netnography of online community of APPLE using regression analysis for prediction.
Apeeje Business Review, 11, 82–96.
Asa Ryegard, K., & Olsson, T. (2010). A Swedish Perspectives on Pedagogical Competence.
Copenhagen, Denmark: Dansk University.
Banholzer, V. (2022). From industry 4.0 to society 5.0 & industry 5.0: Value and missionoriented policies. IKOM Working Paper.
Bengry-Howell, A., Wiles, R., Nind, W., & Crow, G. (2011). A Review of the Academic
Impact of Three Methodological Innovations: Netnography, Child-led Research andCreative Research Methods. Accessed from https://www.ncrm.ac.uk/
Bernstein, B. (1990). Class, Code & Control. Vol. 4 The Structuring of Pedagogic Discourse.
Abington: Routledge.
Boshier, R., & Huang, Y. (2008). In the house of the scholarship of teaching and learning(SoTL), teaching lives upstairs and learning in the basement. Teaching in Higher
Education, 13(6), 645–656. https://doi.org/10.1080/13562510802452368
Bowden, J., & Marton, F. (1998). The university of learning: Beyond quality andcompetence. Education + Training, 41(5). https://doi.org/10.1108/et.1999.41.5.ii.2
Anderson, T. (2016). Getting the most from the learning management system Accessed
01/03/2022 from https://teachonline.cg
Boyer, E. (1990). Scholarship reconsidered: Priorities of the professoriate. New York: The
Carnegie Foundation for the Advancement of Teaching.
Ball, S. (2012). Performativity, commodification & commitment. An I-spy guide to the neoliberal university. British Journal of Education Studies, 60(1), 17–28.
https://doi.org/10.1080/00071005.2011.650940
Bayne, S., Evans, P., Ewins, R., Knox, J., Lamb, J., Macleod, H., O’Shea, C., Ross, J., Sheail,P., & Sinclair C. (2020). The Manifest for Teaching Online. Cambridge: MIT Press.
https://doi.org/10.7551/mitpress/11840.001.0001
Beauvoir, S. de (1947). The Ethics of Ambiguity. New York: Philosophical Library, Citadel.
Blasche, L. (2012). Heutagogy and lifelong learning: A review of heutagogic practice andself-determined learning. International Review of Research in Open and Distance
Learning, 13(1), 57–70. https://doi.org/10.19173/irrodl.v13i1.1076
Brown, A. (1995). Advances in learning and instruction. Education Researcher, 23(4): 4–12.
https://doi.org/10.3102/0013189X023008004
Butler, D. (2020). Designing Sustainable Digital Transformation of Learning. Accessed
01/03/2022 from https://www.beyond-events.eu

56

IAFOR Journal of Education: Technology in Education

Volume 11 – Issue 2 – 2023

Buxton, E. (2014). Pharmacists’ perception of synchronous versus asynchronous distancelearning for continuing education. American Journal of Pharmaceutical Education
78(1), 1–9. https://doi.org/10.5688/ajpe7818
Creswell, J,. & Plano Clark, W. (2011). Designing and Conducting Mixed Method Research
2nd Eds. Thousand Oaks: SAGE.
Costello, L., McDermott, M., & Wallace, R. (2017). Netnography: Range of practices,misperceptions and missed opportunities. International Journal of Qualitative
Methods, 16, 1–12. https://doi.org/10.1177/1609406917700647
Elliot, G. (2012). Policy, performativity & partnership: An ethical leadership perspective.
Research in Post-Compulsory Education, 17(4), 423–433.
https://doi.org/10.1080/13596748.2012.738977
Gardner, H. (1991). The Unschooled Mind. New York: Basic Books.
Hayes, D. (2017). Beyond McDonaldization of Higher Education. Abingdon, UK: Routledge.
https://doi.org/10.4324/9781315270654
Fetterman, D. (1994). “Empowerment Evaluation”. Evaluation Practice, 15(1), 1.15.
https://doi.org/10.1177/109821409401500101
Fetterman, D. (2019). Empowerment evaluation: A stakeholder involvement approach. Wiley
Australian Health Promotion, 30(2), 137–142. https://doi.org/10.1002/hpja.243
Halaweh, M. (2023). Chat-GPT in education. Strategies for responsible implementation.
Contemporary Educational Technology, 15(2), 1-11.
https://doi.org/10.30935/cedtech/13036
Halupa, C. (2015). Pedagogy, Andragogy & Heutagogy. Accessed 01/03/2022 fromwww.researchgate.net.
Heidegger, M. (1954, English translation, 1977). The Question Concerning Technology. New
York: Garland Publishing.
Heidegger, M. (1935, Translation, 1959). Introduction to Metaphysics. Yale: Yale university
Press.
Junco, R., Heibegger, G., & Lokan, G. (2010). The effects of Twitter on college students
engagement and grades. Journal of Computer Assisted Learning, 27(2), 119-132.
https://doi.org/10.1111/j.1365-2729.2010.00387.x
Koester, A. (1967). Ghosts in the Machine. UK: one 70 Books.
Kohlberg, L. (1981). Essays on Moral Development, Vol. 1: The Philosophy of Moral
Development. San Francisco: Harper & Row.
Lave, J., & Wenger, E. (1991). Situated Learning: Legitimate Peripheral Participation.
Cambridge: Cambridge University Press.
https://doi.org/10.1017/CBO9780511815355
Liew, W. Effects beyond effectiveness: Teaching as a performative act. Curriculum Inquiry
43(2), 261–288.
Lebeloane, L., & Baffour, K. (2008). Letsema: A way of inculcating African and preserving
African indigenous knowledge in the youth through formal education in the 21st
century. Journal of Educational Studies, 7(2), 43–49.

57

IAFOR Journal of Education: Technology in Education

Volume 11 – Issue 2 – 2023

Lynch, K. (2010). Carelessness: A hidden doxa of higher education. Arts & Humanities inHigher Education, 9(1), 54–67. https://doi.org/10.1177/1474022209350104
McGhie, V. (2012). Factors Impacting on First-Year Students’ Academic Progress in a South
African University. Stellenbosch: Doctoral Thesis.
Marton, F., Alba, D., Elizabeth, G. (1993). Conceptions of learning. International Journal of
Educational Research, 19(3), 277–300.
Munoz, S., Gayoso, G., Huambo, A., Tapia, R., & Incaluque, J. (2023). Examining theimpacts of Chat-GPT on students’ motivation and engagement. Social Space, 23(1),1–28.
Neuwirth, L., Svetlana, J., & Mukherji, B. (2021). Reimagining higher education during andpost-Covid-19: Challenges & opportunities. Journal of Adult & Continuing
Education, 27(2), 141–156. https://doi.org/10.1177/1477971420947738
Noble, H., & Smith, J. (2015). Issues of validity and reliability in qualitative research.
Evidence-Based Nursing, 18(2), 34–35. https://doi.org/10.1136/eb-2015-102054
Nussbaum, M. (1996). For the Love of Country. Boston, MA: Beacon Press.
National Forum: For the Enhancement of Teaching and Learning in higher Education (2020).
Accessed 01/03/2022 from www.teachingandlearning.ie
Nussbaum, M. (1996). For the Love of Country. Boston, MA: Beacon Press.
Perkins, D. (1995). Empowerment, theory, research and application. American Journal ofCommunity Psychology, 23(5), 569–579. https://doi.org/10.1007/BF02506982
Pitso, T., Lebusa, M., & Kok, L. (2014). Accounting for undergraduates’ teaching
perspectives in a scholarly teaching encounter. South African Journal of Higher
Education, 28(4). https://doi.org/10.20853/28-4-397
Pitso, T. (2018). Shared futures: Exploring the collaborative potential of intelligent
technologies and human ingenuity in cocreating value. In Sílvio Manuel Brito (Ed.)
Toward Super-Creativity. https://doi.org/10.5772/intechopen.85054
Pitso, T. (2021). Contextualised critical reflections on academic development practices:
towards professional learning. Stellenbosch, South Africa: African Sun Media.
Pitso, T. (2023). The Forgotten: Reconstructing and Reclaiming African Spirituality in the
Post-truth Era. Stellenbosch, South Africa: African Sun Media.
https://doi.org/10.2307/j.ctv1nzfwkj
Power, M. (2014). Do scholars need performance free time & space? Times Higher
Education. Accessed 01/03/2022 from http://www.timeshighereducation.co.uk.
Mamphela, R. (2012). Unleashing the power of communities to confront health inequities. A
Accessed 01/07/2022 from letsemletsemacircl.com
Reynolds, K. (2015). Performativity in higher education: An autoethnographic commentary& call to servant leadership. Proceedings of Teaching & Learning Conference held at
London School of Business & Management. Accessed fromhttps://www.academia.edu/35893093/Performativity_in_Higher_Education_An_Auto
ethnographic_Commentary_and_Call_to_Servant_Leadership_in_UKHE
Silverton, C. (2018). From reflexivity to collaboration: Changing roles of a Romani scholar,
activist and performer. Critical Romani Studies, 1(2), 76–97.

58

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Volume 11 – Issue 2 – 2023

Skelton, A. (2005). Understanding Teaching Excellence in Higher Education. London:
Routledge.
Tjabane, M. (2021). Evaluating concepts of teaching excellence in higher education. In T.
Pitso (Ed.), Contextualised Critical Reflections on Academic Development Practices:
towards Professional Learning. Stellenbosch, South Africa: African Sun Media
Publications. https://doi.org/10.2307/j.ctv1nzfwkj.7
Torres, R., & Cruz, O.-D. (2022). Remote learning: Challenges & opportunities for educators& students under the new normal. Anatolian Journal of Education, 7(1), 83–92.
https://doi.org/10.29333/aje.2022.717a
Vosniadou, S. (1996). Towards a revised cognitive psychology for new advances in learningand instruction. Learning and Instruction, 6(2), 95–109.
https://doi.org/10.1016/0959-4752(96)00008-4
Warren, J. (2023). Chat-GPT Reveals Uncomfortable Truth about Undergraduate Skills.
Accessed 03/08/2023 from www.timeshighereducation.co.uk
Watkins, C., Carnell, E., & Lodge, C. (2007). Effective Learning in Classrooms. London:
Paul Chapman Publishing.
Watts, L. (2016). Synchronous and asynchronous communication in distance learning. A
review of the literature. Quarterly Review of Distance education, 17(1), 23–32.
Webb, T., Holyoak, K., & Lu, H. (2023). Emergent analogical reasoning in large language
models. Nat Hum Behav (2023). https://doi.org/10.1038/s41562-023-01659-w
Wright, I. (2020). In Peak of Higher Education – A New World for the University of the
Future Report. Accessed on 14 /07/2022 from available@ey.com
Zimmerman, M.A. (2000). Empowerment Theory. In: Rappaport, J., Seidman, E. (Eds.)
Handbook of Community Psychology. Springer, Boston, MA.
https://doi.org/10.1007/978-1-4615-4193-6_2
Corresponding author: Teboho Pitso
Email: biki@vut.ac.za

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Teaching Avatars on Controversial Issues: Lessons Learned
Per-Olof Hansson
Linköping University, Sweden
Marcus Samuelsson
Linköping University, Sweden
Marie-Louise Höög
Linköping University, Sweden

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Abstract
This paper describes and evaluates student teachers’ virtual simulation training on teaching acontroversial issue. In the fourth year of their program to become social science teachers at
lower and upper secondary schools, 43 student teachers in Sweden conducted simulationteaching on conspiracy theories as an example of a controversial issue. Conspiracy theories
appeal to young people and they often encounter these theories online, but they can be met withincreased knowledge about how conspiracy theories work, and how they can be identified andcountered. Thus, students at primary and secondary school need to develop their critical source
skills. The Swedish Schools Inspectorate (2022) found that these issues were not properly
taught because they were not connected to schools’ values-based work or to the developmentof students’ democratic competence. To analyze the simulation teaching, data was collected
through observations, video-recorded simulation teaching, interviews with student teachers,
and reflective documents. The results show that simulation teaching offers student teachers theopportunity to integrate content knowledge, pedagogical content knowledge, and subject
knowledge, by being trained to become flexible and responsive to avatars’ individual
differences as well as their different attitudes and understanding of the subject.
Keywords: conspiracy theories, controversial issues, simulation teaching, student teachers,
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The Swedish Schools Inspectorate (2022) found that the teaching of social studies and biology
on controversial issues needs to be developed at 21 out of 30 inspected schools. The reasonsfor this were (a) the students do not have teacher-led conversations about issues that may becontroversial, (b) the classroom climate does not favor participation in interaction about
controversial issues, and (c) there are gender differences that inhibit girls. They also found thatthese issues were not connected to schools’ values-based work or the development of students’democratic competence. This paper directs attention to the aforementioned factors byinvestigating simulation training with future teachers of social science on conspiracy theories
as an example of content that can be controversial in the classroom (Douglas & Sutton, 2023).
In a complex information landscape with increased access to the Internet and increased
information flow, social media feeds conspiracy theories in both democratic and nondemocratic contexts. Social media mechanisms – such as followers, likes, shares, tweets, andinfluencers – designed with a focus on viral effects, whereby users can produce, publish, andshare falsified content (Avramov et al., 2020), create a risk of preventing good communicationand making reliable information difficult to distinguish from fake news and hoaxes (Bezael,2022). Önnerfors (2021) argues that conspiracy theories have increased during the pandemic.
Conspiracy theories appeal to young people and they often encounter these theories online.
However, they can be met with increased knowledge about how conspiracy theories work, andhow they can be identified and countered. Thus, students at lower and upper secondary schools
need to develop their critical source skills. This includes listening to other peoples’ opinions
and perspectives that differ from their own values, respecting them, and learning to make wellgrounded decisions.
Given the above, we as teacher educators need to reflect upon how future teachers can develop
their ability to teach source criticism, controversial issues, and conspiracy theories. Usually,
training for such skills and abilities is imparted during teaching practice at schools. However,researchers claim that the training that takes place at schools is insufficient, and as a result
student teachers are given too few opportunities to develop their skills and abilities to be able
to handle a complex classroom situation (McDonald et al., 2013; Westbury et al., 2005).
Campus-based teacher education has a strong focus on concepts, theory, and models that
support the development of analytical ability. Knowledge of relevant theories, such asconspiracy theories, in university courses is described as being difficult to connect to theprofession and school practice (Lindqvist, et al., 2019). Grossman, Hammerness, andMcDonald (2009) emphasize that teacher education needs to help students make thisconnection. Their argument is that the practical training is insufficient, and that there is also aneed to make practical training possible to a greater extent on campus. In this way, theanalytical side of the profession and its action-oriented side can be combined (Grossman, etal., 2009; Jank & Meyer, 2004; Ade-Ojo, et al., 2021). A successful method that has been usedin an attempt to link the two sides of the teaching profession and bridge the perceived gap
between theory on campus and school practice or internship, is simulation training organized
as a virtual practice (Samuelsson, et al., 2021).

