Exploring the Role of Task Value in Online Learning Motivation and ChatGPT Use
Authors
Academy of Language Studies, Universiti Teknologi MARA (Malaysia)
Academy of Language Studies, Universiti Teknologi MARA (Malaysia)
Faculty of Mechanical Engineering, Universiti Teknologi MARA (Malaysia)
Academy of Language Studies, Universiti Teknologi MARA (Malaysia)
Academy of Language Studies, Universiti Teknologi MARA (Malaysia)
Article Information
DOI: 10.47772/IJRISS.2026.100800758
Subject Category: Education
Volume/Issue: 10/8 | Page No: 11172-11193
Publication Timeline
Submitted: 2026-08-31
Accepted: 2026-09-05
Published: 2026-09-17
Abstract
The expansion of online education and generative artificial intelligence (GenAI) has created new opportunities for university students to engage with learning in different ways. Among these developments, ChatGPT has become increasingly relevant as a tool for academic support, although its use is closely connected to how students perceive and engage with their learning. This study aimed to investigate the relationships between task value, online learning motivation, and ChatGPT use among university students. A quantitative approach was adopted, using an online questionnaire comprising four sections covering demographic profile, task value, online learning motivation, and ChatGPT use. Data were collected from undergraduate students at a public university in Malaysia. The findings showed that students generally placed importance on their course learning, while task value was strongly associated with both online learning motivation and ChatGPT use. Students also demonstrated a tendency to evaluate ChatGPT-generated responses critically by checking information and seeking alternative sources. In addition, instructor support emerged as an important aspect of the online learning experience, particularly the provision of clear and timely responses. These findings indicate that students’ perceptions of learning value, critical engagement with AI-generated content, and continued instructor involvement are important considerations in online learning. The study highlights the need for purposeful integration of ChatGPT that complements, rather than replaces, instructional support and students’ active learning.
Keywords
Task value, Online learning motivation, ChatGPT, Critical thinking, AI-supported learning
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References
1. 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. https://appspenang.uitm.edu.my/sigcs/ [Google Scholar] [Crossref]
2. Albadarin, Y., Saqr, M., Pope, N., & Tukiainen, M. (2024). A systematic literature review of empirical research on ChatGPT in education. Discover Education, 3(1), 60. https://doi.org/10.1007/s44217-024-00138-2 [Google Scholar] [Crossref]
3. Chan, C. K. Y., & Zhou, W. (2023). An expectancy value theory (EVT) based instrument for measuring student perceptions of generative AI. Smart Learning Environments, 10, Article 64. https://doi.org/10.1186/s40561-023-00284- [Google Scholar] [Crossref]
4. Dwivedi, Y. K., Kshetri, N., Hughes, L., et al. (2023). So what if ChatGPT wrote it? Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642 [Google Scholar] [Crossref]
5. Eccles, J. S., Adler, T. F., Futterman, R., Goff, S. B., Kaczala, C. M., Meece, J. L., & Midgley, C. (1983). Expectancies, values, and academic behaviours. In J. T. Spence (Ed.), Achievement and Achievement Motivation (pp. 75–146). San Francisco: W. H. Freeman. [Google Scholar] [Crossref]
6. Eccles, J. S., & Wigfield, A. (2020). From expectancy-value theory to situated expectancy-value theory: A developmental, social cognitive, and sociocultural perspective on motivation. Contemporary Educational Psychology, 61, 101859. https://doi.org/10.1016/j.cedpsych.2020.101859 [Google Scholar] [Crossref]
