AI Use and Learning Outcomes Among Undergraduate Students: The Mediating Role of Learning Motivation, Locus of Control, and Learning Preferences

Achmad Nashrul Waahib; Aminah Aminah; Abdul Sahib; M Syahputra

doi:10.65224/jopate.v2i3.300

Authors

Achmad Nashrul Waahib
acmadnaswahibb11@gmail.com
Universitas KH Abdul Chalim, Indonesia
Aminah Aminah Universitas Syedza Saintika, Indonesia
Abdul Sahib Institut Agama Islam Negeri Curup, Indonesia
M Syahputra Politeknik LP3I Indonesia, Indonesia

Vol. 2 No. 3 (2026)

Articles

Accepted September 15, 2026

Published September 30, 2026

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Background. The growing use of artificial intelligence (AI) in higher education has created new opportunities for personalized learning, although its contribution to student achievement remains theoretically and empirically contested.

Purpose. This study examined the direct and indirect effects of AI use on conceptual understanding and problem-solving ability through learning motivation, locus of control, and learning preferences.

Method. A quantitative explanatory design was employed with 88 pre-service teachers enrolled in an Instructional Media course at a teacher education institution in Central Java, Indonesia. Data were collected using validated questionnaires and cognitive assessments. Preliminary analyses were conducted using SPSS, while the measurement and structural models were evaluated through PLS-SEM with 5,000 bootstrap subsamples.

Novelty. This study simultaneously tested three psychological and learning-related mediators within a single structural model, thereby extending previous research that primarily examined the direct relationship between AI use and academic performance.

Results. AI use significantly predicted learning motivation and locus of control but did not significantly influence learning preferences. Learning motivation significantly predicted learning outcomes and mediated the relationship between AI use and learning outcomes. In contrast, locus of control and learning preferences showed no significant mediating effects. The direct effect of AI use on learning outcomes was also nonsignificant.

Conclusion. AI contributes to student learning primarily by strengthening learning motivation rather than through direct technological exposure. Effective AI integration should therefore emphasize reflective, autonomous, and cognitively engaging learning activities.

Waahib, A. N., Aminah, A., Sahib, A., & Syahputra, M. (2026). AI Use and Learning Outcomes Among Undergraduate Students: The Mediating Role of Learning Motivation, Locus of Control, and Learning Preferences. Journal of Paddisengeng Technology, 2(3), 197–211. https://doi.org/10.65224/jopate.v2i3.300

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Anderson, L. W., & Krathwohl, D. R. (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives: Complete edition. Addison Wesley Longman, Inc.

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

Cindy, A. H., Sugiyono, S., Usman, H., & Herwin, H. (2022). Factors that affect the optimisation of vocational high school facilities and infrastructure. Cypriot Journal of Educational Sciences, 17(2), 586–600. https://doi.org/10.18844/cjes.v17i2.6847

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

Erwin, E., Japakiya, R., & Saputra, D. (2023). Utilization of Kine Master Application as a Learning Video. International Journal of Language and Ubiquitous Learning, 1(1), 1–17. https://doi.org/10.70177/ijlul.v1i1.557

Faza, A., & Lestari, I. A. (2025). Self-Regulated Learning in the Digital Age: A Systematic Review of Strategies, Technologies, Benefits, and Challenges. The International Review of Research in Open and Distributed Learning, 26(2), 23–58. https://doi.org/10.19173/irrodl.v26i2.8119

Giovanelli, E., Perinelli, E., Valzolgher, C., Gessa, E., & Pavani, F. (2026). Self-efficacy and locus of control when facing listening challenges: Validation of the listening challenges attitude scale (LiCAS). International Journal of Listening, 40(2), 110–125.