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The aim of this paper is to describe and analyze student teachers’ simulation teaching oncontroversial issues. To achieve this aim, the research question is ‘in what ways can teachingin a virtual space develop student teachers’ general knowledge, pedagogical content
knowledge, and content knowledge about conspiracy theories and promote avatars’ criticalthinking?’
BackgroundResearch on conspiracy theories is mainly found in the fields of social psychology, politicalscience, history, religious studies, and anthropology (Butter & Knight, 2020; Önnerfors, 2021).
This means that only a few research projects have been conducted in education, pedagogy, andsocial studies. This research shows the value of developing students’ understanding of theirown and others’ values in a multicultural society, but also how the teacher chooses to avoid orembrace controversial issues in teaching (Flensner, 2020). Teachers may find a controversial
issue difficult and may hesitate to engage in in-depth discussions for fear of upsetting students
and their parents (Kaka, et al., 2021).
In social science subjects, teaching often engages feelings and emotionally challenging
discussions. Many discussions involve controversial issues such as politics, migration policy,
terrorism, climate change, and so on. Controversial issues are defined by the Council of Europe(2016, p. 8) as “issues which arouse strong feelings and divide opinion in communities andsociety”. These issues raise pedagogical questions for teachers, such as how to create a safe
learning environment to prevent friction in the classroom, how to protect the sensitivities ofpupils from different backgrounds and cultures, how to encourage active participation, and therole of teacher’s own beliefs and values. Sandahl (2020, p. 21) argues: “if we want to contributeto students’ citizenship education and give them tools to tackle one-sided viewpoints about thegood society, we need to challenge their views.”
Leadership in the classroom, often referred to as classroom management, is difficult because
the teacher needs to create an open and permissive atmosphere, and encourage constructive
student dialogue, including the use of effective questioning strategies (cf. Granström, 2007;
Lewis, 2008; Wubbels, 2011). Therefore, it is important to increase student teachers’
confidence by teaching them strategies that promote open and respectful dialogue in theclassroom and not only obtaining knowledge through course literature and lectures (cf. Kounin,
1970; Alexander, 2008; Hamre, et al., 2013). Hence, in this study the student teachers weretrained to perceive, interpret, and make decisions about controversies, such as conspiracy
theories, during teaching in a virtual space.
Virtual practice – teacher-led simulation teaching with virtual characters – was an attempt tobuild student teachers’ ability to teach as part of a campus course (Samuelsson, et al., 2022).
The training was carried out around difficult but necessary content for teaching, with elements
such as fraction calculation or conspiracy theories, in a safe and permissive environment underguidance of knowledgeable teacher educators. Virtual practice reduces complexity andreinforces other aspects of teaching in well-defined exercises of commonly occurring situations

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(Arvola, et al., 2018; Mason, et al., 2011). In this way, virtual practice creates a safe
environment for learning, which provides the opportunity to try (Dawson & Lignugaris/Kraft,
2017; Ledger & Fischetti, 2019) ways of leading and managing the classroom (Bradley &Kendall, 2014; Piro & O’Callaghan, 2018) without the risk of negatively affecting real
students. Virtual practice can augment regular teaching, lectures, and seminars, and can address
their lack of precision in terms of action orientation (Dieker, et al. 2014; Bondie, et al., 2021)
and strengthen confidence in teaching abilities (Ledger & Fischetti, 2019; Samuelsson, et al.,2021). Research on simulations, virtual practice, and virtual characters as a means to train anddevelop teaching skills has partly focused on student teachers’ self-confidence linked toteaching subject content (cf. Chini, et al., 2016; Ledger, et al., 2019), leadership in theclassroom (cf. Hudson, et al., 2019; Smith & Klumper, 2018), or both these aspects(Samuelsson, et al., 2021; Samuelsson, et al., 2022). Usually, student teachers practice theability to teach as a way of learning how to do teaching. In other cases, simulation training is
combined with modeling where the students have previously seen an experienced teacher teacha certain subject matter (Bautista & Boone, 2015). The authors found that modeling has greatsignificance for students’ development of teaching, handling challenging students, and being
able to answer the avatars’ questions in a constructive way. Similarly, various forms of oral
and written feedback have positively affected students’ confidence in their ability to teach(Gundel, et al., 2019; Samuelsson, et al., 2022). The content and quality of feedback can be
described as a key reason why a limited amount of training in virtual practice shows such goodresults for student development.
Theoretical Framework
To analyze the student teacher’s simulation training about conspiracy theories, we used Lee
Shulman’s (1986; 1987) framework of aspects of a knowledge base for teaching. Shulman
divides the knowledge base into (a) content knowledge; (b) general pedagogical knowledge –a broad repertoire of principles and strategies for organization and executing classroom
management; (c) curriculum knowledge – a focus on tools such as materials, representations,and programs; (d) pedagogical content knowledge – teachers’ converted professional
understanding of the content; (e) knowledge of students and their characteristics; (f)educational context knowledge – experience from communities and cultures based on working
with groups, in classroom at schools; (g) knowledge of educational ends – a focus on the
purpose and values of teaching, as well as their historical and philosophical roots (Shulman,
1987). The categories - educational context knowledge and knowledge of educational ends,
knowledge of students and their characteristics, and educational context knowledge were notin line with the course where the simulation training was used and neither the purpose. Thosecategories were therefore disregarded in the analysis. We focused on three categories – content
knowledge, general pedagogical knowledge, and pedagogical content knowledge – both
separately and as overlapping issues (see Figure 1).

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Figure 1
Aspects of the Knowledge Base for Teaching (Shulman, 1987)

Generalpedagogicalknowledge

Pedagogicalcontent
knowledge

Content
knowledge

Methodology
The Program
Twenty student teachers in the social science program for lower and upper secondary school
started a course worth 15 ECTS in August 2021, and an additional 23 student teachers started
in August 2022. This course is the last subject course, including teaching methods, of a fiveyear teacher training program. The ten-week course focused on controversial issues, criticalthinking, the role of the media, digitalization, and assessment. Examinations took the form oforal seminars, written exams, and conducting a project on controversial issues in small groups.
However, part of the course also included teaching avatars in the simulation on conspiracy
theories and critical thinking. The lecturers prepared the students with literature seminars andlectures on controversial issues, conspiracy theories, populism, and so on. The students planned
their forthcoming lesson with the avatars in pairs. The assignment was to plan the first lesson
for lower or upper secondary school students on conspiracy theories about the pandemic,
vaccinations, fake news, and source criticism. More specifically, the student teachers received
the following instructions: Remember that this is the first lesson of the unit, and it is importantto create curiosity, motivation, and engagement among your avatars. Divide the lesson between
you and your student colleague, and practice before the simulation teaching. You will lead thelesson you planned for 30 minutes, thus 15 minutes per student. After completing the teaching,
you will discuss the implementation with the tutors and prepare for the follow-up seminar bywriting down your reflections.
The feedback given after each session of virtual practice was arranged with inspiration fromthe After Action Review (AAR) technique. It was arranged around three questions that each
student teacher was asked to answer: (a) what went well, (b) what could have been done
differently, and (c) what did you learn from teaching about conspiracy theories to avatars?
Questions like these have previously been used successfully adjacent to simulation training

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(Scoresby & Shelton, 2014) and in teacher education (Dieker, et al., 2013; Samuelsson, et al.,2021; Samuelsson, et al., 2022).
Simulation Teaching
In order to train the student teachers to perceive, interpret, and make decisions about ideas andconceptions regarding controversial issues such as conspiracy theories, we designed a semivirtual simulation called TeachLivE where they could teach avatars (see Figure 2). The
simulation contains several environments including variety of classrooms and avatars, –children, young adults, and adults – with different personalities (Long, 1989; Hayes, et al.,2013). Based on earlier experiences with simulation training (Arvola, et al., 2018; Samuelsson
et al., 2022) we thought that TeachLivE and simulation training would serve well for the
training of our Swedish student teachers. Other forms of training such as teaching peers withthe use of roleplay was not considered, based on to earlier research (Samuelsson, et al., 2021).
Simulation with avatars that look and behave like students might provide a more embodied
experience than imagining your peers to be your students in role play. The simulation specialistthat operate the avatars “stays in character” despite what happens during training while peers
might “lose character” due to relational aspects and a sense of play rather than education.
Figure 2
Screenshot from a TeachLivE Session with Five Avatars

Each student teacher had to teach five avatars with different personalities and different ideas
about the content. That way they had some knowledge about the learners and theircharacteristics (Shulman, 1987). The student teachers were also given information about (a)what the avatars could do (interact verbally, carry out non-verbal behaviors, take notes, send,
and receive text-messages on their mobile phones, and talk to each other) and (b) what they
could not do (leave the classroom or change places).

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Before the student teachers entered the simulation, a simulation specialist made elaborate
preparations – in collaboration with the authors – in order to represent different commonlyoccurring ways in which students at lower and upper secondary schools understand, feel about,and reason about the content. The avatars represented and expressed commonly occurring ideas
among school students about conspiracy theories (Önnerfors, 2021). The conspiracy theories
content that the student teachers were assigned to work with had been constructed and tested
with the authors and five teachers teaching in high schools. Based on the results of the testing,the content was adjusted and developed to train the student teachers to teach about conspiracy
theories.
The avatars’ feelings and understandings were designed as different push-backs (see Table 1),
closely aligned to the course goal about what the student teacher was expected to learn andachieve during the teacher training course in social sciences.
Table 1
Push-Backs about Conspiracy Theories
Virtualstudent

Push-backs

Ava

Lack of critical thinking: Is Covid-19 really that dangerous? Brings up
Nicki Minaj as a vaccine skeptic influencer.

Dev

Testing the student’s knowledge by asking and answering questions basedon facts: There are conspiracy theories that turned out to be true. Can wetalk more about Trump and QAnon? How about conspiracy theories in the
Middle East?

Ethan

Provocative comments: You are all brain-washed, the media obscuresreality.

JasmineSilent observer: Asks Dev to explain and help her with answers.

SavannahSensitive reactions: Should vaccines be mandatory? My grandfather’sfriend died from Covid-19, it is real.

The simulation specialist acts as a puppeteer and manages each of the five avatars, bringing
them to life. This was done simultaneously as the specialist saw and heard the student teachers
on Zoom. Such a solution makes it possible for synchronous interaction between the simulation
specialist and participants (Dieker, et al., 2016; Ersozulu, et al., 2021). The compliance andresponse make it possible to adapt the training to student teachers’ different abilities, and create
a feeling of authenticity, which affects the quality of the simulation (Bondie, et al., 2021).

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Data Collection
Data was collected from two student cohorts (2021 and 2022). In total, 43 student teachers
majoring in social sciences participated in the study (see Table 2). The researchers observed
the simulation while it was ongoing (30 min each session), conducted pair interviews (30-45
min) with student teachers afterwards, and watched the videorecorded teaching sessions. The
student pair also compiled reflective notes after the session. In addition, all students met the
day after their simulation teaching to sum up their experiences. Notes from group discussions
were collected and form part of the analysis. The second cohort from 2022 were alsointerviewed after all parts of the simulation training.
Table 2
Data Collection 2021 and 2022
Social Science
courseObservation &video recording

Interview

Reflection notes Group discussion
notes

2021

20

-

20

5

2022

23*

11

23

5

* Students teach in pairs, but one group had three students. All students taught 15 min/each.

Data Analysis
The analysis is based on qualitative data with the aim of a holistic perspective. Rich data and asystematic search for categories are at the core of qualitative content analysis, and a reductionof data and systematization are important (Schreier, 2014). Five stages of analysis were used.
Firstly, individual notes were taken from the observations and the video recordings in thevirtual practice. Interviews were transcribed. Secondly, the text was labelled and systematicallycoded, individually by the first and second author. Thirdly, codes were reduced when they werecompared between the researchers, and then categorized. Fourthly, codes and categories wererelated to the research question and relevant studies. Finally, notes from group discussions wererelated to each other and included in the categorization. The analysis of the empirical data waspresented in three categories in line with Shulman’s aspects of knowledge.
Results
The findings are presented based on Shulman’s theory of knowledge base for teaching, as
described earlier. The empirical data are presented according to the categories: (a) generalknowledge, (b) pedagogical content knowledge, and (c) content knowledge. The datasometimes overlap and intersect as Figure 1 shows, but this is also discussed. We illustrateeach knowledge aspect with quotations from the interviews, reflective documents, and thenotes from follow-up group discussions.

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General Pedagogical Knowledge
This aspect of knowledge emphasizes leadership and classroom management. In the simulationteaching, the student teachers created a good atmosphere and a good classroom climate. Thiswas done with a friendly tone and a willingness to listen to the pupils. Our feedback
conversations show that the student teachers felt that they learned about their own attitudes andapproaches. They said that this was thanks to the alignment of the exercise: first planning thelesson, second teaching in the simulation, third observing peers’ teaching, fourth the reflective
discussion with a teacher educator, and lastly reflections with the other student in the pair andother students in the class. The overall learning experience for the student is that a teacher must
be flexible and responsive: flexible enough to change the lesson plan if the pupils have thoughts
and attitudes that can make the teaching more interesting for them, but also responsive in
relation to the pupils’ sphere of life. This was not done to a high degree in the simulation.
Naturally, it can depend on the context and the teaching avatars, but there were opportunities
to use the pupils’ own thoughts and ideas. Two students mentioned: “There is a balancing act
between following your plan and letting the students’ thoughts (which may not always follow
the plan) take place in the classroom.” (Students A and B).
The content was new for the students to teach, but our analysis suggests that they managed theclassroom well. The student teachers followed their planning, which established a structure forthe lesson, and provided something for the student to hold on to when the pupils tried tomaneuver in other directions than planned. However, it is unclear what the pupils understoodduring teaching. The students did not follow up the objective of the lesson properly, and thus
there was no summarizing or conclusion of the learning objective.
One challenge in the simulation teaching involves dealing with pupils’ differences inunderstanding, feeling about, and reasoning about the content. Even though there were only
five avatars, it was a clear challenge for the student teachers to manage them. Thus, theyexperienced difficulties involving all the pupils. The student teachers had obvious problems
and a lack of strategies to involve the shy pupil (called Jasmine, see Table 1). They also had
problems handling the two boys called Ethan and Dev (see Table 1), who were more interactive
than the girls. Once again, this can depend on the simulation itself, whereby teachers cannot
approach individual students by sitting next to them, for example. This limited the strategies
they could use, but instead of waiting for the shy pupil to answer the question, the teachers
went on to another pupil.
Further, the student teachers had unclear actions towards troublesome behaviors, for example,
speaking before raising a hand, negative attitudes, disagreement between the pupils, use ofmobile phones, or pupils falling asleep during the lesson. One student expressed the following:“I learned about the challenge of distributing the speaking space in a good way between both
quiet and more talkative students. There was a particular challenge of getting a quiet student towant to talk.” (Student F). Another student wrote: “I learned (got the opportunity to practice)
to handle difficult student situations, i.e., when the students got sidetracked or when thestudents made personal attacks against each other.” (Student G).

