Exploring the Relationship Between Online Learning Motivation and ChatGPT Usage among University ESL Students

Rapid development of Artificial Intelligence (AI), particularly ChatGPT, has transformed online learning motivation and influenced learners' motivation in higher education. With the increasing reliance on AI-assisted tools for academic support, concerns have emerged regarding their influence on learners’ motivation, engagement and learning experiences. Therefore, this study investigated the influence of ChatGPT on learners’ online motivation to learn especially in terms of expectancy, value and social support. This study employed quantitative survey research involving 137 students from various academic fields and levels of study. The research instrument consisted of three sections containing 53 items adapted from Fowler (2018) and Youssef et al. (2024). The findings revealed that learners perceived ChatGPT positively particularly in enhancing critical thinking, academic achievement and student engagement. Furthermore, significant positive relationships were found between ChatGPT and expectancy, value and social support. The findings suggest that when used appropriately, ChatGPT has the potential to support learners’ motivation and engagement in online learning. This study contributes to the growing body of literature on AI-assisted learning and provides useful implications for educators, institutions and further research in regards to the implementation of generative AI in higher education.

ChatGPT, online motivation, artificial intelligence

1. Afkarin, M. Y., & Asmara, C. H. (2024). Investigating the implementation of ChatGPT in English language education: Effects on student motivation and performance levels. Journal of English Language and Pedagogy, 7(1), 57–66. https://doi.org/10.33503/journey.v7i1.865 [Google Scholar] [Crossref]

2. Ahmad, N., Alias, F. A., Hamat, M., & Mohamed, S. A. (2024). Reliability analysis: Application of Cronbach’s alpha in research instruments. SIG: e-L earning@CS, 114-119. https://appspenang.uitm.edu.my/sigcs/ [Google Scholar] [Crossref]

3. Annamalai, N., Bervell, B., Mireku, D. O., & Andoh, R. P. K. (2025). Artificial intelligence in higher education: Modelling students’ motivation for continuous use of ChatGPT based on a modified self-determination theory. Computers and Education: Artificial Intelligence, 8, 1–13. https://doi.org/10.1016/j.caeai.2024.100346 [Google Scholar] [Crossref]

4. Annamalai, N., & Nasor, M. (2025). Exploring ChatGPT in education: Unveiling learners' experiences through the lens of self-determination theory. Smart Learning Environments, 12(1), 59. https://doi.org/10.1186/s40561-025-00393-2 [Google Scholar] [Crossref]

5. Baidoo-Anu, D., & Owusu Ansah, L. (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. DOI:10.61969/jai.1337500 [Google Scholar] [Crossref]

6. Bhullar, P. S., Joshi, M., & Chugh, R. (2024). ChatGPT in higher education: A synthesis of the literature and a future research agenda. Education and Information Technologies, 29(16), 21501–21522. https://doi.org/10.1007/s10639-024-12723-x [Google Scholar] [Crossref]

7. Chen, K. C., & Jang, S. J. (2010). Motivation in online learning: Testing a model of self-determination theory. Computers in Human Behavior, 26(4), 741–752. https://doi.org/10.1016/j.chb.2010.01.011 [Google Scholar] [Crossref]

8. Deci, E. L., & Ryan, R. M. (2012). Self-determination theory. In P. A. M. Van Lange, A. W. Kruglanski, & E. T. Higgins (Eds.), Handbook of theories of social psychology (Vol. 1, pp. 416–436). SAGE Publications. [Google Scholar] [Crossref]

9. Dewi, N. E., & Ibrahim, S. (2025). The correlation between motivation and student involvement in learning activities using ChatGPT. Jurnal Ilmiah Ilmu Pendidikan dan Sosial, 14(1), 459–473. https://ejournal.unibabwi.ac.id/index.php/sosioedukasi/index [Google Scholar] [Crossref]

10. Fowler, S. (2018). The motivation to learn online questionnaire [Doctor of Philosophy Dissertation, The University of Georgia]. Retrieved from https://getd.libs.uga.edu/pdfs/fowler_kevin_s_201805_phd.pdf [Google Scholar] [Crossref]

11. Galindo-Domínguez, H., Sainz-de-la-Maza, M., Campo, L., & Losada-Iglesias, D. (2026). The influence of learning motivation and procrastination on ChatGPT dependence [Influencia de la motivación hacia el aprendizaje y la procrastinación en la dependencia a ChatGPT]. RIED-Revista Iberoamericana de Educación a Distancia, 29(1), 217–240. https://doi.org/10.5944/ried.45497 [Google Scholar] [Crossref]

12. He, L. (2024). The application of SPSS correlation analysis in the study of precision teaching of English in universities. Applied Mathematics and Nonlinear Science, 9(1), 1–13. http://dx.doi.org/10.2478/amns-2024-1371 [Google Scholar] [Crossref]

13. Lee, Y., & Song, H. D. (2022). Motivation for MOOC learning persistence: An expectancy–value theory perspective. Frontiers in psychology, 13, 958945. [Google Scholar] [Crossref]

14. Li, Y., Sadiq, G., Qambar, G., & Zheng, P. (2025). The impact of students' use of ChatGPT on their research skills: The mediating effects of autonomous motivation, engagement, and self-directed learning. Education and Information Technologies, 30(4), 4185–4216. https://doi.org/10.1007/s10639-024-12981-9 [Google Scholar] [Crossref]