7. Elshaer, I. A., Hasanein, A. M., & Sobaih, A. E. E. (2024). The moderating effects of gender and study discipline in the relationship between university students’ acceptance and use of ChatGPT. European Journal of Investigation in Health, Psychology and Education, 14(7), 1981–1995. https://doi.org/10.3390/ejihpe14070132 [Google Scholar] [Crossref]
8. Fowler, S. (2018) The Motivation to learn Online Questionnaire. Doctor of Philosophy Dissertation. Graduate Faculty, The University of Georgia. Retrieved from https://getd.libs.uga.edu/pdfs/fowler_kevin_s_201805_phd.pdf [Google Scholar] [Crossref]
9. Hartnett, M. (2016). Motivation in Online Education. Springer. https://doi.org/10.1007/978-981-10-0700-2 [Google Scholar] [Crossref]
10. He, L. (2024) The Application of SPSS Correlation Analysis in the Study if 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]
11. Huda, N., Anwar, K., Novferma, & Kurniawan, W. (2025). ChatGPT scaffolding in supporting metacognition for limit concepts in guided inquiry mathematics learning. Journal of Information Technology Education: Research, 24, 38–62. https://doi.org/10.28945/5645 [Google Scholar] [Crossref]
12. Izni, N. A., Aksah, S. J., Mohd Aslam, S. N. A., & Mohammed, M. N. (2024). Motivation to learn online: Analysis of the relation between expectancy, value and social support in Malaysian higher education. ESTEEM Journal of Social Sciences and Humanities, 8(1), 174–193. [Google Scholar] [Crossref]
13. Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., 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 [Google Scholar] [Crossref]
14. Lee, Y., & Song, H.-D. (2022). Motivation for MOOC learning persistence: An expectancy-value theory perspective. Frontiers in Psychology, 13, 958945. https://doi.org/10.3389/fpsyg.2022.95894 [Google Scholar] [Crossref]
15. Putri, N. M. A. A., & Djuwari, D. (2026). ChatGPT as a scaffolding in English writing of EFL students: Literature review. Esteem Journal of English Education Study Programme, 9 (1), 735–743. https://jurnal.univpgri-palembang.ac.id/index.php/esteem/article/view/21524/10535 [Google Scholar] [Crossref]
16. Rahmat, N.H. (2026) Exploring the Relationship between Online Reading Strategies and ChatGPT Usage among Undergraduates. Quantum Journal of Social Science and Humanities, 7(1), 561-574. https://doi.org/10.55197/qjssh.v7i1.1071 [Google Scholar] [Crossref]
17. Teng, M. F. (2024). “ChatGPT is the companion, not enemies”: EFL learners' perceptions and experiences in using ChatGPT for feedback in writing. Computers and Education: Artificial Intelligence, 6, 100210. https://doi.org/10.1016/j.caeai.2024.100270 [Google Scholar] [Crossref]
18. Teng, M. F. (2024). Integrating ChatGPT into teacher feedback: Practical insights for L2 writing instruction. TESL-EJ, 28(3), 1–22. [Google Scholar] [Crossref]
19. 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? Anesth Analg, 12595), 1797-1802. https://doi.org/10.1213/ane.0000000000002471 [Google Scholar] [Crossref]
20. Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press. [Google Scholar] [Crossref]
21. Wigfield, A., & Eccles, J. S. (2000). Expectancy–value theory of achievement motivation. Contemporary Educational Psychology, 25(1), 68-81. https://doi.org/10.1006/ceps.1999.1015 [Google Scholar] [Crossref]
22. 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(Dec 2024), 100316. https://doi.org/10.1016/j.caeai.2024.100316 [Google Scholar] [Crossref]
23. Youssef, A., et al. (2024). Exploring ChatGPT and the thinking process in writing among university undergraduates. International Journal of Research and Innovation in Social Science, 8(6), 16261–16277. [Google Scholar] [Crossref]
24. Ziegenfuss, J. Y., Casey A. E., Jennifer M. D., Meghan M. J., Thomas E. K, and Marna, C. (2021) Impact of Demographic Survey Questions on Response Rate and Measurement: A Randomized Experiment. Survey Practice 14 (1), https://doi.org/10.29115/SP-2021-0010 [Google Scholar] [Crossref]
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