Understanding ChatGPT Through Two Lenses: Students’ and Lecturers’ Perspectives on Generative AI in Higher Education
DOI:
https://doi.org/10.46328/ijte.7342Keywords:
GenAI, ChatGPT, UTAUT, technology adoption, focus groupsAbstract
The rapid adoption of generative artificial intelligence (GenAI) in higher education has intensified debate regarding its pedagogical and ethical implications. While existing research has largely examined ChatGPT adoption from single-stakeholder perspectives or through quantitative approaches, this study provides a comparative qualitative analysis of both student and lecturer experiences using the Unified Theory of Acceptance and Use of Technology (UTAUT) as a guiding framework. Thematic analysis was conducted on two focus groups, one with graduate students and one with university lecturers, to identify shared patterns and key differences in adoption. The findings reveal distinct engagement logics. Students describe ChatGPT as an embedded academic tool integrated into routine tasks and valued for its efficiency, while also expressing ambivalence, emotional attachment, and emerging reliance. Lecturers, in contrast, frame ChatGPT primarily through pedagogical and ethical considerations, emphasizing assessment redesign, instructional adaptation, and professional responsibility. These results demonstrate that ChatGPT adoption extends beyond functional utility to include affective and relational dimensions. By offering a dual-stakeholder perspective, the study enriches the application of UTAUT in GenAI contexts and provides practical insights for responsible and pedagogically informed integration in higher education.
References
Al-Emran, M., Arpaci, I., & Salloum, S. A. (2020). An empirical examination of continuous intention to use m-learning: An integrated model. Education and Information Technologies, 25(4), 2899–2918. https://doi.org/10.1007/s10639-019-10094-2
Alshammari, S. H., & Alshammari, M. H. (2024). Factors affecting the adoption and use of ChatGPT in higher education. International Journal of Information and Communication Technology Education, 20(1), 1–16. https://doi.org/10.4018/IJICTE.2024010101
Ansari, A. N., Ahmad, S., & Bhutta, S. (2024). Mapping the global evidence around the use of ChatGPT in higher education: A systematic scoping review. Education and Information Technologies. Advance online publication. https://doi.org/10.1007/s10639-024-12987-3
Bahroun, Z., Anane, C., Ahmed, V., & Zacca, A. (2023). Transforming education: A comprehensive review of generative artificial intelligence in educational settings through bibliometric and content analysis. Sustainability, 15(17), 12983. https://doi.org/10.3390/su151712983
Baidoo-Anu, D., & Ansah, L. O. (2023). Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning. Journal of AI, 7(1), 52–62. https://doi.org/10.61969/jai.1337500
Bervell, B., & Arkorful, V. (2020). LMS-enabled blended learning utilization in distance tertiary education: Establishing the relationships among facilitating conditions, voluntariness of use, and use behaviour. International Journal of Educational Technology in Higher Education, 17(1), 6. https://doi.org/10.1186/s41239-020-0183-9
Blumer, H. (1954). What is wrong with social theory? American Sociological Review, 19(1), 3–10. https://doi.org/10.2307/2088165
Bouteraa, M., Bin Nashwan, S. A., Al-Daihami, M., Dirie, K. A., Benlahcene, A., & Sadallah, M. (2024). Understanding the diffusion of AI-generative (ChatGPT) in higher education: Does students’ integrity matter? Computers in Human Behavior Reports, 14, Article 100402. https://doi.org/10.1016/j.chbr.2024.100402
Budhathoki, T., Zirar, A., Njoya, E. T., & Timsina, A. (2024). ChatGPT adoption and anxiety: A cross-country analysis utilising the unified theory of acceptance and use of technology (UTAUT). Studies in Higher Education, 49(5), 1–16. https://doi.org/10.1080/03075079.2024.2398143
Cambra-Fierro, J. J., Blasco, M. F., López-Pérez, M.-E., & Trifu, A. (2025). ChatGPT adoption and its influence on faculty well-being: An empirical research in higher education. Education and Information Technologies, 30(2), 1517–1538. https://doi.org/10.1007/s10639-024-12871-0
Camilleri, M. A. (2024). Factors affecting performance expectancy and intentions to use ChatGPT: Using SmartPLS to advance an information technology acceptance framework. Technological Forecasting and Social Change, 201, 123247. https://doi.org/10.1016/j.techfore.2023.123247
Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20, Article 43. https://doi.org/10.1186/s41239-023-00411-8
Chaudhry, I. S., Sarwary, S. A. M., El Refae, G. A., & Chabchoub, H. (2023). Time to revisit existing student’s performance evaluation approach in higher education sector in a new era of ChatGPT: A case study. Cogent Education, 10(1), Article 2210461. https://doi.org/10.1080/2331186X.2023.2210461
Chávez Herting, D., Pros, C. R., & Castelló Tarrida, A. (2023). Habit and social influence as determinants of PowerPoint use in higher education: A study from a technology acceptance approach. Interactive Learning Environments, 31(1), 497–513. https://doi.org/10.1080/10494820.2020.1801227
Chiu, T. K. (2023). The impact of generative AI (GenAI) on practices, policies, and research direction in education: A case of ChatGPT and Midjourney. Interactive Learning Environments, 1–17. https://doi.org/10.1080/10494820.2023.2253861
Collins, J. W., & O’Brien, N. P. (2003). The Greenwood dictionary of education. Greenwood Press.
