Understanding ChatGPT Through Two Lenses: Students’ and Lecturers’ Perspectives on Generative AI in Higher Education

Authors

DOI:

https://doi.org/10.46328/ijte.7342

Keywords:

GenAI, ChatGPT, UTAUT, technology adoption, focus groups

Abstract

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

2026-06-18

Issue

Section

Articles

How to Cite

Understanding ChatGPT Through Two Lenses: Students’ and Lecturers’ Perspectives on Generative AI in Higher Education . (2026). International Journal of Technology in Education, 9(3), 745-766. https://doi.org/10.46328/ijte.7342