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

Hermansyah, S., Erwin, E., F, Z., & Zahir, R. (2025). The Development of Social Entrepreneurship in Remote Communities: A Case Study in Sidenreng Rappang. Pengabdian: Jurnal Abdimas, 3(1), 23–33. https://doi.org/10.70177/abdimas.v3i1.2228

Khatun, N. (2021). Applications of Normality Test in Statistical Analysis. Open Journal of Statistics, 11(01), 113–122. https://doi.org/10.4236/ojs.2021.111006

Kong, S. C., Yang, Y., & Hou, C. (2024). Examining teachers’ behavioural intention of using generative artificial intelligence tools for teaching and learning based on the extended technology acceptance model. Computers and Education: Artificial Intelligence, 7, 100328. https://doi.org/10.1016/j.caeai.2024.100328

Kumar, J. A., Salim, Z., Annamalai, N., Dhaliwal, J., & Singh, A. D. (2026). A field in fast-forward: Thematic evolution of generative AI research in higher education through bibliometric analysis. Quality & Quantity. https://doi.org/10.1007/s11135-026-02982-w

Lazou, C., & Tsinakos, A. (2023). Critical Immersive-Triggered Literacy as a Key Component for Inclusive Digital Education. Education Sciences, 13(7), 696. https://doi.org/10.3390/educsci13070696

Lim, W. M., Gunasekara, A., Pallant, J. L., Pallant, J. I., & Pechenkina, E. (2023). Generative AI and the future of education: Ragnarök or reformation? A paradoxical perspective from management educators. The International Journal of Management Education, 21(2), 100790. https://doi.org/10.1016/j.ijme.2023.100790

Monib, W. K., Qazi, A., & Mahmud, M. M. (2025). Exploring learners’ experiences and perceptions of ChatGPT as a learning tool in higher education. Education and Information Technologies, 30(1), 917–939. https://doi.org/10.1007/s10639-024-13065-4

Rahiem, M. D. H. (2026). Generative AI in higher education in Indonesia: Patterns of use and learning impact. Social Sciences & Humanities Open, 13, 102672. https://doi.org/10.1016/j.ssaho.2026.102672

Rahman, Md. M., & Watanobe, Y. (2023). ChatGPT for Education and Research: Opportunities, Threats, and Strategies. Social Sciences. https://doi.org/10.20944/preprints202303.0473.v1

Rina, L., Kaira, L., Mohammed Sultan Saif, G., & Setyaningsih, W. (2024). The Contribution of Artificial Intelligence Technology to the Learning Process of Accounting Students in the Digital Era and Learning Ethics. Advances Educational Innovation, 1(1), 16–25. https://doi.org/10.69725/aei.v1i1.83

Ryan, R. M., & Deci, E. L. (2020a). 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

Ryan, R. M., & Deci, E. L. (2020b). Intrinsic and extrinsic motivation from a self-determination theory perspective: Definitions, theory, practices, and future directions. Contemporary Educational Psychology, 61, 101860.

Sánchez Prieto, J. C., Cruz Benito, J., Therón, R., & García Peñalvo, F. J. (2020). Assessed by Machines: Development of a TAM-Based Tool to Measure AI-based Assessment Acceptance Among Students. International Journal of Interactive Multimedia and Artificial Intelligence, 6(4), 80–86. https://doi.org/10.9781/ijimai.2020.11.009

Singh, B., & Kaur, M. (2024). Changing norms of policies and practices for teacher education in India: Implications and concerns. In Globalisation and teacher education in the BRICS countries (pp. 102–123). Routledge.

Symeou, L., Louca, L., Kavadella, A., Mackay, J., Danidou, Y., & Raffay, V. (2025). Development of Evidence?Based Guidelines for the Integration of Generative AI in University Education Through a Multidisciplinary, Consensus?Based Approach. European Journal of Dental Education, 29(2), 285–303. https://doi.org/10.1111/eje.13069

Talledo Monroy, B., De La Cruz Mendoza, F. A., & Lazo Barreda, D. (2026). Adoption of Artificial Intelligence and Sustainable Learning Outcomes in Engineering Education: Evidence from the Technology Acceptance Model. Sustainability, 18(10), 4673. https://doi.org/10.3390/su18104673

Van der Stede, W. A. (2014). A manipulationist view of causality in cross-sectional survey research. Accounting, Organizations and Society, 39(7), 567–574.

Watson, S. (2025). Emergent discourses on generative AI in education and society.

Yan, L., Greiff, S., Teuber, Z., & Gaševi?, D. (2024). Promises and challenges of generative artificial intelligence for human learning. Nature Human Behaviour, 8(10), 1839–1850.