IAFOR Journal of Education: Technology in Education

Volume 11 – Issue 2 – 2023

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

7

IAFOR Journal of Education: Technology in Education

Volume 11 – Issue 2 – 2023

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

IAFOR Journal of Education: Technology in Education

Volume 11 – Issue 2 – 2023

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,

9

IAFOR Journal of Education: Technology in Education

Volume 11 – Issue 2 – 2023

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

10

IAFOR Journal of Education: Technology in Education

Volume 11 – Issue 2 – 2023

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

11

IAFOR Journal of Education: Technology in Education

Volume 11 – Issue 2 – 2023

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).

12

IAFOR Journal of Education: Technology in Education

Volume 11 – Issue 2 – 2023

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

13

IAFOR Journal of Education: Technology in Education

Volume 11 – Issue 2 – 2023

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).

14

IAFOR Journal of Education: Technology in Education

Volume 11 – Issue 2 – 2023

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).

15

IAFOR Journal of Education: Technology in Education

Volume 11 – Issue 2 – 2023

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

16

IAFOR Journal of Education: Technology in Education

Volume 11 – Issue 2 – 2023

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

IAFOR Journal of Education: Technology in Education

Volume 11 – Issue 2 – 2023

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

18

IAFOR Journal of Education: Technology in Education

Volume 11 – Issue 2 – 2023

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.”

19

IAFOR Journal of Education: Technology in Education

Volume 11 – Issue 2 – 2023

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

20

IAFOR Journal of Education: Technology in Education

Volume 11 – Issue 2 – 2023

“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.

21

IAFOR Journal of Education: Technology in Education

Volume 11 – Issue 2 – 2023

Figure 1
Integration of the UTAUT2 Constructs with Identified Themes

22

IAFOR Journal of Education: Technology in Education

Volume 11 – Issue 2 – 2023

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.

23

IAFOR Journal of Education: Technology in Education

Volume 11 – Issue 2 – 2023

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

IAFOR Journal of Education: Technology in Education

Volume 11 – Issue 2 – 2023

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.

25

IAFOR Journal of Education: Technology in Education

Volume 11 – Issue 2 – 2023

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

26

IAFOR Journal of Education: Technology in Education

Volume 11 – Issue 2 – 2023

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

27

IAFOR Journal of Education: Technology in Education

Volume 11 – Issue 2 – 2023

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

28

IAFOR Journal of Education: Technology in Education

Volume 11 – Issue 2 – 2023

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.

29

IAFOR Journal of Education: Technology in Education

Volume 11 – Issue 2 – 2023

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

30

IAFOR Journal of Education: Technology in Education

Volume 11 – Issue 2 – 2023

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.

Volume 11 – Issue 2 – 2023

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.

Volume 11 – Issue 2 – 2023

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

IAFOR Journal of Education: Technology in Education

Volume 11 – Issue 2 – 2023

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?

Volume 11 – Issue 2 – 2023

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?

Volume 11 – Issue 2 – 2023

“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.

Volume 11 – Issue 2 – 2023

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