Cooper, G. (2023). Examining science education in ChatGPT: An exploratory study of generative artificial intelligence. Journal of Science Education and Technology, 32(2), 444–452. https://doi.org/10.1007/s10956-023-10064-8
Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approaches (4th ed.). SAGE Publications.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003. https://doi.org/10.1287/mnsc.35.8.982
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., & Crick, T. (2021). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice, and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2019). Re-examining the unified theory of acceptance and use of technology (UTAUT): Towards a revised theoretical model. Information Systems Frontiers, 21(3), 719–734. https://doi.org/10.1007/s10796-017-9774-y
Elnaem, M. H., Okuyan, B., Mubarak, N., Thabit, A. K., AbouKhatwa, M. M., Ramatillah, D. L., Isah, A., Al-Jumaili, A. A., & Nazar, N. I. M. (2025). Students’ acceptance and use of generative AI in pharmacy education: International cross-sectional survey based on the extended unified theory of acceptance and use of technology. International Journal of Clinical Pharmacy. Advance online publication. https://doi.org/10.1007/s11096-025-01936-w
Gazit, T., Eitan, T., Gal, L. & Gradovitch, N. (2026). Adoption of Generative AI Technologies: Insights From the UTAUT2 Model, Personality Characteristics, and Behavioural Factors. Journal of Computer Assisted Learning 42(1). e70162. https://doi.org/10.1002/jcal.70162.
Granić, A., & Marangunić, N. (2019). Technology acceptance model in educational context: A systematic literature review. British Journal of Educational Technology, 50(5), 2572–2593. https://doi.org/10.1111/bjet.12864
Gupta, M., Akiri, C., Aryal, K., Parker, E., & Praharaj, L. (2023). From ChatGPT to ThreatGPT: Impact of generative AI in cybersecurity and privacy. IEEE Access: Practical Innovations, Open Solutions, 11, 145321–145334. https://doi.org/10.1109/ACCESS.2023.3312518
Gursoy, D., Chi, O. H., Lu, L., & Nunkoo, R. (2019). Consumers’ acceptance of artificially intelligent (AI) device use in service delivery. International Journal of Information Management, 49, 157–169. https://doi.org/10.1016/j.ijinfomgt.2019.03.008
Hammarberg, K., Kirkman, M., & de Lacey, S. (2016). Qualitative research methods: When to use them and how to judge them. Human Reproduction, 31(3), 498–501. https://doi.org/10.1093/humrep/dev334
Hayes, A. F., & Krippendorff, K. (2007). Answering the call for a standard reliability measure for coding data. Communication Methods and Measures, 1(1), 77–89. https://doi.org/10.1080/19312450709336664
Holmes, W., & Miao, F. (2023). Guidance for generative AI in education and research. UNESCO Publishing.
Hou, L., & Li, Z. (2023). ChatGPT legal challenges and institutional responses in academic use. Journal of Northeast Normal University (Philosophy and Social Science Edition), 324, 29–39. https://doi.org/10.16164/j.cnki.22-1062/c.2023.04.004
Ilieva, G., Yankova, T., Klisarova-Belcheva, S., Dimitrov, A., Bratkov, M., & Angelov, D. (2023). Effects of generative chatbots in higher education. Information, 14(9), 492. https://doi.org/10.3390/info14090492
Johnston, H., Wells, R. F., & Shanks, E. M. (2024). Student perspectives on the use of generative artificial intelligence technologies in higher education. International Journal for Educational Integrity, 20(2), Article 2. https://doi.org/10.1007/s40979-024-00149-4
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., et al. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274
Korneeva, E., Oliver, T., Teubner, T., & Antons, D. (2023). Tracing the legitimacy of artificial intelligence: A longitudinal analysis of media discourse. Technological Forecasting and Social Change, 192, 122467. https://doi.org/10.1016/j.techfore.2023.122467
Krueger, R. A., & Casey, M. A. (2000). Focus groups: A practical guide for applied research (3rd ed.). SAGE Publications.
Law, L. (2024). Application of generative artificial intelligence (GenAI) in language teaching and learning: A scoping literature review. Computers and Education Open, 5, Article 100174. https://doi.org/10.1016/j.caeo.2024.100174
Leavy, P. (2022). Research design: Quantitative, qualitative, mixed methods, arts-based, and community-based participatory research approaches. The Guilford Press.