15. Lokman, A., Hassan, F., Ustadi, Y. A., Rahman, F. A. A., Zain, Z. M., & Rahmat, N. H. (2022). Investigating motivation for learning via Vroom’s theory. International Journal of Academic Research in Business and Social Sciences, 12(1), 504–530. [Google Scholar] [Crossref]

16. Mahande, R. D., Fakhri, M. M., Suwahyu, I., & Sulaiman, D. R. A. (2026). Unveiling the impact of ChatGPT: Investigating self-efficacy, anxiety and motivation on student performance in blended learning environments. Journal of Applied Research in Higher Education, 18(1), 282–297. https://doi.org/10.1108/JARHE-07-2024-0372 [Google Scholar] [Crossref]

17. Qu, K., & Wu, X. (2024). ChatGPT as a CALL tool in language education: A study of hedonic motivation adoption models in English learning environments. Education and Information Technologies, 29, 19471–19503. https://doi.org/10.1007/s10639-024-12598-y [Google Scholar] [Crossref]

18. Rahmat, N. H. (2022). Motives to learn English as a foreign language: An analysis from Vroom’s theory. International Journal of Academic Research in Business and Social Sciences, 12(4), 1539–1548. [Google Scholar] [Crossref]

19. Rahmat, N. H. (2025). A comparison of online engagement for undergraduates. International Journal of Research and Innovation in Social Sciences, IX(VI), 3260–3270. https://dx.doi.org/10.47772/IJRISS.2025.906000240 [Google Scholar] [Crossref]

20. Rehman, N., & Kang, M. A. (2024). Exploring the impact of artificial intelligence on students’ engagement and motivation in online learning environments. International Journal of Trends and Innovations in Business & Social Sciences, 2(4), 537–548. https://doi.org/10.48112/tibss.v2i4.959 [Google Scholar] [Crossref]

21. Ryan, R. M., & Deci, E. L. (2020). Intrinsic and extrinsic motivation from a self-determination theory perspective: Definitions, theory, practices, and future directions. Contemporary Educational Psychology, 61, 101860. https://doi.org/10.1016/j.cedpsych.2020.101860 [Google Scholar] [Crossref]

22. Tersta, F. W., Arief, H., Sholiha, M., Syarifuddiin, A., & Risdalina, R. (2026). The use of ChatGPT and student learning motivation: A self-determination theory perspective. Proceedings Academic Universitas Jambi, 2(1), 66–77. https://doi.org/10.22437/proca.v2i1.50581 [Google Scholar] [Crossref]

23. UNESCO. (n.d.). Artificial intelligence in education. United Nations Educational, Scientific and Cultural Organization. https://www.unesco.org/en/digital-education/artificial-intelligence [Google Scholar] [Crossref]

24. 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. https://doi.org/10.1213/ane.0000000000002471 [Google Scholar] [Crossref]

25. Vroom, V. H. (1964). Work and motivation. Wiley. [Google Scholar] [Crossref]

26. Wang, B., Xu, D., & Wang, C. (2025). The role of ChatGPT and Grammarly in promoting emotion regulation, psychological well-being, motivation, and academic writing in Chinese college students: A self-determination theory perspective. Learning and Motivation, 90, 102131. https://doi.org/10.1016/j.lmot.2025.102131 [Google Scholar] [Crossref]

27. Yamaoka, K. (2024). ChatGPT’s motivational effects on Japanese university EFL learners: A qualitative analysis. International Journal of TESOL Studies, 6(3), 24–35. https://doi.org/10.58304/ijts.20240303 [Google Scholar] [Crossref]

28. 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, 100316. https://doi.org/10.1016/j.caeai.2024.100316 [Google Scholar] [Crossref]

29. Zare, J., Al-Issa, A., & Madiseh, F. R. (2025). Interacting with ChatGPT in essay writing: A study of L2 learners’ task motivation. ReCALL, 37(3), 385–402. https://doi.org/10.1017/S0958344025000035 [Google Scholar] [Crossref]

30. Zhang, Z., & Huang, X. (2024). The impact of chatbots based on large language models on second language vocabulary acquisition. Heliyon, 10(3), 1–13. http://dx.doi.org/10.1016/j.heliyon.2024.e25370 [Google Scholar] [Crossref]

31. Zhou, L., & Li, J. (2023). The impact of ChatGPT on learning motivation: A study based on self-determination theory. Education Science and Management, 1(1), 19–29. https://doi.org/10.56578/esm010103 [Google Scholar] [Crossref]

32. Zhou, W. (2024). The impact of ChatGPT on undergraduate student's English learning motivation. Journal of Education, Humanities and Social Sciences, 30, 33–39. https://doi.org/10.54097/9hyk2039 [Google Scholar] [Crossref]

33. Ziegenfuss, J. Y., Casey, A. E., Dempsey, J. M., Johnson, M. M., Knueppel, T. E., & Corcoran, M. (2021). Impact of demographic survey questions on response rate and measurement: A randomized experiment. Survey Practice, 14(1). https://doi.org/10.29115/SP-2021-00 [Google Scholar] [Crossref]

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