Motivated by AI? Exploring Students’ Perceptions of ChatGPT and Online Learning Motivation in Higher Education

The growing use of ChatGPT in higher education offers new opportunities to support online learning, yet its relationship with learners’ motivation remains underexplored. This study examined the relationship between ChatGPT usage and online motivation across three dimensions: expectancy, value, and social support. A quantitative survey was conducted among 199 diploma and bachelor’s degree students from three faculties using a five-point Likert-scale questionnaire. The instrument demonstrated excellent reliability (Cronbach’s alpha = .965). Findings showed generally positive perceptions of ChatGPT usage and online motivation. Correlation analysis revealed significant positive relationships between ChatGPT usage and expectancy (r = .465, p < .001), value (r = .512, p < .001), and social support (r = .474, p < .001), with value showing the strongest relationship. The findings suggest that ChatGPT usage is positively associated with learners’ online motivation and highlight the importance of purposeful and responsible AI integration that maintains learner agency, task relevance, critical evaluation, and human interaction in higher education.

perceptions of ChatGPT use; online motivation; expectancy; value; social support; higher education

1. Abbas, M., Jam, F. A., & Khan, T. I. (2024). Is it harmful or helpful? Examining the causes and consequences of generative AI usage among university students. International Journal of Educational Technology in Higher Education, 21, Article 10. https://doi.org/10.1186/s41239-024-00444-7 [Google Scholar] [Crossref]

2. Adam, M. S., Hamid, J. A., Khatibi, A., & Azam, S. M. F. (2025). The impact of community of inquiry presences on student motivation in blended learning: A self-determination theory perspective. Discover Education, 4, Article 186. https://doi.org/10.1007/s44217-025-00612-5 [Google Scholar] [Crossref]

3. Ahmad, N., Alias, F. A., Hamat, M., & Mohamed, S. A. (2024). Reliability analysis: Application of Cronbach’s alpha in research instruments. SIG: e-Learning@CS, 114–119. [Google Scholar] [Crossref]

4. https://appspenang.uitm.edu.my/sigcs/ [Google Scholar] [Crossref]

5. Al-Emran, M., Mezhuyev, V., & Kamaludin, A. (2018). Technology acceptance model in M-learning context: A systematic review. Computers & Education, 125, 389–412. [Google Scholar] [Crossref]

6. https://doi.org/10.1016/j.compedu.2018.06.008 [Google Scholar] [Crossref]

7. Askangela, M. L., Asih, R., & Widodo, E. (2025). The relationship between learners’ perceptions of community of inquiry and their motivation to learn in an online English course. Knowledge Management & E-Learning: An International Journal, 17(2), 278–296. [Google Scholar] [Crossref]

8. https://doi.org/10.34105/j.kmel.2025.17.013 [Google Scholar] [Crossref]

9. 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]

10. Fowler, K. S. (2018). The motivation to learn online questionnaire [Doctoral dissertation, University of Georgia]. University of Georgia Electronic Theses and Dissertations. [Google Scholar] [Crossref]

11. https://getd.libs.uga.edu/pdfs/fowler_kevin_s_201805_phd.pdf [Google Scholar] [Crossref]

12. Garrison, D. R., Anderson, T., & Archer, W. (2000). Critical inquiry in a text-based environment: Computer conferencing in higher education. The Internet and Higher Education, 2(2–3), 87–105. https://doi.org/10.1016/S1096-7516(00)00016-6 [Google Scholar] [Crossref]

13. Granić, A., & Marangunić, N. (2019). Technology acceptance model in educational context: A systematic literature review. British Journal of Educational Technology, 50(5), 2572–2593. [Google Scholar] [Crossref]

14. https://doi.org/10.1111/bjet.12864 [Google Scholar] [Crossref]

15. Habibi, A., Muhaimin, M., Danibao, B. K., Wibowo, Y. G., Wahyuni, S., & Octavia, A. (2023). ChatGPT in higher education learning: Acceptance and use. Computers and Education: Artificial Intelligence, 5, Article 100190. https://doi.org/10.1016/j.caeai.2023.100190 [Google Scholar] [Crossref]

16. Hmoud, M., Swaity, H., Hamad, N., Karram, O., & Daher, W. (2024). Higher education students’ task motivation in the generative artificial intelligence context: The case of ChatGPT. Information, 15(1), Article 33. https://doi.org/10.3390/info15010033 [Google Scholar] [Crossref]

17. Mailizar, M., Burg, D., & Maulina, S. (2021). Examining university students’ behavioural intention to use e-learning during the COVID-19 pandemic: An extended TAM model. Education and Information Technologies, 26(6), 7057–7077. https://doi.org/10.1007/s10639-021-10557-5 [Google Scholar] [Crossref]

18. Mehrvarz, M., Salimi, G., Abdoli, S., & McLaren, B. M. (2025). How do students’ perceptions of ChatGPT shape online learning engagement and performance? Computers and Education: Artificial Intelligence, 9, Article 100459. https://doi.org/10.1016/j.caeai.2025.100459 [Google Scholar] [Crossref]

19. 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, Article 46. https://doi.org/10.1186/s41239-024-00478-x [Google Scholar] [Crossref]

20. Uppal, K., & Hajian, S. (2025). Students’ perceptions of ChatGPT in higher education: A study of academic enhancement, procrastination, and ethical concerns. European Journal of Educational Research, 14(1), 199–211. https://doi.org/10.12973/eu-jer.14.1.199 [Google Scholar] [Crossref]

21. Vetter, T. R. (2017). Descriptive statistics: Reporting the answers to the 5 basic questions of who, what, why, when, where, and a sixth, so what? Anesthesia & Analgesia, 125(5), 1797–1802. [Google Scholar] [Crossref]

22. https://doi.org/10.1213/ANE.0000000000002471 [Google Scholar] [Crossref]

23. Youssef, E., Medhat, M., Abdellatif, S., & Al Malek, M. (2024). Examining the effect of ChatGPT usage on students’ academic learning and achievement: A survey-based study in Ajman, UAE. Computers and Education: Artificial Intelligence, 7, Article 100316. [Google Scholar] [Crossref]

24. https://doi.org/10.1016/j.caeai.2024.100316 [Google Scholar] [Crossref]

25. Zuo, M., Hu, Y., Luo, H., Ouyang, H., & Zhang, Y. (2022). K-12 students’ online learning motivation in China: An integrated model based on community of inquiry and technology acceptance theory. Education and Information Technologies, 27(4), 4599–4620. https://doi.org/10.1007/s10639-021-10791-x [Google Scholar] [Crossref]

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