Leitão, B. J., & Vergueiro, W. (2000). Using the focus group approach for evaluating customers’ opinions: The experience of a Brazilian academic library. New Library World, 101(2), 60–65. https://doi.org/10.1108/03074800010308640
Limna, P., Kraiwanit, T., Jangjarat, K., Klayklung, P., & Chocksathaporn, P. (2023). The use of ChatGPT in the digital era: Perspectives on chatbot implementation. Journal of Applied Learning and Teaching, 6(1), 64–74. https://doi.org/10.37074/jalt.2023.6.1.32
Lin, M. P.-C., Chang, D. H., & Winne, P. H. (2024). A proposed methodology for investigating student–chatbot interaction patterns in giving peer feedback. Educational Technology Research and Development. Advance online publication. https://doi.org/10.1007/s11423-024-10408-3
Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. SAGE Publications.
MAXQDA. (2024). All-in-one tool for qualitative data analysis. VERBI Software. https://www.maxqda.com/
Noy, S., & Zhang, W. (2023). Experimental evidence on the productivity effects of generative artificial intelligence. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4375283
Onwuegbuzie, A. J., & Frels, R. K. (2015). A framework for conducting critical dialectical pluralist focus group discussions using mixed research techniques. Journal of Educational Issues, 1(2), 159–177. https://doi.org/10.5296/jei.v1i2.8246
Prince, M., & Davies, M. (2001). Moderator teams: An extension to focus group methodology. Qualitative Market Research: An International Journal, 4(4), 207–216. https://doi.org/10.1108/EUM0000000005907
Qadir, J. (2023). Engineering education in the era of ChatGPT: Promise and pitfalls of generative AI for education. In Proceedings of the IEEE Global Engineering Education Conference (EDUCON 2023) (pp. 1–6). IEEE. https://doi.org/10.1109/EDUCON57172.2023.10117784
Ravšelj, D., Keržič, D., Tomaževič, N., Umek, L., Brezovar, N., Iahad, N. A., et al. (2025). Higher education students’ perceptions of ChatGPT: A global study of early reactions. PLOS ONE, 20(2), e0315011. https://doi.org/10.1371/journal.pone.0315011
Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.
Tian, W., Ge, J., Zhao, Y., & Zheng, X. (2024). AI chatbots in Chinese higher education: Adoption, perception, and influence among graduate students. An integrated analysis utilizing UTAUT and ECM models. Frontiers in Psychology, 15, 1268549. https://doi.org/10.3389/fpsyg.2024.1268549
Twinn, S. (1998). An analysis of the effectiveness of focus groups as a method of qualitative data collection with Chinese populations in nursing research. Journal of Advanced Nursing, 28(3), 654–661. https://doi.org/10.1046/j.1365-2648.1998.00708.x
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
Yilmaz, F. G. K., Yilmaz, R., & Ceylan, M. (2023). Generative artificial intelligence acceptance scale: A validity and reliability study. International Journal of Human–Computer Interaction, 39(14), 1–13. https://doi.org/10.1080/10447318.2023.2291585
Yilmaz, R., & Yilmaz, F. G. K. (2023). Augmented intelligence in programming learning: Examining student views on the use of ChatGPT for programming learning. Computers in Human Behavior: Artificial Humans, 1(2), 100005. https://doi.org/10.1016/j.chbah.2023.100005
Yousaf, A., Mishra, A., & Gupta, A. (2021). From technology adoption to consumption: Effect of pre-adoption expectations from fitness applications on usage satisfaction, continual usage, and health satisfaction. Journal of Retailing and Consumer Services, 62, 102655. https://doi.org/10.1016/j.jretconser.2021.102655
Zhang, H., Liu, C., Wang, D., & Zhao, Z. (2023). Research on factors influencing user intention to use ChatGPT. Information Theory and Practice, 4(4), 15–22. https://doi.org/10.16353/j.cnki.1000-7490.2023.04.003
Downloads
Published
Issue
Section
License
Copyright (c) 2026 International Journal of Technology in Education

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Articles may be used for research, teaching, and private study purposes. Authors alone are responsible for the contents of their articles. The journal owns the copyright of the articles. The publisher shall not be liable for any loss, actions, claims, proceedings, demand, or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of the research material.
The author(s) of a manuscript agree that if the manuscript is accepted for publication in the International Journal of Technology in Education (IJTE), the published article will be copyrighted using a Creative Commons “Attribution 4.0 International” license. This license allows others to freely copy, distribute, and display the copyrighted work, and derivative works based upon it, under certain specified conditions.
Authors are responsible for obtaining written permission to include any images or artwork for which they do not hold copyright in their articles, or to adapt any such images or artwork for inclusion in their articles. The copyright holder must be made explicitly aware that the image(s) or artwork will be made freely available online as part of the article under a Creative Commons “Attribution 4.0 International” license.

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
