AI Acceptance and Learning Discipline in Vocational Mathematics Education
DOI:
https://doi.org/10.30998/formatif.v16i2.3546Keywords:
artificial intelligence in mathematics education, technology acceptance model;, self-regulated learning, learning disciplineAbstract
This study examines the relationships among students’ acceptance of artificial intelligence (AI) for mathematics learning, self-regulated learning (SRL), and learning discipline. The proposed model extends a partial Technology Acceptance Model (TAM) by incorporating perceived ease of use (PEOU), perceived usefulness (PU), attitude toward use (ATU), and behavioural intention (BI), together with SRL and learning discipline. A quantitative cross-sectional survey was conducted with 206 vocational high school students (Sekolah Menengah Kejuruan/SMK) from DKI Jakarta, West Java, Central Java, and East Java, Indonesia. Data were analysed using partial least squares structural equation modelling (PLS-SEM). The results supported the TAM sequence: PEOU was positively associated with PU (β = .743, p < .001), PU with ATU (β = .738, p < .001), and ATU with BI (β = .851, p < .001). PU was positively associated with SRL (β = .393, p < .001), whereas BI was not significantly associated with SRL (β = .131, p = .195). SRL was positively associated with learning discipline (β = .593, p < .001; f² = .494). The model explained 24.1% of the variance in SRL and 45.2% of the variance in learning discipline. Mediation analysis showed a significant indirect association between PU and learning discipline through SRL (β = .233, 95% CI [.115, .348], p < .001), while the direct association between PU and learning discipline remained significant (β = .140, p = .030), indicating partial mediation. These findings suggest that perceived usefulness may be more educationally relevant than behavioural intention for understanding SRL and learning discipline in AI-supported mathematics learning.
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References
Bastani, H., Bastani, O., Sungu, A., Ge, H., Kabakcı, Ö., & Mariman, R. (2025). Generative AI without guardrails can harm learning: Evidence from high school mathematics. Proceedings of the National Academy of Sciences, 122(26), e2422633122. https://doi.org/10.1073/pnas.2422633122
Crompton, H. & Burke, D. (2023). Artificial intelligence in higher education: The state of the field. International Journal of Educational Technology in Higher Education, 20, 22. https://doi.org/10.1186/s41239-023-00392-8
Dai, W., Li, Z., & Jia, N. (2022). Self-regulated learning, online mathematics learning engagement, and perceived academic control among Chinese junior high school students during the COVID-19 pandemic: A latent profile analysis and mediation analysis. Frontiers in Psychology, 13, 1042843. https://doi.org/10.3389/fpsyg.2022.1042843
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
Gabriel, F., Kennedy, J. P., Marrone, R., & Leonard, S. (2025). Pragmatic AI in education and its role in mathematics learning and teaching. npj Science of Learning, 10, 26. https://doi.org/10.1038/s41539-025-00315-4
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., 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, 115–135. https://doi.org/10.1007/s11747-014-0403-8
Järvelä, S., Nguyen, A., & Hadwin, A. F. (2023). Human and artificial intelligence collaboration for socially shared regulation in learning. British Journal of Educational Technology, 54(5), 1057–1076. https://doi.org/10.1111/bjet.13325
Jin, S. H., Im, K., Yoo, M., Roll, I., & Seo, K. (2023). Supporting students’ self-regulated learning in online learning using artificial intelligence applications. International Journal of Educational Technology in Higher Education, 20, 37. https://doi.org/10.1186/s41239-023-00406-5
Kuo, B.-C., Bai, Z.-E., & Lin, C.-H. (2026). Developing an AI learning companion for mathematics problem-solving in elementary schools. Computers & Education, 240, 105463. https://doi.org/10.1016/j.compedu.2025.105463
Lan, M. & Zhou, X. (2025). A qualitative systematic review on AI empowered self-regulated learning in higher education. npj Science of Learning, 10, 21. https://doi.org/10.1038/s41539-025-00319-0
Nitzl, C., Roldán, J. L., & Cepeda, G. (2016). Mediation analysis in partial least squares path modeling. Industrial Management & Data Systems, 116(9), 1849–1864. https://doi.org/10.1108/IMDS-07-2015-0302
Ouyang, F., Zheng, L., & Jiao, P. (2022). Artificial intelligence in online higher education: A systematic review of empirical research from 2011–2020. Education and Information Technologies, 27, 7893–7925. https://doi.org/10.1007/s10639-022-10925-9
Venkatesh, V. & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926
Zimmerman, B. J. (1989). A social cognitive view of self-regulated academic learning. Journal of Educational Psychology, 81(3), 329–339. https://doi.org/10.1037/0022-0663.81.3.329
Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64–70. https://doi.org/10.1207/S15430421TIP4102_2
Zhu, H., Sun, Y., & Yang, J. (2025). Towards responsible artificial intelligence in education: A systematic review on identifying and mitigating ethical risks. Humanities and Social Sciences Communications, 12, 1111. https://doi.org/10.1057/s41599-025-05252-6
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Copyright (c) 2026 Andi Fajeriani Wyrasti, Leonard Leonard, Kristiani Kristiani, Nuryanti Rumalolas, Riki Suliana Ranggawati Sidik (Author)

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