ChatGPT, Achievement Motivation, and Problem-Solving: Evidence from Thai and Cambodian Undergraduates
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Background. The rapid global adoption of ChatGPT has raised growing interest in its role in supporting student learning, yet the effectiveness of generative AI tools in enhancing higher-order cognitive skills such as problem-solving may depend on individual learner characteristics that remain underexplored.
Purpose. This quantitative study aimed to examine the relationship between ChatGPT usage and problem-solving ability among students enrolled in a Media Design course, and to test whether this relationship is moderated by achievement motivation.
Method. The study involved 115 university students from four intact classes across two higher education institutions in Thailand and Cambodia. Data were collected through a survey questionnaire and analysed using Partial Least Squares Structural Equation Modeling (PLS-SEM), preceded by a measurement invariance test (MICOM) to assess the appropriateness of pooling data across the two national samples.
Results. The findings indicate that ChatGPT usage was positively and significantly associated with problem-solving ability (? = 0.386, p < 0.001), as was achievement motivation (? = 0.291, p < 0.01). The interaction between ChatGPT usage and achievement motivation also showed a significant positive effect (? = 0.157, p < 0.05), indicating that achievement motivation strengthens the relationship between ChatGPT usage and problem-solving ability. The model explained 44.6% of the variance in problem-solving ability (R² = 0.446).
Conclusion. This study has significant implications for instructional practices in higher education settings, particularly for lecturers designing AI-integrated learning activities. By understanding how achievement motivation shapes students’ benefit from ChatGPT, educators can tailor instructional strategies that support both technology adoption and the development of independent problem solving skills.
Al Issa, H.-E., & Maheshwari, G. (2026). Shaping the future of education: A cluster analysis of generative AI’s transformative impact. Interactive Technology and Smart Education, 23(1), 113–165. https://doi.org/10.1108/ITSE-01-2025-0013
Almailabi, B. S., & Buunk, A. P. (2026). Psychometric validation and cross-language invariance of the Arabic INCOM short form using PLS-SEM. Frontiers in Psychology, 17, 1801103. https://doi.org/10.3389/fpsyg.2026.1801103
Aydin, H., Halpern, C., Arphattananon, T., & Guo, Z. (2026). Can ChatGPT help international students integrate socially and academically in US and Thai universities? A multiple case study approach. International Journal of Comparative Education and Development, 28(1), 36–53. https://doi.org/10.1108/IJCED-02-2025-0014
Banat, A., Wahyuni, E., Erwin, E., Siti Aisyah Hanim, S. A., & Mudinillah, A. (2024). Impact of Using Online Learning Platforms on Student Engagement. Journal Emerging Technologies in Education, 2(1), 110–121. https://doi.org/10.70177/jete.v2i1.800
Be, B., Tep, S., & Long, S. (2026). Determinants of AI Adoption in Higher Education: Evidence from Cambodian University Students. Interdisciplinary Journal of Innovation in Nepalese Academia, 5(1), 1–22. https://doi.org/10.32674/fq333m72
Bin Amatan, M. A., Han, C. G. K., & Pang, V. (2025). PLS-SEM Approach: Validity and Reliability of a Questionnaire on Context, Input, Process and Acceptance of STEM Implementation in Malaysia. Journal of Applied Science, Engineering, Technology, and Education, 7(2), 231–241. https://doi.org/10.35877/454RI.asci3885
Bratu, M. L., Rosca, L. I., & Rosca, N. A. (2025). Machiavellianism, Lying, and Motivation as Predictors of Academic Performance in Romanian Engineering Students. Education Sciences, 15(8), 1028. https://doi.org/10.3390/educsci15081028
Chicco, D., Warrens, M. J., & Jurman, G. (2021). The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Computer Science, 7, e623. https://doi.org/10.7717/peerj-cs.623
Creswell, J. W., & Creswell, J. D. (2017). Research design: Qualitative, quantitative, and mixed methods approaches. Sage publications.