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Hence, one learning experience from the simulation involved encountering both disruptive andquiet pupils.
Pedagogical Content Knowledge
This knowledge form relates to having teaching methods that are appropriate in relation to theage group and its content. In the simulation, the student teachers tried to involve the pupils in
the lesson by asking questions. However, there were only questions and answers on an
individual level, making it difficult to engage all pupils in the same discussion. The student
teachers lacked proper follow-up questions that could give further explanations about what had
just been said. The teachers listened to the answers, but rarely used the answers to find out
about other pupils’ opinions on the same topic. In addition, it was difficult for the student
teachers to decide about the relevance of pupils’ questions and their answers. As mentioned,
there was a willingness to include the pupils, but also a lack of action in terms of deciding when
to interrupt a conversation or statement which was irrelevant to the lesson. The reasons for this
could be that the student teachers had unclear purposes with the lessons and not enoughknowledge of the subject. The subject was not clearly described in the beginning of the lesson
and not summarized at the end. In the reflective notes, student C stated: “It is important to know
where to go in the lesson in order to stay on topic and limit what is important for this lesson.”Group 3 noted that: “we felt the importance of subject didactics.” With that said, the student
teachers need to expand their toolbox of methods, giving them a broader repertoire to selectfrom and the possibility to use a variety of methods if the current method does not work well.
In the simulation, the methods were questions from the teachers with the pupils answering them
individually, and assignments where the pupils followed the pedagogical method “listen – think– pair – share.”Moreover, the pace during the simulation lessons was slow and the content taught in the lessons
was on a basic level. The feedback and reflections suggest that the students agreed with thisand realized that a lesson must have a different pace and engage with deeper content
knowledge.
Content Knowledge
In this knowledge form, the subject’s width and depth are important. The student teachers usedappropriate language according to the age of the pupils. They used appropriate concepts, butsometimes too academically, and when the pupils did not respond as the teachers wanted themto, they tried to bring the pupils back to the topic (conspiracy theories and source criticism) todevelop their learning. However, when the topic itself was too difficult for the student teachers
to handle or build a continuation of the lesson on, they more often postponed the difficulties
by saying “that’s an interesting thought you have, but let us go back to what I have planned
today” or “let us discuss that next lesson so I can gather more information, because I am notfully aware of what you are talking about now”. The reasons for this avoidance were mentioned

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as: “When you don’t have sufficient knowledge about a subject, it is easier to dismiss a
student’s thought instead of following it up.” (Students D and E).
Clearly, conspiracy theories have not been taught enough in the education program and its
theory courses. However, the student teachers could have been better prepared by studying thecontent for the simulation lesson more properly. Additionally, the student teachers haddifficulties dealing with pupils’ misunderstandings. The student teachers were not firm enoughin correcting pupils when the answer was wrong or when they expressed opinions that could
be described as “fake news.” Once again, this goes back to a lack of deep knowledge about the
subject itself which emerged in the interviews and in the group notes.
Discussion
The simulation teaching about conspiracy theories created an authentic training situation in asafe environment, where the students felt that they could try and learn to act as competentteachers without harming pupils in a school. In this case, our results from two cohorts of student
teachers in a Swedish teacher education program are in line with earlier findings (cf. Bradley& Kendall, 2014; Piro & O’Callaghan, 2018). We are extending the previous research on
simulation training by incorporating the content of conspiracy theories which has never – asfar as we know – been dealt with before (cf. Gundel, et al., 2019; Samuelsson, et al., 2022).
The student teachers that were part of the simulation considered the activity in the virtualenvironment to be both a practical and theoretical exercise (Grossman, et al., 2009; Ade-Ojo,
et al., 2021), combining analytical and action-oriented aspects of teaching (Jank & Meyer,2004). Another useful feature was conversations with university teachers and reflections withpeers, which provided opportunities to practice teaching about a content that is mostly taught
during school practice in the teacher education programs. The additional practice wasappreciated by the student teachers, but in this setting, they were not evaluated, and were rather
supported by university teachers who acted as instructors. The student teachers had the chance
to receive feedback and feed-forward from the instructors and their student peers, in what felt
like a safe space. It was also useful to observe other students teaching the same content to thesame pupils. Another learning experience was seeing how others handled difficulties in theclassroom and used the AAR technique (Dieker, et al., 2013; Scoresby & Shelton, 2014). One
could perhaps have considered other forms of practice, for example, role play with student
peers, as another way to train the student teachers. However, simulation was chosen as researchcomparing role play and simulation training found significant effects in teacher self-efficacy(TEB) when using simulation training (Samuelsson, et al., 2021).
There were many overall learning experiences, pertinent beyond a Swedish educational
context. In particular, the student teachers highlighted handling conflicts in the classroom,
trying to be flexible as a teacher, allocating speaking space, involving the pupils in the teachingprocess, and becoming more comfortable and secure as a teacher, which could be understoodas general knowledge (Shulman, 1987) or classroom management (Lewis, 2008; Wubbels,
2011). Many student teachers were nervous but appreciated the fact that the content was
something they had planned themselves. Still, it was difficult for them to use the avatars’72

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knowledge in the teaching. Many of the student teachers felt that confirming the avatars’
thoughts while also trying to move forward with the lesson was a challenge. We found that thegreatest challenge for the student teachers was to respond to the avatars, extending the findings
from previous research Samuelsson, et al., 2021; Samuelsson, et al., 2022). The avatars
challenged the student teachers with misconceptions, using mobile phones, coming into
conflict with other avatars, and not raising their hand when answering a question or when theywanted to talk. In the discussion with the student teachers, they considered this to be an extreme
situation; however, as university educators, we do not agree. These are situations that a teacher
must be prepared for. In any case, the student teachers realized the importance of seeing theirown weaknesses. The student teachers wanted to involve the pupils more in the teaching byrelating to the pupils’ own lives, but that did not happen. They seemed to lack the tools forcreating a high participation format (cf. Kounin, 1970; Alexander, 2008). The reasons given
were fear of losing control in the classroom, which stems from a lack of variation in teaching
methods, and a lack of deep knowledge in the subject (conspiracy theories). To be more precise,the student teachers seemed to lack content knowledge and pedagogical content knowledge(Shulman, 1987). Another reason was that they found the content (controversial issues)difficult. The student teachers chose to have a friendlier classroom climate rather than risk
engaging the avatars in in-depth discussions that could be difficult to handle (cf. Kaka, et al.,2021). Experiencing how difficult it could be to teach about conspiracy theories was an “Aha!”
moment for them. They seemed a little surprised about the need to be as secure in content
knowledge as in pedagogical content knowledge. However, the most important thing was thatthey observed how other student teachers handled different situations in the simulation and had
the opportunity to reason about this afterwards. Although the student teachers were notcompletely successful during their limited time in the simulation at getting the avatars tounderstand different conspiracy theories, source criticism was discussed in-depth with themafter the simulation exercise, and the student teachers deepened their subject knowledge byobserving their peers and following up on conversations.
A typical lesson structure was that the student teachers described their plan, informed theavatars about classroom rules, gave a lecture, and then held question and answer sessions
and/or discussions with the whole class. This was not a sufficiently creative or challenging
learning environment for the avatars. The student teachers had good intentions but could not
create a classroom climate that allowed all the avatars to participate. In addition, even though
there were only five avatars in the simulation practice, not all of them were heard during thelesson. It was notable that although there were only two avatar boys, they were heard morethan the three avatar girls. The teachers turned more often to the boys in the classroom andasked for their opinions than to the girls. In that sense, the student teachers reproduced therecent findings from the Swedish Schools Inspectorate (2022) as the student teachers
reproduced gender differences that inhibit the avatar girls and had problems to create aclassroom climate that supported interaction about controversial issues. This was something
the student teachers realized afterwards when the educators asked them about it. Moreover,there was a significant challenge dealing with the shy avatar, Jasmine. Instead of waiting foran answer from her, the student teachers turned quickly to more talkative pupils or those who
agreed with the teachers, which felt secure for the student teachers. However, in the simulation,73

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the students got ideas about solutions by observing peer students’ teaching and from thereflective discussions afterwards.
Although our results show promise, there are also limitations to our study. One limitation is theabsence of prior research with a similar focus as ours. A more extensive literature review onsimilar research about simulation training on controversial issues would have provided a solid
foundation for understanding the research problem and discussions about contextual
similarities and differences. Another limitation is the sample size. Even with twice as many
student teacher cohorts, the number of individuals and pairs would still have been small. A
larger sample size would have provided more results.
Conclusion
Simulation teaching has its place in terms of offering more teaching practice and creating
experiences of acting as a teacher in a realistic situation (cf. Samuelsson, et al., 2021;
Samuelsson, et al., 2022). The Swedish student teachers that participated in our study have a
total of 20 weeks of school practice spread over the five-year teaching program, and simulation
practice seems to be useful addition in between. Simulation teaching can integrate different
forms of knowledge: (a) general knowledge, (b) pedagogical content knowledge, and (c)content knowledge. In a safe space, student teachers can develop experiences of authentic
practice that combines theoretical and practical aspects of knowledge for the student’s
development to become a good teacher. In the simulation, the student teachers gained insights
into their approaches and an opportunity to test important teaching skills such as flexibility,
responsiveness, leadership, using a selection of methods, and demonstrating subject
knowledge. In this case, this subject knowledge related to conspiracy theories and developing
learning about critical thinking. However, this study reveals that more attention must be paid
to teacher education in order to develop student teachers’ confidence and their experience ofusing a variety of methods to motivate and include all pupils, especially when teaching difficult
to handle subject matter. To be a skilled teacher, the student teachers need experience andpractice offered by simulation teaching. This is a cost-effective and climate-friendly solution
for improving student teachers’ skills. As the spread of conspiracy theories is a growing
phenomenon world-wide, this experience conducting simulation training with TeachLivE toteach controversial issues can be applied to teacher training contexts elsewhere.

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References
Ade-Ojo, G. O., Markowski, M., Essex, R., Stiell, M., & Jameson, J. (2021). A systematic
scoping review and textual narrative synthesis of physical and mixed-reality
simulation in pre-service teacher training. Journal of Computer Assisted Learning, 38,
861–874. https://doi.org/10.1111/jcal.12653
Alexander, R. (2008). Towards dialogic teaching: Rethinking classroom talk. Cambridge:
Dialogos.
Arvola, M., Samuelsson, M., Nordvall, M., & Ragnemalm, E. (2018). Simulated
Provocations: A Hypermedia Radio Theatre for Reflection on Classroom
Management. Simulation & Gaming, 49(2), 98–114
https://doi.org/10.1177/1046878118765594
Avramov, K., Gatov, V., & Yablokov, I. (2020). Conspiracy theories and fake news. In M.
Butter & P. Knight (Eds.), Routledge Handbook of Conspiracy Theories (pp. 512–
524). London: Routledge. https://doi.org/10.4324/9780429452734-4_10
Bautista, N. U., & Boone, W. J. (2015). Exploring the impact of TeachMETM Lab Virtual
Classroom Teaching Simulation on early childhood education majors’ self-efficacy
beliefs. Journal of Science Teacher Education, 26(3), 237–262.
https://doi.org/10.1007/s10972-014-9418-8
Beazel, G. (2022). Of conspiracy and controversy. A pedagogy of conspiracy theories.
Educational Theory, 72(3), 257–280. https://doi.org/10.1111/edth.12529
Bradley, E. G., & Kendall, B. (2014). A review of computer simulations in teacher education.
Journal of Educational Technology Systems, 43(1), 3–12.
https://doi.org/10.2190/ET.43.1.b
Butter, M., & Knight, P. (2020). General introduction. In M. Butter & P. Knight (Eds.),
Routledge Handbook of Conspiracy Theories (pp. 1–8). London: Routledge.
https://doi.org/10.4324/9780429452734-0
Bondie, R., Mancenido, Z., & Dede, C. (2021). Interaction principles for digital puppeteering
to promote teacher learning. Journal of Research on Technology in Education, 53(1),107- 123. https://doi.org/10.1080/15391523.2020.1823284
Chini, J., Straub, C., & Thomas, K. (2016). Learning from avatars: Learning assistants
practice physics pedagogy in a classroom simulator. Physical Review Physics
Education Research, 12(1). https://doi.org/10.1103/PhysRevPhysEducRes.12.010117
Council of Europe. (2016). Living with Controversy. Teaching controversial issues througheducation for democratic citizenship and human rights [Training pack for teachers].
Retrieved April, 27 2023 from https://www.coe.int/en/web/learning-resources//training-pack-teaching-controversial-issues
Dawson, M. R., & Lignugaris/Kraft, B. (2017). Meaningful practice: Generalizing foundationteaching skills from TLE TeachLivETM to the classroom. Teacher Education andSpecial Education, 40(1), 26–50. https://doi.org/10.1177/0888406416664184
Dieker, L. A., Rodriguez, J. A., Lignugaris/Kraft, B., Hynes, M. C., Hughes, C. E. (2013).
The potential of Simulated Environments in Teacher Education: Current and Future
Possibilities. Teacher Education and Special Education, 37(1), 21–33.
https://doi.org/10.1177/0888406413512683

75

IAFOR Journal of Education: Technology in Education

Volume 11 – Issue 2 – 2023

Dieker L. A., Lignugaris/Kraft B., Hynes M., & Hughes C. E. (2016). Mixed reality
environments in teacher education: Development and future applications. In B.
Collins & B. Ludlow (Eds.), Online in real time: Using WEB 2.0 for distanceeducation in rural special education (pp. 122–131). American Council for Rural
Special Educators.
Douglas, K. M., & Sutton, R. M. (2023). What are conspiracy theories? A definitional
approach to their correlates, consequences, and communication. Annu. Rev. Psychol.,74, 271–298. https://doi.org/10.1146/annurev-psych-032420- 031329
Ersozlu, Z., Ledger, S., Ersozlu, A., Mayne, F., & Wildy, H. (2021). Mixed-reality Learning
Environments in Teacher Education: An analysis of TeachLive Research. SageOPEN,
July-September, 1–10. https://doi:10.1177/21582440211032155
Flensner, K. K. (2020). Teaching Controversial Issues in Diverse Religious Education
Classrooms. Religions 2020, 11, 465. https://doi.org/10.3390/rel11090465
Giry, J., & Tika, P. (2020). Conspiracy theories in political science and political theory. In M.
Butter & P. Knight (Eds.), Routledge Handbook of Conspiracy Theories (pp. 108–
120). London: Routledge. https://doi.org/10.4324/9780429452734-1_8
Granström, K. (2007). Leadership in the classrooms. In K. Granström (Ed.), Research onteachers work in classrooms. (pp. 13–32). Stockholm: Myndigheten för
Skolutveckling.
Grossman, P., Hammerness, K. & McDonald, M. (2009). Redefining teaching, re‐imagining
teacher education, Teachers and Teaching, 15(2), 273–289.
https://doi.org/10.1080/13540600902875340
Gundel, E., Piro, J. S., Straub, C., & Smith, K. (2019) Self-efficacy in mixed reality
simulations: Implications for preservice teacher education, The Teacher Educator,
54(3), 244–269. https://doi.org/10.1080/08878730.2019.1591560
Hamre, B. K., Pianta, R. C., Downer, J. T., DeCoster, J., Mashburn, A. J., Jones, S. M.,
Brown, J. B., Cappella, E., Atkins, M., Rivers, S. E., Brackett, M. A., & Hamagami,
A. (2013). Teaching through Interactions: Testing a Developmental Framework of
Teacher Effectiveness in over 4,000 Classrooms. The Elementary School Journal,
113(4), 461–487. https://doi.org/10.1086/669616
Hayes, A. T., Straub, C. L., Dieker, L. A., Hughes, C. E., & Hynes, M. C. (2013). Ludic
Learning: Exploration of TLE TeachLivE TM and Effective Teacher Training.
International Journal of Gaming and Computer-Mediated Simulations, 5, 20-33.
https://doi.org/10.4018/jgcms.2013040102
Hudson, M. E., Voytecki, K. S., Owens, T. L., & Zhang, G. (2019). Preservice teacher
experiences implementing classroom management practices through mixed-reality
simulations. Rural Special Education Quarterly, 38(2), 79–94.
https://doi.org/10.1177/8756870519841421
Kaka, S.J., Littenberg-Tobias, J., Kessner, T., Francis, A.T., Kennett, K., Marvez, G. &Reich, J. (2021). Digital Simulations as Approximations of Practice: Preparing
Preservice Teachers to Facilitate Whole-Class Discussions of Controversial Issues.
Journal of Technology and Teacher Education, 29(1), 67–90.
https://doi.org/10.35542/osf.io/95gyd
Kounin, J. S. (1970). Discipline and group management in classrooms. Holt, Rinehart andWinston.

