Explaining Students' Intention to Use AI-Enhanced E-Learning in Tunisian Higher Education: An Integrative Acceptance Model

Purpose. This study explores what shapes Tunisian university students' intention to use e-learning environments that incorporate artificial intelligence (AI). It brings together selected beliefs from the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT), alongside an exploratory measure of how students perceive AI-related features.
Design/methodology/approach. The analysis draws on 200 complete responses from a cross-sectional online convenience survey, as documented in the available analytical summary. Composite-score diagnostics and multiple regression were used, together with a previously reported bootstrap estimate of an indirect association. The outcome is students' stated intention to use these environments, not observed use.
Findings. Together, the five predictors explain 57.1% of the observed variance in behavioural intention. The strongest unique association is with AI-related acceptability (β = 0.508), followed by perceived usefulness (β = 0.289) and social influence (β = 0.184). Neither perceived ease of use nor facilitating conditions has a statistically significant unique direct association. The reported indirect estimate linking ease of use to intention through AI-related acceptability is positive (0.277; 95% bootstrap CI [0.184, 0.374]), although the full resampling output is unavailable.
Research limitations/implications. The survey relied on a convenience sample, was conducted at one point in time and used a single self-report questionnaire. It therefore cannot support causal claims or population-wide generalisation. Moreover, the AI-related acceptability score combines six different facets, each measured by one item; it should not be treated as a validated multidimensional scale. Privacy concerns were not measured separately, respondents were not asked about one common AI platform, and some diagnostic and mediation details cannot be independently checked.
Practical implications. Universities should make the educational benefits of AI-supported learning visible to students, explain how AI features and data practices work, and provide accessible technical and human support. These suggestions are informed by the wider literature rather than tested intervention effects in this survey.
Originality/value. The findings suggest that students' overall appraisal of AI-enabled features deserves attention alongside established TAM and UTAUT beliefs. By focusing on Tunisian higher education while acknowledging the limits of its exploratory measure, the article offers a basis for more precise measurement and longitudinal investigation.

AI-enhanced e-learning; behavioural intention; AI acceptability; technology acceptance; TAM; UTAUT; higher education; Tunisia

1. Al-Adwan, A. S. (2020). Investigating the drivers and barriers to MOOCs adoption: The perspective of the technology acceptance model. Education and Information Technologies, 25(6), 5771–5795. https://doi.org/10.1007/s10639-020-10250-z [Google Scholar] [Crossref]

2. Al-Zahrani, A. M., & Alasmari, T. M. (2024). Exploring the impact of artificial intelligence on higher education: The dynamics of ethical, social, and educational implications. Humanities and Social Sciences Communications, 11(1), 1–12. https://doi.org/10.1057/s41599-024-03432-4 [Google Scholar] [Crossref]

3. Almassaad, A., Alajlan, H., & Alebaikan, R. (2024). Student perceptions of generative artificial intelligence: Investigating utilization, benefits, and challenges in higher education. Systems, 12(10), 385. https://doi.org/10.3390/systems12100385 [Google Scholar] [Crossref]

4. Barz, N., Benick, M., Dörrenbächer-Ulrich, L., & Perels, F. (2024). Students' acceptance of e-learning: Extending the technology acceptance model with self-regulated learning and affinity for technology. Discover Education, 3, Article 114. https://doi.org/10.1007/s44217-024-00195-7 [Google Scholar] [Crossref]

5. 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, 43. https://doi.org/10.1186/s41239-023-00411-8 [Google Scholar] [Crossref]

6. Cheng, X., Lin, X., Shen, X. L., Zarifis, A., & Mou, J. (2022). The dark sides of artificial intelligence. Electronic Markets, 32, 1–5. https://doi.org/10.1007/s12525-022-00531-5 [Google Scholar] [Crossref]

7. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008 [Google Scholar] [Crossref]

8. Erragcha, N., Babay, H., Bchir, S., & Saidi, S. (2022). Impact of the COVID-19 pandemic on perceptions and behaviors of university students in Tunisia. E-Learning and Digital Media, 19(5), 456–474. https://doi.org/10.1177/20427530221108462 [Google Scholar] [Crossref]

9. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104 [Google Scholar] [Crossref]

10. Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51–90. https://doi.org/10.2307/30036519 [Google Scholar] [Crossref]

11. Gerlich, M. (2023). Perceptions and acceptance of artificial intelligence: A multidimensional study. Social Sciences, 12(9), 502. https://doi.org/10.3390/socsci12090502 [Google Scholar] [Crossref]

12. Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43, 115–135. https://doi.org/10.1007/s11747-014-0403-8 [Google Scholar] [Crossref]

13. Ministry of Higher Education and Scientific Research. (2026). Higher education and scientific research in figures: Academic year 2024–2025 [Statistical bulletin]. https://www.mes.tn/image.php?id=20718 [Google Scholar] [Crossref]

14. Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2), 1–21. https://doi.org/10.1177/2053951716679679 [Google Scholar] [Crossref]

15. Naidoo, D. T. (2023). Integrating TAM and IS success model: Exploring the role of blockchain and AI in predicting learner engagement and performance in e-learning. Frontiers in Computer Science, 5, Article 1227749. https://doi.org/10.3389/fcomp.2023.1227749 [Google Scholar] [Crossref]

16. Niu, B., & Mvondo, G. F. N. (2024). I Am ChatGPT, the ultimate AI Chatbot! Investigating the determinants of users' loyalty and ethical usage concerns of ChatGPT. Journal of Retailing and Consumer Services, 76, 103562. https://doi.org/10.1016/j.jretconser.2023.103562 [Google Scholar] [Crossref]

17. Organisation for Economic Co-operation and Development. (2024). Digital education outlook 2024: Pushing the frontiers with AI and data. OECD Publishing. https://doi.org/10.1787/38b9f525-en [Google Scholar] [Crossref]

18. Pieters, W. (2011). Explanation and trust: What to tell the user in security and AI? Ethics and Information Technology, 13, 53–64. https://doi.org/10.1007/s10676-010-9253-3 [Google Scholar] [Crossref]

19. Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879 [Google Scholar] [Crossref]

20. Sghari, A., & Bouaziz, F. (2022). The Moodle distance learning platform usage by Tunisian university teachers under the COVID-19 context. International Journal of Technology and Human Interaction, 18(1), 1–19. https://doi.org/10.4018/IJTHI.297621 [Google Scholar] [Crossref]

21. Shahzad, M. F., Xu, S., & Javed, I. (2024). ChatGPT awareness, acceptance, and adoption in higher education: The role of trust as a cornerstone. International Journal of Educational Technology in Higher Education, 21, 46. https://doi.org/10.1186/s41239-024-00478-x [Google Scholar] [Crossref]

22. Shin, D. (2021). The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable AI. International Journal of Human–Computer Studies, 146, 102551. https://doi.org/10.1016/j.ijhcs.2020.102551 [Google Scholar] [Crossref]

23. Université Virtuelle de Tunis. (2024). L’UVT en chiffres 2023–2024. https://www.uvt.rnu.tn/component/content/article/1015-l-uvt-en-chiffres-2023-2024?catid=12 [Google Scholar] [Crossref]

24. Université Virtuelle de Tunis. (n.d.). Héberger son cours à l’UVT. https://www.uvt.rnu.tn/enseignant/heberger-son-cours [Google Scholar] [Crossref]

25. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926 [Google Scholar] [Crossref]

26. 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 [Google Scholar] [Crossref]

27. Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178. https://doi.org/10.2307/41410412 [Google Scholar] [Crossref]

28. Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – Where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 1–27. https://doi.org/10.1186/s41239-019-0171-0 [Google Scholar] [Crossref]

29. Zheng, H., Han, F., Huang, Y., Wu, Y., & Wu, X. (2025). Factors influencing behavioral intention to use e-learning in higher education during the COVID-19 pandemic: A meta-analytic review based on the UTAUT2 model. Education and Information Technologies, 30(9), 12015–12053. https://doi.org/10.1007/s10639-024-13299-2 [Google Scholar] [Crossref]

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