Deng, P., Liu, X., Li, C., Zhu, X., Cui, J., Hua, P., & Chen, G. (2025). Knowledge domain and emerging trends in medication literacy research from 2003 to 2024: A scientometric and bibliometric analysis using CiteSpace and VOSviewer. Frontiers in Public Health, 13, 1598482. https://doi.org/10.3389/fpubh.2025.1598482
Edelsbrunner, P. A., Simonsmeier, B. A., & Schneider, M. (2025). The Reliability, But Not the Cronbach’s Alpha, of Knowledge Tests Matters: Response to Zitzmann and Orona (2025). Educational Psychology Review, 37(2), 48. https://doi.org/10.1007/s10648-025-10023-5
Erwin, Degeng, M. D. K., Degeng, I. N. S., & Praherdhiono, H. (2026). The effectiveness of collaborative blended learning on mastery of instructional media concepts. Multidisciplinary Reviews, 9(12), 2026497. https://doi.org/10.31893/multirev.2026497
Erwin, E., & Dakhalan, A. M. (2024). Ethics of Using Social Media for Learning: Encouraging Appreciation and Responsibility. Scientechno: Journal of Science and Technology, 3(2), 215–232. https://doi.org/10.70177/scientechno.v3i2.1355
Gursoy, D., & Cai, R. (2025). Artificial intelligence: An overview of research trends and future directions. International Journal of Contemporary Hospitality Management, 37(1), 1–17. https://doi.org/10.1108/IJCHM-03-2024-0322
Hair, J., & Alamer, A. (2022). Partial Least Squares Structural Equation Modeling (PLS-SEM) in second language and education research: Guidelines using an applied example. Research Methods in Applied Linguistics, 1(3), 100027. https://doi.org/10.1016/j.rmal.2022.100027
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203
Henseler, J. (2017). Bridging Design and Behavioral Research With Variance-Based Structural Equation Modeling. Journal of Advertising, 46(1), 178–192. https://doi.org/10.1080/00913367.2017.1281780
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(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
Jo, H. (2024). From concerns to benefits: A comprehensive study of ChatGPT usage in education. International Journal of Educational Technology in Higher Education, 21(1), 35. https://doi.org/10.1186/s41239-024-00471-4
Johnson, B. R. (1990). Toward a Multidimensional Model of Entrepreneurship: The Case of Achievement Motivation and the Entrepreneur. Entrepreneurship Theory and Practice, 14(3), 39–54. https://doi.org/10.1177/104225879001400306
Kessler, S. H. (2025). Students and ChatGPT: Insights from the University of Zurich.
Kock, N., & Hadaya, P. (2018). Minimum sample size estimation in PLS?SEM: The inverse square root and gamma?exponential methods. Information Systems Journal, 28(1), 227–261.
Kosar, T., Ostoji?, D., Liu, Y. D., & Mernik, M. (2024). Computer Science Education in ChatGPT Era: Experiences from an Experiment in a Programming Course for Novice Programmers. Mathematics, 12(5), 629. https://doi.org/10.3390/math12050629
Panmei, B. (2025). Generative AI in Multicultural Classrooms: A Narrative Inquiry into International Students’ Experiences. International Journal of Multicultural Education, 27(2).
Pentapati, K. C., Chenna, D., Kumar, V. S., & Kumar, N. (2025). Reliability generalization meta-analysis of Cronbach’s alpha of the oral impacts on daily performance (OIDP) questionnaire. BMC Oral Health, 25(1), 220. https://doi.org/10.1186/s12903-025-05496-3
Qadhi, S. M., Habibi, A., Chaaban, Y., & Khraisheh, M. (2025). Examining factors influencing university students’ perceptions of AI technologies in academic writing. Cogent Education, 12(1), 2587487. https://doi.org/10.1080/2331186X.2025.2587487
Sok, S., & Bunry, R. (2021). Internationalization of higher education in Cambodia: Toward an agenda for higher education development. International Journal of Comparative Education and Development, 23(3), 193–211. https://doi.org/10.1108/IJCED-08-2020-0049
Sok, S., Heng, K., & Pum, M. (2025). Investigating High School Students’ Attitudes Toward the Use of AI in Education: Evidence from Cambodia. Sage Open, 15(3), 21582440251353575. https://doi.org/10.1177/21582440251353575
Thongsri, N., Shen, L., Bao, Y., & Alharbi, I. M. (2018). Integrating UTAUT and UGT to explain behavioural intention to use M-learning: A developing country’s perspective. Journal of Systems and Information Technology, 20(3), 278–297. https://doi.org/10.1108/JSIT-11-2017-0107
Urban, M., D?cht?renko, F., Lukavský, J., Hrabalová, V., Svacha, F., Brom, C., & Urban, K. (2024). ChatGPT improves creative problem-solving performance in university students: An experimental study. Computers & Education, 215, 105031. https://doi.org/10.1016/j.compedu.2024.105031
Ventura-León, J., Lino-Cruz, C., Tocto-Muñoz, S., Sánchez-Villena, A., & Gamboa-Melgar, G. (2025). Problem-solving: Development and validation of a short instrument for higher education. Frontiers in Education, 10, 1555167. https://doi.org/10.3389/feduc.2025.1555167
Wang, X.-M., Wang, J.-L., Xu, S.-Y., & Xu, S.-J. (2025). Concept mapping in STEM education: A meta-analysis of its impact on students’ achievement (2004–2023). International Journal of STEM Education, 12(1), 30. https://doi.org/10.1186/s40594-025-00554-2
Wu, X., Zhu, P., Zhang, J., Yin, M., & Wang, Y. (2026). ChatGPT’s impact on student learning outcomes: A meta-analysis of 35 experimental studies. Humanities and Social Sciences Communications, 13(1), 684. https://doi.org/10.1057/s41599-026-07019-z
Zhu, Y., Liu, Q., & Zhao, L. (2025). Exploring the impact of generative artificial intelligence on students’ learning outcomes: A meta-analysis. Education and Information Technologies, 30(11), 16211–16239. https://doi.org/10.1007/s10639-025-13420-z
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