76

IAFOR Journal of Education: Technology in Education

Volume 11 – Issue 2 – 2023

Ledger, S., Ersozlu, Z., & Fischetti, J. (2019). Preservice Teachers’ Confidence and Preferred
Teaching Strategies using TeachLivETM Virtual Learning Environment: A Two-Step
Cluster Analysis. EURASIA Journal of Mathematics, Science and Technology
Education, 15(3), 1–17. https://doi.org/10.29333/ejmste/102621
Lewis, R. (2008). The developmental management approach to classroom behaviour:
Responding to individual needs. ACER Press.
Long, W. A. (1989). Personality and learning: 1988 John Wilson Memorial address. Focus on
Learning Problems in Mathematics, 11, 1–16.
McDonald, M., Kazemi, E. & Schneider Kavanagh, S. (2013). Core Practices and Pedagogies
of Teacher Education: A Call for a Common Language and Collective Activity.
Journal of Teacher Education, 64(5) 378–386.
https://doi.org/10.1177/0022487113493807
Piro, J., & O’Callaghan, C. (2018). Journeying towards the profession: Exploring liminal
learning within mixed reality simulations. Action in Teacher Education, 41(1), 79–95.
https://doi.org/10.1080/01626620.2018.1534221
Samuelsson, M. (2018). Pathfinders or Explorers: Student teachers’ approaches to challenges
of classroom management in a simulation. Education in the North, 25(1-2), 45–63.
https://doi.org/10.26203/rmy1-d092
Samuelsson, M., Samuelsson, J., & Thorsten A. (2021). Simulation training is as effective asteaching pupils: Development of efficacy beliefs among pre-service teachers. Journalof Technology and Teacher Education 29(2), 225–251.
Samuelsson, M., Samuelsson, J., & Thorsten, A. (2022). Simulation training – A boost for
pre-service teachers’ efficacy beliefs. Computers and Education Open, 3,
https://doi.org/10.1016/j.caeo.2022.100074
Sandahl, J. (2020). Opening up the echo chamber: Perspective taking in social scienceeducation. Acta Didactica Norden, 14(4), 1–24. https://doi.org/10.5617/adno.8350
Schreier, M. (2014). Qualitative content analysis. In U. Flick (Ed.) The SAGE Handbook of
Qualitative Data Analysis (pp. 170–183). London: Sage.
https://doi.org/10.4135/9781446282243.n12
Scoresby, J., & Shelton, B. E. (2014). Reflective Redo From the Point of Error. Implications
for After Action Review. Simulation & Gaming, 45(4-5), 666–696.
https://doi.org/10.1177/1046878114549426
Shulman, L. S. (1986). To those who understand. Knowledge growth in Teaching.
Educational Researcher, 15(2), 4–14. https://doi.org/10.3102/0013189X015002004
Shulman, L. S. (1987). Knowledge and Teaching. Foundations of the New Reform. Harvard
Educational Review, 57(1), 1–23.
https://doi.org/10.17763/haer.57.1.j463w79r56455411
Smith, K., & Klumper, D. (2018). Virtually in the Classroom. ASCD, 76, 1–14.
Swedish Schools Inspectorate. (2022). Skolans hantering av kontroversiella frågor iundervisningen. Inriktning samhällskunskap och biologi i årskurs 8 och 9. Retrieved
February 16, 2023 from https://www.skolinspektionen.se/beslut-rapporterstatistik/publikationer/kvalitetsgranskning/2022/the-schools-handling-of-controversial-issues-in-teaching/77

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Westbury, I., Hansén S-E., Kansanen P, Björkvist, O. Teacher education for researchbased
practice in expanded roles: Finland’s experience. Scandinavian Journal of
Educational Research, 49(5), 475–85. https://doi.org/10.1080/00313830500267937
Wubbels, T. (2011). An international perspective on classroom management: what shouldpro- spective teachers learn? Teaching Education, 22(2), 113–131.
https://doi.org/10.1080/10476210.2011.567838
Önnerfors, A. (2021). Konspirationsteorier och covid-19: mekanismerna bakom en
snabbväxande samhällsutmaning. Stockholm: Myndigheten för samhällsskydd och
beredskap [MSB].
Corresponding author: Per-Olof Hansson
E-mail: per-olof.hansson@liu.se

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Moroccan Teachers’ Perceptions and Concerns about ICT Integration
El Mustapha Baytar
Multidisciplinary Laboratory in Educational Sciences and Training Engineering, Higher
Normal School, Hassan II University of Casablanca,50069, Casablanca, Morocco
Abdelaziz Ettourouri
Multidisciplinary Laboratory in Educational Sciences and Training Engineering, Higher
Normal School, Hassan II University of Casablanca,50069, Casablanca, Morocco
Nadia Saqri
Multidisciplinary Laboratory in Educational Sciences and Training Engineering, Higher
Normal School, Hassan II University of Casablanca,50069, Casablanca, Morocco
Lynda Ouchaouka
Multidisciplinary Laboratory in Educational Sciences and Training Engineering, Higher
Normal School, Hassan II University of Casablanca,50069, Casablanca, Morocco79

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Abstract
Integrating technology into teaching practices often changes teachers’ work patterns. Thus,
several studies have insisted on supporting such change by understanding teachers’ concerns.
The present study adopts the Concerns-Based Adoption Model (CBAM) as a conceptual
framework to examine Moroccan teachers’ concerns about integrating information andcommunications technology (ICT). To this end, we relied on a self-reporting instrument for asample of teachers (n = 382) from two Regional Academies of Education. Our findings suggestthat the overall profile of teachers’ stages of concern (SoC) is that of “reluctant” non-users.
This profile showed high percentiles for the first three SoC, a low consequence stage percentile,
medium percentiles for the management and collaboration stages, and a tailing up at the
refocusing stage. Furthermore, the results highlighted a positive relationship between teachers’
concerns about integrating ICT and previous continuous training on the one hand and the
pandemic’s impact on their attitudes toward self-training on the other. In addition, significant
differences in teachers’ concerns regarding teaching experience and age were found. Our study
provides change leaders insight into teachers’ concerns about integrating technology which
will help the field design appropriate interventions to reduce their limiting concerns.
Keywords: CBAM, COVID-19, ICT, Morocco, stages of concern, teachers

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Morocco has made significant efforts to improve the quality of national schools’ outcomes.
This reform dynamic extends from the National Charter of Education announcement (2000)
until the promulgation of framework law 51.17 on the education system (2019). Moreover,integrating Information and Communication Technologies (ICT) into teaching practices andrelated training were common components of all reform stations. However, the opportunities
technology can bring to improve learning quality must be better tapped (The Ministry of
National Education, 2022). Such a conclusion raises the question of teachers’ training
efficiency regarding ICT integration: Does the formal training effectively meet their realneeds? Responding to teachers’ real training needs to keep up with the change entailed byreform is crucial in ensuring their proactivity (Brinkerhoff, 2006; Magallanes et al., 2022).
Nevertheless, professional training is historically designed based on what policymakers assume
educators need rather than what they effectively need (Vaughan, 2002). Furthermore, the
COVID-19 crisis revealed the important role of technology in teaching and learning activities(Hansson, 2021; Tzankova et al., 2023), and it also highlighted the need for rethinking teachers’
professional training in ICT (Stracke et al., 2022).
Based on the Concerns-Based Adoption Model (CBAM) (Hall et al., 1973), the present study
examined teachers’ concerns about integrating ICT. The study took place in the context of thereform of the Moroccan educational system. Early in the previous year, the Ministry of
Education launched national consultations in order to reinvigorate the reform process
embodied in law 51.17. Thus, a roadmap for the next four years (2022-2026) was developed
based on these consultations (The Ministry of National Education, 2022). This roadmap set 12
commitments needed to enhance the national schools’ outcomes. The integration of ICT into
teaching practices and related professional development were included in the 2nd , the 6th, andthe 9th commitments. Thus, the aim of this study was to understand teachers’ needs in termsof technology integration to help in designing appropriate professional development.
Furthermore, we think that our study’s relevance stems from three other particularities. First,
our understanding of ICT continuous training includes not only formal professional
development but also the personal effort of self-training that takes place in informal settings.
Second, our study integrates the COVID-19 crisis as a new independent variable to examine
its potential influence on teachers’ concerns about integrating technology. Finally, we followed
a rigorous data analysis procedure based on the guidelines proposed by George et al. (2013).
Thus, according to what we mentioned earlier, and by considering the integration of ICT as asource of change, the present study aimed to meet two research objectives: (1) Explore
Moroccan teachers’ concerns about integrating ICT into their teaching practices and (2)Examine the sensitivity of teachers’ concerns toward continuous training in ICT and theteaching experience amid the COVID-19 crisis.

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Literature Review
The Concerns-Based Adoption Model
Many studies stressed the significant influence of teachers’ feelings and perceptions on theireffective integration of technology (Baytar et al., 2023; Fearnley & Amora, 2020; Huang et al.,2023; Njiku et al., 2019). Fuller (1969) was the first to call these feelings and perceptions“concerns.” In her theory of concerns, based on a series of studies on student teachers, Fuller
believed that teacher education programs should meet their concerns which move theoreticallythrough a four-level continuum: unrelated-concerns, self-concerns, task-concerns, and impactconcerns. Four years later, Hall et al. (1973), relying on Fuller’s work, proposed the conceptual
framework known as the Concerns-Based Adoption Model (CBAM). This model was
supposed to help change leaders identify the concerns of an innovation’s implementers in orderto facilitate the change process by designing appropriate interventions (Ohlemann et al., 2023).
Concerns are defined as “the composite representation of the feelings, preoccupations,thoughts, and considerations given to a particular issue or task” (Hall & Hord, 2014, p. 85).
Hence, Hall et al. (1977) built a 35-item questionnaire where they displayed seven stages ofconcern (SoC) through the four levels suggested by Fuller (1969) (Table 1).
Table 1
Stages of Concern Descriptions
Levels ofconcern

IMPACT

TASK

Stages ofconcern(6) Refocusing

The implementer has new and innovative ideas on
how to improve/change the innovation’s actions.

(5) Collaboration

The implementer is more concerned about co-workand collaborating with others.

(4) Consequence

The implementer is more concerned about how theinnovation might affect his/her learners.

(3) Management

All the implementer’s interest is focused on preparing
materials.

(2) Personal

The implementer is more concerned about how theinnovation might impact him/her.

SELF(1) Informational
UNRELATED

Description(0) Unconcerned

The implementer is curious about gathering
information about the innovation.
The implementer is not concerned about theinnovation; he/she is more interested in otheractivities or tasks.

According to Hall and Hord (2014), implementing innovation is a source of change, which
entails implementers’ resistance. Hall and Hord (2014) argued change leaders should
understand implementers’ concerns in order to reduce their résistance. For them, the apparent

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resistance would be just a symptom of “grief” resulting from leaving his/her comfort zone, thatis, moving from doing comfortable tasks to implementing new instructions. Thus, they insistedon considering the change as a “process” rather than an “event” to give time for implementers
to learn and improve their practices (Hall & Hord, 2014, p. 11).
Related Studies
In conducting our literature review, we were able to distinguish three types of studies: studies
that addressed teachers’ concerns about ICT integration in general, (e.g., Agormedah et al.,2019; Dele-Ajayi et al., 2021; Dubey, 2016; Sarfo et al., 2017), studies that examined concerns
regarding a particular technology after a period of implementation, (e.g., Alnujaidi, 2021;
Amankwah et al., 2022; Gu et al., 2023; He & Yusop, 2020), and studies that addressed
concerns between two points of time (t1 and t2) during a defined period (weeks or months) ofa professional development program (e.g., Georgiou & Ioannou, 2019; Kayaduman & Demirel,
2019; Ziegenfuss et al., 2019). Thus, in Dele-Ajayi et al.’s study (2021), the concerns of 340
Nigerian teachers about ICT integration were higher at stages 0, 3, and 1, and lower at stages
5 and 4. Additionally, significant differences were found at the SoC in terms of age, level
taught, and teaching experience. Moreover, Dubey’s (2016) study suggested higher selfconcerns (stages 0, 1 and 2) of 190 in-service teachers about ICT integration with no interesting
differences regarding participants’ age and teaching experience. In addition, Agormedah et al.
(2019) found that the concerns of 66 Business Studies teachers were intense at stages 4 and 1
and low at stage 0, with no significant differences according to teachers’ characteristics(teaching experience, age, and gender). Furthermore, as an example of the second category ofstudies, in Alnujaidi’s study (2021), the concerns about “Mobile Assisted Language Learning”
of 130 Saudi in-service teachers were intense at stages 1, 2, and 3. The later study highlighted
the role of attending previous professional development related to this technology in decreasing
self-concerns and increasing impact-concerns (stages 4, 5, and 6). Finally, as an example of thethird category of studies, Georgiou and Ioannou (2019) found that the concerns of 31 in-service
teachers at the end of a professional development program on “Technology-Enhanced
Embodied Learning” were higher at stages 1, 5, and 6. Finally, it is noteworthy that we did not
find studies that adopted the CBAM conceptual framework to examine teachers’ concerns
about integrating ICT within the Moroccan context.
The present study contributes to the research dynamic described above. It relies on the CBAM
model as a reliable and valid theoretical framework, used on a large scale, to explore Moroccan
teachers’ concerns regarding integrating technology. Moreover, our research’s particularities,
mentioned earlier, will help generate original results that could enrich scientific discussion ontechnology integration in education as an innovation that entails a change in teaching practices.

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MethodResearch Design
Our study adopted a quantitative research design to explore Moroccan teachers’ concerns
regarding ICT integration. After a validation stage with a limited population (17 participants),
the SoC questionnaire was used to collect data from elementary and secondary school teachers(n = 382) belonging to two Regional Academies of Education. All data were analyzed using
descriptive and inferential statistics.
Participants
Our study population comprised 382 teachers from the three national education system levels:
elementary, middle, and high school. Individuals in this sample represented teachers working
in two Regional Academies of Education: The Academy of Marrakech-Safi and the Academyof Casablanca-Settat. Participants filled out a questionnaire (described below) with theirconsent after obtaining our commitment to respect the anonymity of answers and to use their
responses for purely academic purposes. Moreover, the research objectives were clear to the
respondents. Table 2 displays more details about the participants’ demographics.
Table 2
Demographics of Teacher Participants
Variable
GenderAge

Teaching
experience

WorkplaceCategories
FemaleMale
20-30 years
31-40 years
41-50 years
51 and over1 - 5 years
6 - 15 years
16 - 25 years
26 and over
Rural
Urban

n
170
212
102
120
114
46
111
121
119
31
140
242

%
Variable
Categories
44.5
Doctorate
55.5
Master
26.7 Diploma Bachelor
31.4
Baccalaureate + 2
29.8
Baccalaureate
12.0
Elementary school
Teaching
29.1
Middle school
level
31.7
High school
31.2
Elementary
8.1 School Scientific36.6 subject Literary
63.4
Activity

n
13
86
238
26
19
135
151
96
129
65
168
20

%
3.4
22.5
62.3
6.8
5.0
35.3
39.5
25.1
33.8
17.0
44.0
5.2

Instrument
As detailed earlier, our study adopts the CBAM conceptual model to examine teachers’
concerns about integrating ICT. Hence, our survey’s instrument was the 35-item questionnaireelaborated by Hall et al. (1977) and published by George et al. (2013, pp. 27–28). Thus, toadapt the questionnaire statements to our research objectives, we substituted “innovation” with84

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“ICT.” The 35 items of the questionnaire are divided into seven stages of five items each. The
answer to the items is made according to a seven-option Likert-scale (0 = Irrelevant; 1 and 2 =Not true of me now; 3,4, and 5 = Somewhat true of me now; and 6 and 7 =Very true of menow). Besides the 35 items, we added questions about participants’ demographics and two
Yes/No questions designed to address the second research objective. The first such question
asked, “Have you received any training in using ICT, whatever form it takes (in a formal orinformal setting)?”. The second question asked, “Given that the COVID-19 crisis did highlightthe importance of using ICT, did you make a self-training effort to develop your competence
in using ICT after the outbreak of this crisis?”
The questionnaire was administered in Arabic. Thus, a translation validation was needed. As afirst step, the researchers collaborated with an English teacher, who had experience using ICT,
to translate the original questionnaire into Arabic, taking into account the participants’ culture.
Subsequently, two other English teachers with the same profile scored the previoustranslation’s accuracy based on a 10-point grade for each item and suggested rectifications, ifany. Consequently, the questionnaire items scored between 8 and 10. The researchers and thefirst English teacher discussed the items, suggested rectifications, and made necessary changes.
Moreover, the researchers pretested the revised questionnaire among 17 participants to checkits clarity and appropriateness.
As for the questionnaire’s internal reliability, the overall Cronbach’s α coefficient was .959,
and Cronbach’s α coefficient of the seven SoC ranged from .588 to .896 (Table 3). An
outstanding remark noted during our literature review is the low Cronbach’s α coefficient ofStage 0 found in most studies, (e.g., Hall et al., 1977; Ashrafzadeh & Sayadian, 2015;
Alnujaidi, 2021). Generally, the high Cronbach’s alpha coefficients found for our instrument
were deemed satisfying.
Data Collection and Analysis
The data collection was performed through two modes: paper-and-pencil and online format.
Within the final sample (n =382), 73 (19.1%) paper and 309 (80.9%) online questionnaires
were deemed valid. As for the data analysis, SPSS.22 software was used to make the analysis
necessary to meet the two research objectives. Furthermore, as mentioned earlier, a rigorous
data analysis and interpretation procedure, recommended by George et al. (2013), wasfollowed. To illustrate the overall profile of concerns, we first averaged the seven stages’ raw
scores and then converted the averages to percentiles. To this end, we built a program in Excel
to convert raw scores to percentiles. As for the inferential statistics, we used raw scores insteadof percentiles.

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Table 3
Cronbach’s Alpha Coefficient by Stage
Stage
Stage 0Stage 1
Stage 2*C.α.C
.588
.812

Stage
Stage 3
Stage 4*C.α.C
.754
.878

Stage
Stage 5
Stage 6*C.α.C
.896
.855

.891

Results
Teachers’ Overall Stages of Concern Profile
The teachers’ overall SoC profile (Figure 1) showed, at first sight, that the first three stages(unconcerned, informational, and personal), were the highest stages. Among these three stages,
the personal stage had the highest percentile (76th), followed by the informational stage (72nd),and finally, the unconcerned stage (69th). In addition, the overall profile suggested that thelowest stage of concern was stage 4 (consequence), with a percentile score of 33rd. Moreover,medium intensities were recorded for stage 3 and stage 4 (56th and 52nd consecutively).
Finally, a remarkable feature of this profile was the “tailing-up” of the refocusing stage (the65th).
Figure 1
Teachers’ Overall SoC Profile

Furthermore, the overall profile reflected only the average percentiles scores of the SoC
questionnaire. Therefore, we could not infer from this profile the teachers’ distribution
according to their percentiles’ frequency (for instance, through quartiles) as well as theirdistribution according to their highest stage of concern. Thus, for improved visibility of ourresults, Table 4 and the Box Plot (Figure 2) provide more details on these two later types of
distribution.

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Table 4
Distribution of Teachers’ Highest Stage of Concern

n

0
106

1
119

2
91

3
24

4
4

5
22

6
69

Total
438*

%

12.9

27.2

20.8

5.5

0.9

5

15.8

100

Note. * The highest percentile is replicated over two or more stages for some participants.

Figure 2
Distribution of Teachers by Percentile Frequency (Quartiles)

Relationship between Teachers’ Stages of Concern and the Main Independent Variables
By using Student’s t-test, our results highlighted statistically significant differences (p<0.05)
in the teachers’ concerns regarding ICT integration according to two variables, namely ICT
training and the impact of the COVID-19 crisis on their attitude toward self-training in the field(Table 5). Hence, teachers who previously received training in ICT, regardless of its form orsource, were more concerned about consequence, collaboration, and refocusing issues (Figure3). Moreover, the results suggested that teachers who replied “Yes” to a question about thepositive impact of the COVID-19 experience on their self-training efforts to improve theircapability to integrate ICT meaningfully in their classrooms had more concerns in five of theseven stages of the questionnaire: Informational, Personal, Consequence, Collaboration, andRefocusing stages (Figure 4).
Furthermore, our statistical analysis using an one-way ANOVA test revealed a significant
relationship (p<0.05) between the SoC and two other variables, namely participants’ teaching87

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experience and their age (Table 6). Regarding teaching experience, there were statisticallysignificant differences in the unconcerned, informational, personal, management, andcollaboration stages. As for age, significant differences were recorded in the unconcerned,
informational, personal, management, and consequence stages.
Table 5Comparison of Teachers’ SoC Means by ICT Training and COVID-19 Impact
StagesStage 0Stage 1
Stage 2
Stage 3
Stage 4
Stage 5
Stage 6

ICT
training
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No

n

M

214
168
214
168
214
168
214
168
214
168
214
168
214
168

11.88
12.73
20.26
20.58
21.67
21.20
15.12
15.93
22.39
19.49
22.68
18.93
21.72
17.85

Sig.
COVID-19(t-test)impact
Yes
.241
No
Yes
.731
No
Yes
.653
No
Yes
.369
No
Yes
.005*
No
Yes
.001*
No
Yes
.000*
No

n

M

312
70
312
70
312
70
312
70
312
70
312
70
312
70

12.14
12.76
20.91
18.14
22.12
18.56
15.59
14.97
22.05
16.96
22.21
15.77
21.09
15.24

Figure 3
Teachers’ SoC by Previous ICT Training

Figure 4
Teachers’ SoC by the Impact of the COVID-19 on their Self-training Efforts

88

Sig.
(t-test)
.505
.021*
.009*
.615
.000*
.000*
.000*

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Table 6Comparison of Teachers’ SoC Means by Teaching Experience and Age
Teaching Experience
StagesF
Sig.
Stage 05.328
.001*Stage 1
3.934
.009*
Stage 2
3.789
.011*
Stage 3
4.110
.007*
Stage 42.424
.065
Stage 52.884
.036*
Stage 6
1.937
.123StagesStage 0Stage 1
Stage 2
Stage 3
Stage 4
Stage 5
Stage 6

Age
F
4.478
3.069
3.169
3.654
2.866
2.085
1.887

Sig.
.004*
.028*
.024*
.013*
.037*
.102
.131

Moreover, for an in-depth analysis of the results arising from the ANOVA test, a Post Hoc test(the Least Significant Difference test) was carried out. Thus, without reporting detailed statistics,
the general conclusion highlighted for the teaching experience variable was that the moreteaching experience, the less concerned the teachers were, especially for the two extremegroups (1-5 years and over 26). Although the ANOVA test did not reveal significant
differences for the consequence and refocusing stages, the Post Hoc test did between theextreme age groups. This conclusion is well illustrated in Figure 5. In addition, the Post Hoc
test results suggested a similar trend regarding the age variable. That is, the younger theteachers, the higher the intensity of concerns (Figure 6). Differences were significant for
extreme age groups (20-30 and “51 and over”) over all SoC, including the collaboration andthe refocusing stages, where the ANOVA test p-value was not significant.
It is noteworthy that no significant differences were suggested for the other independent
variables, namely gender, teaching level, workplace, school subject, and diploma.

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Figure 5
Teachers’ SoC by Teaching Experience

Figure 6
Teachers’ SoC by Age

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Discussion
In the present study, we attempted to explore Moroccan teachers’ concerns about ICT
integration in their teaching practices (Objective 1). In addition, we examined whether theseconcerns were sensitive to continuous training in the field and the experience of teaching amid
the COVID-19 crisis (Objective 2).
To interpret the teachers’ overall profile of concerns illustrated in our findings (Figure 1), we
refer to the guidelines recommended by George et al. (2013). This profile showed highpercentile scores for the three first SoC, that is, stage 2 (76th), stage 1 (72nd), and stage 0(69th). First, the participants’ high personal percentile score suggests they were moreconcerned about self-issues like professional status, promotions, and personal privileges. In
other words, they were most concerned about how ICT might impact them. Second, the highinformational percentile score shows, in turn, that the participants were curious about thefeatures and requirements of ICT integration. Participants with high informational stage scores
were not bound to be knowledgeable, but they were rather curious people who wanted to know
more. The high stage 1 and stage 2 scores find their explanation in other studies, combining
them into one stage because of their strong correlation (Bailey & Palsha, 1992; Cheung et al.,
2001; Shotsberger & Crawford, 1999). Likewise, many other studies found high and close stage
1 and 2 scores (Alnujaidi, 2021; Ashrafzadeh & Sayadian, 2015; Dubey, 2016; Gu et al., 2023;
Masarweh, 2019; Yang-Hsueh & Syh-Jong, 2014). Moreover, the fact that the personal stage
score is higher than the informational stage score would indicate a potential resistance toward
ICT integration. Third, the participants’ high unconcerned stage scores indicated that the
integration of ICT was not a priority for the participants. They were not interested in using
ICT; they would be more engaged in other activities or tasks. In sum, high scores in the three
first stages generally characterized the non-user profile.
Furthermore, the lowest consequence stage score (33rd) indicated that the participants wereless concerned about the effect of their ICT use on their learners. It is an alarming finding for
the Ministry of Education, which had placed the learner at the center of the new reform’s
actions (The Ministry of National Education, 2022). Likewise, the consequence stage score
was the lowest in other studies (Alnujaidi, 2021; Ashrafzadeh & Sayadian, 2015; Dele-Ajayi
et al., 2021; Dubey, 2016; Georgiou & Ioannou, 2019). Nevertheless, in Sarfo et al.’s (2017)
study, consequence concerns about integration ICT had the second highest score. In addition,
our findings suggest medium scores in the management and collaboration stages. Thesefindings indicate a relative interest in managerial issues, like logistics and time, and incollaborating with others. Hence, From the previous analysis of the first six stages, we could
infer that the typical profile of our sample was that of a non-user. Additionally, the tailing upof the refocusing stage score highlighted valuable information about teachers’ typical profile;
despite having innovative ideas, teachers were reluctant to integrate ICT. Thus, this tailing up
emphasized the hypothesis, raised previously, of the participants’ resistance toward ICT
integration, and it “[…] should be heeded as an alarm” (George et al., 2013, p. 42). Being able
to use ICT is necessary, but it is not a condition for effectively integrating them (Sandholtz &Reilly, 2004). Dubey (2016) found an overall profile shape close to ours.

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Regarding continuous training in ICT, our findings suggest that teachers who have previously
received such training have significantly higher impact-concerns than teachers who have not.
Adams (2002) found positive correlations between training in ICT and impact-concerns. In
addition, most studies that have addressed professional development in a particular technology
found shifts from self-concerns to impact-concerns at the end of this professional development(Alnujaidi, 2021; Kayaduman & Demirel, 2019; Vaughan, 2002); the professional
development mitigates participants’ self-concerns and accentuates their impact-concerns. In
our study, the absence of significant differences in self-concerns could be explained by thewide range of technologies that the acronym ICT refers to as innovation. That is, participants
need more information about technologies and about how they can affect them. On the other
hand, lecturing about a particular technology within a professional development programprovides answers to participants’ questions about their self-concerns. Furthermore, participants
who think the COVID-19 experience raised their self-training efforts regarding ICT integration
have significantly high informational, personal, consequence, collaboration, and refocusing
concerns. That is, this experience aroused their curiosity about ICT and made their practices
more sophisticated. The COVID-19 experience has positively impacted teachers’ attitudes
about using ICT (Baytar et al., 2022). Furthermore, our findings suggested significant
differences in teachers’ SoC regarding teaching experience and age. Recent hires and young
participants were more concerned than experienced and older teachers. Adams (2002, p. 285)
inferred that “[…] young faculty and faculty with less teaching experience expressed higherorder concerns”. In contrast, more experienced and older teachers had deeper concerns overmost of the stages in other studies (Alnujaidi, 2021; Dele-Ajayi et al., 2021).
Conclusion
The ongoing advance of technology, as well as its significant contribution to ensuring
pedagogical continuity during the COVID-19 crisis, would explain the outstanding presenceof ICT integration in education in the current scientific research. The researchers haveaddressed the teachers’ ICT integration from many perspectives to examine how they use
technology to improve their teaching. In the same context, our study adopted a quantitative
approach to examine Moroccan teachers’ concerns about using technology.
Hence, our finding suggested that the teachers’ overall SoC profile is that of “reluctant” nonusers. This profile showed high percentiles for the first three SoC, a low consequence stage
percentile, medium percentiles for stages 3 and 5, and a tailing up at the refocusing stage.
Moreover, the findings highlighted a positive relationship between teachers’ concerns about
integrating ICT and continuous training in technology on the one hand and the pandemic
impact on their attitudes toward self-training on the other. In addition, our results showedsignificant differences in teachers’ concerns regarding teaching experience and age.
Furthermore, the results of this paper do not claim exhaustiveness. We are aware of the limitsof our approach related chiefly to our sample’s non-representativity of the entire schools of thecountry and the risk of getting subjective responses from using a self-perception instrument.
However, we think that our findings propose a theoretically framed diagnosis that could help

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policymakers obtain answers to some questions related to the integration of technology in
Moroccan public schools, especially as the study takes place in a context where the Ministryof Education is initiating its efforts to implement the twelve commitments of the Roadmap
2022-2026. Moreover, as further research, our study’s approach would be used to understandteachers’ concerns about particular technologies’ implementation with a limited number ofparticipants to address not only the overall profile of concerns but also look into every
participant’s concerns to design subsequently appropriate interventions according to theindividuals’ needs. In addition, for a successful implementation of change, the concerns ofchange leaders need to be addressed as well.

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References
Adams, N. B. (2002). Educational computing concerns of postsecondary faculty. Journal of
Research on Technology in Education, 34(3), 285–303.
https://doi.org/10.1080/15391523.2002.10782350
Agormedah, E. K., Ansah, E. A., Betakan, M. B., & Parker, D. (2019). Instructional
technology integration: Understanding senior high school business studies teachers’
concerns. Humanities, 4(4), 486-497. https://doi.org/10.20448/801.44.486.497
Alnujaidi, S. (2021). Adoption of mobile assisted language learning (Mall) in saudi arabian
efl classrooms. Journal of Language Teaching and Research, 12(2), 312–323.
https://doi.org/10.17507/jltr.1202.13
Amankwah, F., Sarfo, F. K., Aboagye, M. O., Konin, D., & Dzakpasu, R. K. (2022).
Concerns of university teachers about the adoption of the Moodle learning
management system in a Ghanaian University campus. Education Inquiry, 0(0), 1–21.
https://doi.org/10.1080/20004508.2022.2109849
Ashrafzadeh, A., & Sayadian, S. (2015). University instructors’ concerns and perceptions oftechnology integration. Computers in Human Behavior, 49, 62–73.
https://doi.org/10.1016/j.chb.2015.01.071
Bailey, D. B., & Palsha, S. A. (1992). Qualities of the Stages of Concern Questionnaire andImplications for Educational Innovations. The Journal of Educational Research,
85(4), 226–232. https://doi.org/10.1080/00220671.1992.9941120
Baytar, E. M., Elyacoubi, H., Saqri, N., & Ouchaouka, L. (2023). Teachers’ sense ofcompetence in terms of ICT use: The case of secondary school teachers. Research in
Learning Technology, 31. https://doi.org/10.25304/rlt.v31.2874
Baytar, E. M., Ouchaouka, L., & Saqri, N. (2022). Secondary school teachers’ uses of ICT.
Procedia Computer Science, 203, 621–626.
https://doi.org/10.1016/j.procs.2022.07.090
Brinkerhoff, J. (2006). Effects of a Long-Duration, Professional Development Academy on
Technology Skills, Computer Self-Efficacy, and Technology Integration Beliefs andPractices. Journal of Research on Technology in Education, 39(1), 22–43.
https://doi.org/10.1080/15391523.2006.10782471
Cheung, D., Hattie, J., & Ng, D. (2001). Reexamining the Stages of Concern Questionnaire:
A Test of Alternative Models. The Journal of Educational Research, 94(4), 226–236.
https://doi.org/10.1080/00220670109598756
Dele-Ajayi, O., Fasae, O. D., & Okoli, A. (2021). Teachers’ concerns about integrating
information and communication technologies in the classrooms. PLoS ONE, 16(5May). https://doi.org/10.1371/journal.pone.0249703
Dubey, A. D. (2016). ICT in education: Evaluating the concerns of the in-service students ofFiji National University. International Journal of Information and Communication
Technology Education, 12(4). https://doi.org/10.4018/IJICTE.2016100104
Fearnley, M. R., & Amora, J. T. (2020). Learning Management System Adoption in Higher
Education Using the Extended Technology Acceptance Model. IAFOR Journal of
Education, 8(2), 89–106. https://doi.org/10.22492/ije.8.2.05

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Fuller, F. F. (1969). Concerns of Teachers: A Developmental Conceptualization. American
Educational Research Journal, 6(2), 207–226.
https://doi.org/10.3102/00028312006002207
George, A. A., Hall, G. E., & Stiegelbauer, S. (2013). Measuring implementation in schools:
The stages of concern questionnaire. Southwest Educational Development
Laboratory.
Georgiou, Y., & Ioannou, A. (2019). Investigating In-Service Teachers’ Concerns About
Adopting Technology-Enhanced Embodied Learning. Lecture Notes in Computer
Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture
Notes in Bioinformatics), 11722 LNCS.
https://doi.org/10.1007/978-3-030-29736-7_47
Gu, N., Donovan, L., Green, T., Ma, S., & Currie, D.-J. (2023). Higher education faculty
concerns teaching in a hybrid environment: Implications for Chinese private higher
education faculty developers and faculty. International Journal of Professional
Development, Learners and Learning, 5(1), ep2302.
https://doi.org/10.30935/ijpdll/12638
Hall, G. E., George, A. A., & Rutherford, W. L. (1977). Measuring Stages of Concern about
the Innovation: A Manual for the Use of the SoC Questionnaire.
https://eric.ed.gov/?id=ED147342
Hall, G. E., & Hord, S. M. (2014). Implementing Change: Patterns, Principles, and Potholes.
Pearson.
Hall, G. E., Wallace, R. D., & Dossett, W. A. (1973). A Developmental Conceptualization of
the Adoption Process Within Educational Institutions.
https://eric.ed.gov/?id=ED095126
Hansson, P.-O. (2021). Teaching Practice Online: Challenges in Japan, India and Kenya
Under Pandemic. IAFOR Journal of Education, 9(2), 77–91.
https://doi.org/10.22492/ije.9.2.05
He, Y. Z., & Yusop, F. D. (2020). The dataset for the stages of concerns of public-school
teachers towards the use of e-learning platform: Malaysian context. Data in Brief, 29.
https://doi.org/10.1016/j.dib.2020.105230
Huang, F., Zhao, M., Qi, J., & Zhang, R. (2023). English teachers’ perceptions of emergency
remote teaching: Emotional attitudes, professional identity, and coping strategies.
Frontiers in Psychology, 13.
https://www.frontiersin.org/articles/10.3389/fpsyg.2022.1064963
Kayaduman, H., & Demirel, T. (2019). Investigating the concerns of first-time distanceeducation instructors. International Review of Research in Open and Distributed
Learning, 20(5), 85–103. https://doi.org/10.19173/irrodl.v20i5.4467
Magallanes, K., Chung, J. Y., & Lee, S. (2022). The Philippine teachers concerns on
educational reform using concern based adoption model. Frontiers in Education, 7,763991. https://doi.org/10.3389/feduc.2022.763991
Masarweh, M. A. (2019). Evaluating m-learning system adoption by faculty members in
Saudi Arabia using concern based adoption model (CBAM) stages of concern.
International Journal of Emerging Technologies in Learning (IJET), 14(05), 153–
164. https://doi.org/10.3991/ijet.v14i05.8296

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Njiku, J., Maniraho, J. F., & Mutarutinya, V. (2019). Understanding teachers’ attitude
towards computer technology integration in education: A review of literature.
Education and Information Technologies, 24(5), 3041–3052.
https://doi.org/10.1007/s10639-019-09917-z
Ohlemann, S., Imhof, M., & Bellhäuser, H. (2023). Implementing reform in the teacher
education system: Concerns of teacher educators. Teaching and Teacher Education,
126, 104087. https://doi.org/10.1016/j.tate.2023.104087
Sandholtz, J. H., & Reilly, B. (2004). Teachers, not technicians: Rethinking technicalexpectations for teachers. Teachers College Record, 106(3), 487–512.
https://doi.org/10.1111/j.1467-9620.2004.00348.x
Sarfo, F. K., Amankwah, F., Baafi-Frimpong, S., & Asomani, J. (2017). Concerns of teachers
about the implementation of information and communication technology curriculumin basic education in Ghana. Contemporary Educational Technology, 8(2), 103–118.
https://doi.org/10.30935/cedtech/6190
Shotsberger, P. G., & Crawford, A. R. (1999). On the elusive nature of measuring teacher
change: An examination of the stages of concern questionnaire. Evaluation &Research in Education, 13(1), 3–17. https://doi.org/10.1080/09500799908666942
Stracke, C. M., Burgos, D., Santos-Hermosa, G., Bozkurt, A., Sharma, R. C., Cassafieres, C.
S., Inamorato Dos Santos, A., Mason, J., Ossiannilsson, E., Shon, J. G., Wan, M.,
Agbu, J.-F. O., Farrow, R., Karakaya, Ö., Nerantzi, C., Ramírez-Montoya, M. S.,
Conole, G., Cox, G., & Truong, V. (2022). Responding to the initial challenge of the
COVID-19 pandemic: Analysis of international responses and impact in school andhigher education. Sustainability (Switzerland), 14(3), 1876.
https://doi.org/10.3390/su14031876
The Ministry of National Education. (2022). Roadmap 2022-2026. [online] Available at:https://www.men.gov.ma/Ar/Documents/FRoute20222026ar.pdf
Tzankova, I., Compare, C., Marzana, D., Guarino, A., Di Napoli, I., Rochira, A., Calandri, E.,
Barbieri, I., Procentese, F., Gatti, F., Marta, E., Fedi, A., Aresi, G., & Albanesi, C.
(2023). Emergency online school learning during COVID-19 lockdown: A qualitative
study of adolescents’ experiences in Italy. Current Psychology, 42(15), 12743–12755.
https://doi.org/10.1007/s12144-021-02674-8
Vaughan, W. (2002). Professional development and the adoption and implementation of new
innovations: Do teacher concerns matter? International Electronic Journal for
Leadership in Learning, 6, 1–13.
Yang-Hsueh, C., & Syh-Jong, J. (2014). Interrelationship between stages of concern andtechnological, pedagogical, and content knowledge: A study on Taiwanese seniorhigh school in-service teachers. Computers in Human Behavior, 32.
https://doi.org/10.1016/j.chb.2013.11.011
Ziegenfuss, D., Furse, C., Sykes, E., & Buendía, E. (2019). Beyond the click: Rethinking
assessment of an adult professional development MOOC. International Journal of
Teaching and Learning in Higher Education, 31(1), 63–72.
Corresponding author: El Mustapha Baytar
Email: elmustapha.baytar@enscasa.ma

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Rethinking Education: An In-Depth Examination of Modern Technologies
and Pedagogic Recommendations
Murielle El Hajj
Lusail University, Qatar
Hiba Harb
Lusail University, Qatar

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Abstract
Educational technology has changed the teaching and learning process in many ways. As
teachers needed new strategies to adapt to emerging technology tools and to interact withstudents, immediate feedback and digital instructional resources had become the norm.
Teaching and learning expectations are growing because of technological improvements. Many
digital libraries and online resources are now easily accessible to teachers and students.
Therefore, a qualified teacher must be aware of the new teaching requirements and theadjustments related to educational technology trends. Additionally, it is important to adopt a
Technological Pedagogical Content Knowledge (TPCK) framework and to clarify howteachers and students can collaborate to achieve learning outcomes through updated
educational practices. This collaborative education can occur because technology makes it
easier for students and teachers to interact together, despite the physical distance that could
separate them. This paper emphasizes the optimum use of educational technologies and thepotential of new digital tools that have already been exploited in an unprecedented way. It aims
to provide a comprehensive review of current technologies with pedagogic recommendations,to highlight the importance of educational technologies in teaching, and to reconsider strategies
for acquiring digital skills in the Artificial Intelligence era. The discussion revolves around
identifying and depicting various advanced educational technologies that can be integrated
with engaging pedagogical approaches. Furthermore, it explains how each technology canenhance the learning experience, emphasizing the teacher’s role in the technology-based
educational process.
Keywords: active/interactive learning, education, student-centred approach, teachingstrategies, technology, technology-based education

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The teaching and learning process is currently undergoing adjustments. How students learn
and how knowledge is transmitted globally have both been significantly impacted by the paceat which technological innovations can bring about changes in pedagogy. The use of new anddeveloping technologies is creating challenges to both the traditional approaches to educationand instruction as well as the structure of the educational system. Despite the substantial
influence of information technology on educational studies and developments, instant accessto a wide range of data makes it challenging to assess information. Therefore, modern
technology provides a wide range of tools for improving education in the digital age. The
adoption of audiovisual instructional devices by teachers is currently recommended, and thevariety of educational materials is always growing.
This paper aims to explore the integration of technology in K-12 classroom setting through thelens of Technological Pedagogical Content Knowledge (TPCK). The primary focus is on howtechnology can be harnessed to support a student-centered learning environment. By examining
the intersection of technology, pedagogy, and CK within the TPCK framework, this study
elucidates how educators can effectively leverage technology to create engaging learning
experiences that prioritize the specific needs of students. The rationale behind this focus lies inthe significance of nurturing a technologically enriched learning environment that not only
caters to students’ diverse learning styles but also prepares them to thrive in an increasingly
digital and interconnected world. Emphasizing the K-12 setting within the TCPK framework
is crucial as it provides a targeted and tailored approach to address the unique challenges andopportunities present in primary and secondary education. Ultimately, this exploration aims toempower educators with the knowledge and strategies needed to use technology’s potential
effectively and create impactful educational experiences for students.
Many studies have been conducted on education and technology, specifically after the COVID19 pandemic due to the huge shift of education to online learning. However, technology in
education has always been an area of discussion and exploration. In a study published in 2003,
Linn argues that there appears to be no end in sight for the number of technological tools thatare being invented. They have also altered the method in which we interact with one anotherand how we perceive the environment. In another study, Oliver (2005) demonstrates how the
integration of digital technology into educational settings has resulted in positive shifts anddevelopments in today’s educational environment. Alkhamisi and Monowar (2013) reveal how
augmented reality enhances one’s view of the real world by superimposing digitally generated
visual, audio, or other sensory information. They reckon that technology can help students
better capture their ideas, which will ultimately improve their educational qualifications, andthat the scope of its influence has a positive impact on the ways in which students learn andcollaborate with their instructors. Turgut and Aslan (2021) discuss that there are now more
opportunities for the dissemination of learning material and for gaining access to it because of
the proliferation of new communication channels brought about by the internet.
Many of the previous studies highlighted the importance of using technological tools as ameans of delivering information in the teaching and learning process. This integration oftechnology into education falls within the realm of TPCK, which emphasizes the critical99

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interplay between technological, pedagogical, and content knowledge. However, there is a
research gap in the literature in studies highlighting the importance of applying different
pedagogical approaches centered on the learner using technology and informed by TPCK.
Therefore, our discussion focuses on the different pedagogical approaches infused with TPCK
that can be used in combination with educational technologies, and how each approach can beused to enhance the learning experience and improve student learning outcomes. We will start
our discussion with a theoretical framework about TPCK and a review of the literature, thenwe will discuss the importance and ways of using technology in teaching strategies as well as
the importance of the teacher’s role in technology-based education. We will conclude our study
by highlighting the role of technology in enhancing a student-centered learning environmentand by providing recommendations for future studies.
Theoretical Framework
TPCK is a theoretical framework that intricately weaves three vital components in education:technology, pedagogy, and content knowledge (CK) (Mishra and Koehler, 2006). TPCK
emphasizes integrating technology to enrich teaching and learning experiences significantly.
The TPCK framework comprises seven factors, each defining a specific aspect of teacher
knowledge for effective technology integration. These components include technology
knowledge (TK), referring to knowledge about various technologies; CK, which involves
understanding the subject matter to be taught; pedagogical knowledge (PK), encompassing
teaching methods and processes; pedagogical content knowledge (PCK), blending content andpedagogy for better teaching practices; technological content knowledge (TCK), understanding
how technology can create new representations for content; technological pedagogicalknowledge (TPK), knowledge of using technology in teaching; and technological pedagogicalcontent knowledge (TPACK), the integration of technology in any content area.
The TPCK framework emphasizes the importance of teachers having a deep understanding of
the interplay between content, pedagogy, and technology to enhance student learning
effectively. This understanding serves as a foundation to devise context-specific strategies andrepresentations tailored to individual circumstances. For technology integration to be
productive in teaching, it must holistically address the three key elements, recognizing theirinterdependent relationships within the system (Mishra and Koehler, 2006). Educators withwell-developed TPCK can select appropriate technology aligned with learning goals, design
engaging lessons that promote active learning and critical thinking, cater to diverse student
needs, facilitate assessment and feedback through technology, and foster 21st-century skills
such as creativity and collaboration.
This paper presents a comprehensive examination of technology and its role in successful
teaching and learning. The discussion provides insights into the development of TPCK throughcontinuous professional development, fostering a growth mindset, and implementing various
strategies. Specifically, it offers practical tips on how each technological tool can be effectively
utilized in the teaching process to enhance student engagement, improve learning outcomes,

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and create personalized and differentiated learning experiences. The focus is to empower
educators to leverage TPCK to establish engaging, student-centered learning environments that
foster academic success and nurture essential 21st-century skills.
Literature Review
Learning may now be achieved rapidly, and all individuals can receive education in the age ofdigital technology, which also coincides with the new era of multimedia tools and web
networks. The quality of education is supported by new technologies, and more people are
given opportunities to learn. Sharing information and enhancing one’s ability to communicate
are two key aspects of education. Students are more likely to work well together and develop
excellent communication skills when they have access to technology. Online collaboration
between teachers and students allows for the completion of any project or task. In educational
broadcasting, for instance, the utilization of technological instruments as well as
recording systems based on computer technology are all important factors. Learners who areunable to attend classes have a better chance of accessing education using distance learning.
“The Internet of Things (IoT) is proven to be one of the most cost-effective methods ofeducating young brains. It is also a robust mechanism for integrating a world-class learning
experience for everybody.” (Haleem et al., 2022, p. 275) This is certainly relevant forindividuals who are home schooled.
In a review of the literature from 1996 through 2008, Means et al. (2010) reported that students
in online learning conditions generally performed moderately better than those receiving faceto-face teaching. However, despite this advantage, Mishra and Koehler (2006) argued that
simply adopting technology in pedagogical approaches is not enough; rather, they stressed thenecessity of developing TPCK, a complex, situated form of knowledge that intertwines content,pedagogy, and TK. While TPCK holds promise, the study published in 2009 recognized thecomplexity of teaching and the challenges posed by technology integration, indicating the needfor a nuanced understanding of the interplay between these domains. Voogt et al. (2013)
conducted a systematic literature review, unveiling diverse interpretations of TPCK andtechnological knowledge, raising concerns about its measurement and emphasizing theimportance of subject-specific TPCK understandings. Moreover, Herrington and Kervin(2007) cautioned against ad hoc technology integration, advocating for teachers to adopt
technology as cognitive tools to empower students in authentic learning experiences.
Nonetheless, the role of teachers remains crucial in providing clear purpose and support fortechnological integration. The same idea has been explored by Hew and Cheung (2013) in astudy emphasizing that the positive effects of Web 2.0 technologies are not solely attributed tothe technologies themselves but rather to how they are utilized and conceptualized in thelearning process. Meanwhile, Bower et al. (2015) stressed the importance of designing richmedia synchronous technologies for active learning in face-to-face instructions, highlighting
considerations of communicative requirements and cognitive load. However, Alharthi’s study
in 2021 revealed university students’ difficulties in using technological tools and emphasized
the need for training before taking online courses. Furthermore, the study highlighted thelimited use of diverse online activities in university courses, warranting a comprehensive101

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approach to technology integration in education. While highlighting the widespread acceptanceof technology in education, Akram et al. (2022) also identified numerous barriers to effective
technology integration, necessitating solutions to resource constraints, lack of leadershipsupport, inadequate infrastructure, and other challenges.
From the above-mentioned examples, the existing literature on technology integration in
education primarily focuses on examining the limitations of technology in the classroom.
Moreover, numerous studies have concentrated on the shift to online learning, utilizing
technology primarily as a means of communication between teachers and students. However,there is a noticeable gap in the literature regarding the necessity of transforming the educational
paradigm to keep pace with technological advancements in the context of TPCK. This limited
perspective fails to explore the full potential of technological tools beyond communication andtheir integration within TPCK’s dynamic framework. To address this gap, it is crucial toinvestigate adapted teaching strategies that harness the potential of technology to actively
engage students in the learning process while aligning with TPCK principles. In response tothis need, our present research sheds light on teaching strategies that effectively incorporate
technological tools within the TPCK framework to create and sustain student-centered learning
environments.
By conducting this study with a focus on K-12 students and educators, we aim to bridge thegap in the literature and emphasize the integration of technology within TPCK. Our goal is tocontribute significantly to the development of effective and forward-looking educational
practices that harness technology’s transformative power to its fullest extent in the K-12classroom setting. Through this approach, we strive to empower educators with insights andstrategies that can effectively create student-centered, technology-enhanced learning
environments for K-12 students, fostering their active participation and success in the
educational process.
Engaging Teaching Strategies Using Technology
Engaging teaching strategies start with designing the classroom having the students in mind.
The active/interactive learning strategy encourages students to discuss, participate, investigate,
and create. It challenges them by questioning them, requiring problem-solving and criticalthinking. Adopting this strategy starts by shifting from the traditional classroom setting to theroundtable classroom setting. The latter encourages interactive learning, face-to-faceaccountability, and verbal immediacy (Parsons, 2017). In addition, active/interactive learning
increases the students’ CK (see Menekse et al., 2013) and contributes to co-inferring new
information with the help of input from one’s learning partners enabling students to co-createknowledge (see Chi and Wylie, 2014). While changing the classroom setting can provide a
conducive environment for active/interactive learning and a student-centered approach, it is
not enough on its own. The success of active and interactive learning largely depends on the
instructional strategies and techniques used by the teacher. The teacher needs to use
pedagogical approaches that encourage inquiry-based learning, critical thinking, and problemsolving skills. Additionally, integrating technology and multimedia resources into the learning

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process enhances active/interactive learning and promotes student-centeredness within thecontext of TPCK. Therefore, a combination of classroom design, instructional strategies, andtechnological tools helps create a more engaging and student-centered learning environment.
We will focus on the use of active/interactive teaching strategies in the K-12 classroom and theadapted technological tools that further enhance students’ engagement.
Brainstorming
Brainstorming is a commonly used technique in education where a group of students work
together to generate and share ideas related to a particular topic or problem. The primary goalof brainstorming is to encourage creativity and critical thinking by allowing students to freely
express their thoughts and ideas without fear of judgment or criticism. During a brainstorming
session, the teacher can collect students’ ideas and facilitate a discussion that encourages
students to share and respond to each other’s ideas. This helps create an open and inclusive
learning environment where students can learn from each other and build on their collective
knowledge. Moreover, brainstorming allows the teacher to assess the students’ priorknowledge and understanding of a topic. By gathering information about what students alreadyknow, the teacher can tailor the instruction to meet the needs of individual learners and provide
input on what they do not know. Brainstorming can be conducted in various formats, such asverbal discussions, written notes, or using technological tools such as Tricider, AnswerGarden,or MindMeister. When using Tricider1, the teacher creates a board and invites students tosubmit their ideas on a topic or problem. The board can be shared with the whole class, andeveryone can see and vote on each other’s ideas. The teacher can also use the voting feature,
which allows students to vote on the most important or relevant ideas. Once the brainstorming
session is complete, the teacher can use Tricider to facilitate a discussion on the ideas
generated. Students can comment on each other’s ideas, and the teacher can provide feedback
and guidance. AnswerGarden2 is another tool that could be used in this teaching strategy. The
teacher creates a simple questionnaire and asks students to add words they know related to the
topic. These words are displayed on the teacher’s board as they are added in the form of a word
cloud. The size of each answer in the word cloud depends on how frequently it has been
submitted by the students. This makes it easy for the teacher to quickly identify the mostcommon responses and themes. MindMeister3 is also a tool that could be used forbrainstorming, and to organize ideas into themes. The teacher creates a mind map of a topic
and asks students to join it and contribute their ideas. Moreover, the teacher can use the voting
feature to have students vote on the most important or relevant ideas. This contributes tonarrowing down the focus and identifying the most important points for further discussion.
Once the brainstorming session is complete, the teacher presents the mind map to the class tosummarize the ideas generated during the session. This helps students understand the thinking
process behind the ideas and provides a visual representation of the brainstorming session.

1

Tricider: https://www.tricider.com/
AnswerGarden: https://answergarden.ch/3
MindMeister: https://www.mindmeister.com/
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Regardless of the format, the key to successful brainstorming is to create a safe and supportive
environment that encourages open and honest communication among students.
Flipped Classroom
The flipped classroom approach involves providing students with access to online video
lectures or tutorials before the in-class sessions. This allows students to engage in interactive
and collaborative activities, such as debates, problem-solving activities, and discussions. In theclassroom, the teacher acts as a facilitator and observes the students’ performance, providing
individual or group feedback, as needed. The flipped classroom model enhances students’learning experiences by allowing them to engage with the material at their own pace andencouraging them to take an active role in their learning. Additionally, it allows teachers to useclassroom time more efficiently and effectively, as they can focus on providing personalized
support and guidance to their students (See Loizou, 2022). The technological tools that can beused in this teaching strategy include video creation and sharing tools. These tools can be used
to create and share pre-recorded lectures, tutorials, or other instructional videos that students
can watch before the classroom session. The most common video creation tools are Camtasia
and Adobe Premiere Rush. With Camtasia1, the teacher can record the computer screen,
webcam, and audio simultaneously, allowing the creation video tutorials and demos that show
exactly how to complete a task. In addition to recording and editing videos, Camtasia alsooffers features for sharing and publishing the generated product. The teacher can export thevideos in various formats, including MP4, WMV, AVI, and MOV, and share them on
YouTube, or other online platforms. Adobe Premiere Rush2 allows teachers to create and edit
high-quality videos. It also includes features such as trimming and splitting clips, adding text
overlays and graphics, and creating customized titles and transitions, as well as adding motion
graphics and animations. Moreover, this tool includes integration into popular social media
platforms such as YouTube, Instagram, and Facebook, making it easy to export videos in thecorrect format and aspect ratio. Other technological tools used for flipped classroom include
online discussion and collaboration tools, such as Flipgrid3. The latter is a video-based social
learning platform that allows teachers and students to create and share short video responses toprompts or questions. With Flipgrid, the teacher can create a “Grid”, which is a collection of
topics for discussion or prompts for students to respond to using video. Students can then create
video responses to these prompts and respond to their classmates’ videos. Subsequently, theteacher provides feedback and assessments on the student videos. Furthermore, students canrecord and edit their videos using a range of editing tools, including filters, emojis, and text
annotations. Flipgrid integrates with other educational tools such as Microsoft Teams, Google
Classroom, and Canvas, making it easy to incorporate into existing workflows.

1

Camtasia: https://www.techsmith.com/video-editor.html
Adobe Premiere Rush: https://filmora.wondershare.net/alternative-adobe.html
3
Flipgrid: https://info.flip.com/en-us/blog/product-updates/flipgrid-updates-timeline.html
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Inquiry-Based Learning
Inquiry-based learning is an approach that focuses on developing students’ problem-solving
skills by engaging them in an active and collaborative learning process. In this approach, theteacher plays the role of a facilitator rather than a lecturer. He/she poses questions, situations,and problems that require students to think critically and find solutions. Students are thenencouraged to research these topics individually or in groups, using various resources such as
books, articles, and websites, to formulate their answers. This research process not only helps
students develop their understanding of the topic, but also enables them to practice essential
research and information literacy skills. After conducting their research, students present theirfindings and supporting evidence to the class. This presentation allows them to further develop
their answers by listening to what their peers have found, identifying areas that require more
attention, and refining their arguments. This also promotes collaboration and teamwork among
students, which is essential in today’s workforce. Moodle1, a virtual learning environment, canbe used as a tool to support inquiry-based learning strategies. The teacher creates a repositoryof learning materials such as videos, articles, and case studies that students can access anytime
and anywhere. This helps students engage in self-directed learning and explore the topic they
are researching. Moodle also provides various tools for collaboration such as discussion
forums, wikis, and chat rooms. Moreover, the teacher can create groups and assign groupprojects, which promotes teamwork and helps students develop important skills such ascommunication, delegation, and negotiation. This platform also allows teachers to monitor
student progress, provide feedback, and assess their learning. The feedback helps students
understand their strengths and weaknesses and provides guidance on how to improve theirwork.
Google Workspace2 (formerly Google Suite) is another tool that could be used in inquiry-basedlearning. Students can collaborate, share ideas, and receive feedback in real-time, which
enhances their learning experience and improves their critical thinking skills. The tools
available in Google Suite can help streamline the inquiry-based learning process and make it
more efficient and effective. On the one hand, students can use Google Docs to collaboratively
write and edit documents related to their research and inquiry questions. They can also share
their documents with their peers and teachers for feedback and suggestions. Students can alsouse Google Slides to collaboratively create presentations and share their findings and solutions
to problems they have investigated. They can also use Google Drive to store and organize theirresearch and inquiry materials, such as articles, videos, and images. On the other hand, teachers
can create through Google Forms surveys and quizzes to collect feedback from students on
their understanding of a particular topic or to assess their progress in their inquiry-basedlearning journey. They can also use Google Classroom to create a virtual learning environment
where they can share resources, assign tasks, and provide feedback to students. ThingLink3 isanother tool that can be utilized to facilitate engaging and interactive inquiry-based learning

1

Moodle: https://moodle.org/
Google Workspace: https://workspace.google.com/business/3
ThingLink: https://www.thinglink.com/
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experiences for students. By using ThingLink, teachers design interactive images, videos,historical events, scientific experiments, and presentations that students can explore anddiscover on their own, enabling them to gain a deeper understanding of a wide range of topics.
Additionally, teachers can leverage ThingLink to create interactive presentations thatencourage collaboration among students. They can invite their peers to add their own
information, links, and videos to these presentations, thereby facilitating peer-to-peer learning.
By designing interactive images and videos that provide different learning styles and abilities,
teachers can ensure that all students have access to the same learning opportunities. For
example, by providing links to different types of resources such as text, audio, or video,teachers can accommodate different learning styles and abilities. Nearpod1 can also support
inquiry-based learning as teachers can use its interactive features to design engaging
presentations and activities that motivate students to investigate, examine and learn about
various topics. Before initiating the inquiry process, teachers can utilize Nearpod to evaluate
students’ preexisting knowledge and comprehension of a given topic. They can also create
interactive lessons that allow students to explore and examine a topic, and work collaboratively
to share their discoveries and insights with each other. Furthermore, through Nearpod, teachers
can provide students with multimedia materials such as images, videos, and audio files toimprove their research and inquiry.
Open-Ended Questions
The technique of using open-ended questions promotes lively class discussions and helps
improve students’ communication skills. By posing open-ended questions, the teacher prompts
students to connect various pieces of information they have acquired or encountered in theirlives, enabling them to link related ideas. This approach not only encourages students toexpress their own views but also allows them to find their own voice. Seesaw2 is a digital
portfolio tool that can be effectively utilized for open-ended questioning in the classroom. By
using Seesaw, teachers can present open-ended questions to students and encourage them torespond in various formats, including written responses, audio recordings, and videos. Students
can use Seesaw to share their work and ideas with their peers, creating an environment thatencourages collaboration. Furthermore, Seesaw’s feedback and commenting features provide
opportunities for ongoing discussions, promoting critical thinking and reflection among
students. Additionally, teachers can use Seesaw to offer personalized feedback to students on
their open-ended responses, supporting them in further enhancing their communication andcritical thinking abilities. Poll Everywhere3 is another tool that can facilitate open-ended
questions effectively. It allows teachers to ask questions and receive instant responses fromtheir students. Using Poll Everywhere, students can respond anonymously, which canencourage them to express their thoughts and ideas more honestly. Teachers can display the
responses in real-time, stimulating class discussions and encouraging students to share their

1

Nearpod: https://nearpod.com/
Seesaw: https://web.seesaw.me/
3
Poll Everywhere: https://www.polleverywhere.com/
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opinions. Furthermore, Poll Everywhere enables students to respond with multimedia such asimages and videos, providing a platform for creative thinking and self-expression.
Peer and Self-Feedback
Developing the ability to offer and accept constructive feedback is an important aspect ofstudents’ learning. Peer feedback plays a crucial role in enabling students to evaluate and assesseach other’s work, helping them gain insights into the quality of their own work and develop
evaluative judgement and self-regulated learning skills (Nicol, 2010). Digital tools such as
Canvas Pages, Google Docs, and Peergrade can facilitate peer feedback. Canvas Pages1 can beutilized for peer feedback in several ways. The teacher can assign group assessments and create
a separate Canvas Page for each group to collaborate on. By utilizing the commenting feature,each group can offer feedback on each other’s work uploaded on the Canvas Page. Moreover,students can create their ePortfolios on Canvas Pages to share their work and receive
constructive feedback from their peers. Canvas Pages allow for multimedia elements like
videos, images, and links, which provide students with a variety of options to enhance their
feedback. Additionally, Canvas Pages maintains a revision history, enabling students to keep
track of their progress over time and make revisions based on the feedback received. Google
Docs2 is a collaborative writing tool that can facilitate peer feedback within a classroom setting.
By assigning group writing projects, teachers can encourage students to share feedback andwork together in creating a shared document using Google Docs. For peer editing purposes,
teachers can pair up students and encourage them to use the commenting and suggesting
features in Google Docs to provide feedback and make necessary changes to their peer’s
writing. Students can also create writing portfolios using Google Docs, which allows them todisplay their work and receive feedback from their peers. With Google Docs, students can track
the revision history of their work, which gives them an opportunity to analyze their progressand make improvements to their writing skills accordingly. Peergrade3 is an online platformthat focuses on peer feedback and evaluation. With Peergrade, teachers can create assignments
and rubrics that are distributed to students, who can then submit their work through the platform
for peer review. Peergrade ensures that each student receives multiple reviews from his/herpeers by assigning grades based on the teacher’s rubrics. Anonymous feedback is available in
Peergrade, encouraging students to be more honest and constructive. Peergrade reports allow
teachers to identify areas for improvement as they monitor the feedback process and view thecomments and grades assigned by students.
In addition to peer feedback, self-feedback is also valuable for students as it helps them in theirself-regulation and critical self-reflection, which are essential for achieving success (Ryan,
2020). By engaging in self-feedback, students can identify their strengths and weaknesses, setgoals, and make necessary improvements to their work. Self-feedback helps students become
more self-aware, reflective, and proactive in their learning process. Teachers can facilitate this1

Canvas Pages: https://www.canva.com/
Google Docs: https://www.google.com/docs/about/3
Peergrade: https://www.peergrade.io/
2

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process by providing students with clear and specific criteria or rubrics to evaluate their work,
using rubrics to provide students with a framework for self-assessment and help themunderstand the expectations for their work. Using digital tools such as ePortfolios, students cancreate a personalized space to upload their work, provide self-assessment and reflection, andreceive feedback from their teachers and peers. Platforms for creating ePortfolios include
Google Sites1, Weebly2, and WordPress3. Through self-feedback, students gain a deeper
understanding of their own learning process, develop self-regulatory skills, and become
proactive learners.
Peer Learning
According to Havnes et al. (2016), peer learning can offer several advantages to students, suchas enhancing time management, communication, and study skills, encouraging creative andcritical thinking, promoting psychological well-being, increasing academic aspirations andexpectations, and fostering lifelong learning. To implement this teaching strategy, the teacher
can group students and provide them with different pieces of information related to the subject.
Each group learns and comprehends the information to explain it to other groups. The process
is repeated until every group has a complete understanding of the subject matter. Alternatively,
students can select an area of interest within the subject matter and conduct independent
research on it. They can then create a PowerPoint presentation to present to the class, which
can encourage information sharing and group discussion. Quizlet4 is an effective digital toolthat enhances the learning experience through peer collaboration. This online platform allows
students to create digital flashcards, which can be used to test their knowledge and shared with
their peers, enabling them to actively participate in the learning process and reinforce theirunderstanding. In addition to flashcards, Quizlet provides several other features that support
peer learning. For example, it offers various study modes such as games, quizzes, and audio
materials that are aligned with different learning styles, allowing students to engage with thematerial in diverse ways. This aspect promotes mutual learning and allows students to benefit
from the diverse insights and perspectives of their peers.
Project-based learning (PBL)
Project-based learning (PBL) is an instructional approach that offers students a hands-on, realworld learning experience. The teacher proposes a project, and students work together toanalyze, research, and develop an action plan to address the given problem. PBL encourages
students to engage with the subject matter actively and apply what they have learned to theirproject. This learning strategy fosters the development of critical thinking, problem-solving,and decision-making skills. The teacher takes on the role of a guide and mentor, supporting
students throughout the learning experience. Through PBL, students become more selfdirected in their learning, taking ownership of their project, and making decisions about their
1

Google Sites: https://workspace.google.com/intl/en/lp/sites/
Weebly: https://www.weebly.com/
3
WordPress: https://wordpress.com/
4
Quizlet: https://quizlet.com/
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work. This approach also promotes the development of social and emotional skills, such as
collaboration and effective communication. Glogster1 is a tool that could be used in thislearning strategy. Students can create glogs to showcase research findings or create visual
portfolios of their work, using various media types and external hyperlinks. Collaborative
projects are also possible, with students working together to create glogs that represent different
aspects of a larger topic. Another tool that could be used in PBL is Edorble2. With this platform,
students can create their 3D virtual classrooms and interact with various digital resources. For
example, in a science class, students can generate their virtual worlds based on a particular
topic such as designing a virtual ecosystem that highlights the interdependence of different
species. They can populate the ecosystem with various species and create interactive elementsthat provide more information about their relationships, helping them develop a deeper
understanding of the subject matter.
Although technology tools have revolutionized teaching and learning methods, just providing
teachers with these tools is insufficient. For technology to have a significant influence on
education, it is necessary to train teachers to use them properly. The absence of proper training
could lead to difficulties in integrating technology into teaching, which may negatively impact
the learning and engagement of students. Hence, it is imperative for educational institutions
and schools to emphasize teacher training programs that provide the essential skills andknowledge to use technology tools effectively in the classroom. That being said, it is important
to emphasize the crucial role of teachers in technology-based education.
Importance of Teacher’s Role in Technology-Based Education
The advancement in education and the enhancement of teaching and learning techniques cangreatly contribute to the improvement of students’ academic performance, especially in the K12 setting. However, the evolution of education also presents challenges for K-12 teachers
regarding the implementation of new teaching theories and methods. As education continues
to evolve, teachers are expected to take on new roles as leaders and facilitators for students,
moving away from traditional teaching techniques to become more actively involved in theirstudents’ learning process. By adopting this approach in the K-12 setting, teachers will be better
equipped to identify their students’ interests, encourage their enthusiasm for learning, anddevelop their critical thinking skills and intellectual curiosity (Kelly and McAnear, 2002). In
other words, the transformation in education offers an opportunity for teachers to shift fromthe traditional role of being just transmitters of knowledge to becoming facilitators of learning.
This means that they will not only deliver knowledge but also provide students with aconducive environment to learn actively and independently. Therefore, teachers will better
understand their students’ individual needs and interests, making learning more engaging andrelevant to their lives.

1
2

Glogster: https://edu.glogster.com/
Edorble: https://www.edorble.com/

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Although technology has transformed the teaching and learning process, it cannot replace theteacher. In a technology-based and student-centered learning environment, teachers are morethan just instructors: they are instructional designers, trainers, and mentors. As instructional
designers, teachers plan and organize their classrooms to effectively integrate technology into
their curriculum and create well-designed activities to meet the individual needs of theirstudents. They also act as trainers, showing students how to use software tools to accomplish
learning tasks. Additionally, they act as team coordinators, creating opportunities forcollaborative and social learning activities, peer tutoring, and support among students withvarying abilities. As enabling mentors, they offer advice and ask questions to help students findthe information they need to complete tasks. They work with students to create a learning
environment that is engaging and challenging, using technology to enhance learning andproviding support and guidance throughout the process. They create a safe and supportive
environment that encourages students to take risks and learn from their mistakes. Through theirmultiple roles, teachers enable students to become active and independent learners, capable ofapplying the knowledge and skills they acquire to real-world situations. Eventually, technology
is a tool that teachers can use to enhance the learning process, but it is the teacher who plays acrucial role in guiding and supporting students’ growth and success.
Therefore, while incorporating educational technology to enhance student learning, teachers
must shift their perspectives on teaching and learning and be open to new responsibilities. To
effectively address technology challenges and facilitate teaching and learning, they must be
willing to learn new skills (Zhu et al., 2010). One such skill is developing computer proficiency
to maximize the benefits of interactive and engaging technologies in education and create
positive interactions with their students. Nevertheless, for teachers to become proficient andeffective users of technology, they require tailored resources, updated training programs,
ongoing support, and flexibility (Roschelle et al., 2005). This is essential if they are expected
to perform their duties as learning facilitators, especially when integrating technology in the
K-12 classroom setting. The purpose of education is to empower rather than to provide
knowledge, as Nuyen (1995) succinctly explains. There is now a need to find educators who
can both provide knowledge and spark students’ creativity, so teachers are required to have a
deep understanding of how each student learns and the abilities to adapt the learning systems(Volman, 2005). Educational technologies are practical tools that facilitate teachers’ tasks.
Nevertheless, rather than being easier, the instructors’ responsibility will get more challenging
as they need to design various pedagogical approaches, gather educational materials, andorganize them in a systematic methodology.
Furthermore, developing TPCK requires continuous professional development, a growthmindset, and various strategies, including attending technology integration workshops,
engaging in collaborative learning communities, practicing reflection, and designingtechnology-integrated lesson plans. By aligning their teaching practices with TPCK principals
in the K-12 setting, teachers can leverage technology effectively and create dynamic learning
environments that foster active student engagement and meaningful learning experiences.
Continuous re-evaluation of teacher education practices is needed to propose new strategies
that effectively facilitate technology integration in the classroom.

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Conclusion
Technology-based education enables K-12 students to access, analyse, manage, synthesize,
evaluate, create, and share information in a variety of forms and media that incorporate a global
perspective (McNeil, 2015). This integration of technology and its various applications aligns
with the concept of TPCK, which underscores the importance of educators possessing a deep
understanding of how technology, pedagogy, and content intersect. It also reinforces a studentcentred learning environment that contributes to enhance the students’ competencies increativity, communication, collaboration, digital literacy, critical thinking, and problem
solving, all of which are central components of TPCK. Therefore, schools are responsible not
only for integrating technology into the curriculum but also for doing so in a way thatsynergizes pedagogical approaches with technology tools while considering the principles of
TPCK.
Advancing technology-based education and integrating technology tools into the teaching andlearning process is crucial in achieving this goal of comprehensive student development. This
integration, when approached with an awareness of TPCK, empowers educators to design
learning experiences that holistically nurture students’ abilities. By investing in these areas,
schools not only enhance student learning outcomes but also equip educators with thenecessary skills and resources to effectively engage students and foster their growth as
autonomous lifelong learners. Ultimately, investing in technology-based education becomesan investment in the future success of students, preparing them not only for their future careers
but also for the challenges and opportunities they will encounter in higher education anduniversity settings.
Furthermore, additional research studies are needed to address two key areas in education. The
first area is designing innovative teaching strategies to address the challenges that students facein a rapidly changing world. Teachers need to adopt creative approaches to instruction toprepare students for their future careers. The second area is the integration of technology into
the curriculum to promote student-centred learning and develop critical thinking, problemsolving, and creativity. By conducting further research in these areas, educators can better
prepare students for the future, create effective strategies for technology integration, andlifelong learning. In addition, as new technologies, such as the ChatGPT and all OpenAI tools,
become increasingly accessible to everyone, it is important to conduct further research to assist
educators in integrating Artificial Intelligence into the teaching and learning process.

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References
Akram H., Abdelrady A. H., Al-Adwan A. S. and Ramzan M. (2022). Teachers’pPerceptions oftechnology integration in teaching-learning practices: A Systematic Review. Frontiers in
Psychology, 13. https://doi.org/10.3389/fpsyg.2022.920317
Alzahrani, A. I., Alsaif, M. A., & Alghamdi, R. A. (2021). Exploring university students’attitudes toward using technology in learning: A cross-sectional study. International
Journal of Higher Education, 10(3), 43–58.
Alkhamisi A., & Monowar M. (2013). Rise of augmented reality: Current and future application
areas. International Journal of Internet and Distributed Systems, 1(4), 25–34.
https://doi.org/10.4236/ijids.2013.14005
Bower, M., Dalgarno, B., Kennedy, G. E., Lee, M. J., & Kenney, J. (2015). Design andimplementation factors in blended synchronous learning environments: Outcomes from across-case analysis. Computers & Education, 86, 1–7.
https://doi.org/10.1016/j.compedu.2015.03.006
Chi, M. T., & Wylie, R. (2014). The ICAP framework: Linking cognitive engagement to active
learning outcomes. Educational Psychologist, 49(4), 219–243.
https://doi.org/10.1080/00461520.2014.965823
D.N. Nguyen, B. Zierler, & H.Q. Nguyen. (2011). A survey of nursing faculty needs for training
in the use of new technologies for education and practice. Journal of Nursing Education,
50(4), pp. 181–189. https://doi.org/10.1016/j.susoc.2022.05.004
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
Havnes, A., Christiansen, B., Bjørk, I. T., & Hessevaagbakke, E. (2016). Peer learning in higher
education: Patterns of talk and interaction in skills centre simulation. Learning, Culture
and Social Interaction, 8, 75–87. https://doi.org/10.1016/j.lcsi.2015.12.004.
Herrington, J., & Kervin, L. (2007). Authentic learning supported by technology: Tensuggestions and cases of integration in classrooms. Educational Media International,
44(3), 219–236. https://doi.org/10.1080/09523980701491666
Hew, K. F., & Cheung, W. S. (2013). Use of Web 2.0 technologies in K-12 and higher
education: The search for evidence-based practice. Educational Research Review, 9, 47–
64. https://doi.org/10.1016/j.edurev.2012.08.001
Kelly, M.G., & McAnear, A. (2002). (Eds). National educational technology standards forteachers, preparing teachers to use technology. Eugene, OR: International Society forTechnology in Education (ISTE).
Koehler, M. J., & Mishra, P. (2009). What is technological pedagogical content knowledge(TPACK)? Contemporary Issues in Technology and Teacher Education, 9(1), 60–70.
Linn, M. (2003). Technology and science education: starting points, research programs, andtrends. International Journal of Science Education, 25, 727–758.
https://doi.org/10.1080/09500690305017.
Loizou, M. (2022). Digital tools and the flipped classroom approach in primary education.
Frontiers in Education, 7. https://doi.org/10.3389/feduc.2022.7934.

112

IAFOR Journal of Education: Technology in Education

Volume 11 – Issue 2 – 2023

McNeil, S. (2015). Twenty-first-century technology skills. In J. Spector (Ed.), The SAGE
encyclopedia of educational technology (pp. 794–796). SAGE Publications, Inc.,https://dx.doi.org/10.4135/9781483346397.n322.
Means, B., Toyama, Y., Murphy, R., Bakia, M., & Jones, K. (2010). Evaluation of evidencebased practices in online learning: A meta-analysis and review of online learning
studies. US Department of Education.
Menekse, M., Stump, G. S., Krause, S., & Chi, M. T. (2013). Differentiated overt learning
activities for effective instruction in engineering classrooms. Journal of Engineering
Education, 102(3), 346–374. https://doi.org/10.1002/jee.20021.
Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A
framework for teacher knowledge. Teachers College Record, 108(6), 1017–
1054. https://doi.org/10.1111/j.1467-9620.2006.00684.x.
Nicol, D. (2010). From monologue to dialogue: improving written feedback processes in mass
higher education. Assessment & Evaluation in Higher Education, 35(5), 501–517.
https://doi.org/10.1080/02602931003786559.
Nuyen, A. T. (1995). Lyotard and Rorty on the role of the professor. In M. Peters (Ed.),
Education and the postmodern condition (pp. 41–57). Westport, CT: Bergin and Garvey.
Oliver, R. G. (2005). Ten more years of educational technologies in education: how far have wetravelled?. Australian Educational Computing, 20(1), 18–23.
https://search.informit.org/doi/10.3316/aeipt.145837.
Parsons, C. S. (2017). Reforming the Environment: The influence of the roundtable classroom
design on interactive learning. Journal of Learning Spaces, 6(3), 23–33
Roschelle J., Sharples M., & Chan T. W. (2005). Introduction to the special issue on wirelessand mobile technologies in education. Journal of computer-assisted learning, 21(3),159–161. https://doi.org/10.1016/j.susoc.2022.05.004.
Ryan, T. (2020). Effective feedback in digital learning environments. Melbourne CSHE
Discussion Paper. Australia: The University of Melbourne. https://melbournecshe.unimelb.edu.au/__data/assets/pdf_file/0004/3417079/effective-feedback-in-digitallearning-environments_final.pdf.
Volman, M. (2005). A variety of roles for a new type of teacher educational technology and theteaching profession. Teaching and Teacher Education, 21, 15–31.
https://doi.org/10.1016/j.tate.2004.11.003
Voogt, J., Fisser, P., Pareja Roblin, N., Tondeur, J., & van Braak, J. (2013). Technologicalpedagogical content knowledge – A review of the literature. Journal of Computer
Assisted Learning, 29(2), 109–121. https://doi.org/10.1111/j.1365-2729.2012.00487.x.
Turgut, Y. E., & Aslan, A. (2021). Factors affecting ICT integration in TURKISH education: A
systematic review. Education and Information Technologies, 26(4), 4069-4092.
https://doi.org/10.1007/s10639-021-10441-2.
Zhu, C., Valcke, M., & Schellens, T. (2010). A cross-cultural study of teacher perspectives onteacher roles and adoption of online collaborative learning in higher education. European
Journal of Teacher Education, 33(2), 147–165. https://doi.org/10.1080/02619761003631849
Corresponding author: Murielle El Hajj
Email: mnahas@lu.edu.qa

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Reviewers – Volume 11 – Issue 2
The editorial team, Dr Daniel L. Hoffman, Dr Michael P. Menchaca, and Dr Devayani Tirthali
would like to thank the following reviewers for their contributions to the peer review of thisissue. Their dedication and assistance are greatly appreciated.
Senior Reviewers
Dr Shiao-wei Chu, National Chin-Yi University of Technology, Taiwan
Dr Elena Mishieva, Moscow State University, Russia
Dr Leonardo Munalim, The Philippine Women's University-School of Arts & Sciences andSchool of Education, The Philippines
Dr Murielle El Hajj Nahas, Lusail University, Qatar
Dr Michelle Yeo, Coventry University and University of Newcastle, Australia
Dr Serpil Meri-Yilan, Ibrahim Cecen University, Turkey
Reviewers
Dr Philip Ajewole, Federal University Oye-Ekiti, Nigeria
Dr Hee Seon Jang, Pyeongtaek University, Korea
Dr Michelangelo Magasic, Kochi University of Technology, JapanDr Debbie Meharg, Edinburgh Napier University, United Kingdom
Dr Suja Nair, Educe Micro Research, India
Dr Nancy Nisbett, California State University, Fresno, USA
Dr Queen Ogbomo, Tennessee Technological University, USA
Dr Urarat Parnrod, Songkhla Rajabhat University, Thailand
Dr Elena Raevskikh, Department of Culture and Tourism, United Arab Emirates
Dr Miguel Varela, Computer Faculty, University of Doha for Science and Technology, Qatar

the iafor

journal of education

ISSN: 2187-0594