The Effect of AI Usage Strategy and Self-Regulated Learning on Students' Academic Integrity: The Moderating Role of AI Ethics Perception
Keywords:
academic integrity, AI ethics perception, AI usage strategy, self-regulated learning
Abstract
The emergence of generative artificial intelligence (AI) poses new challenges to students' academic integrity. This study aims to analyze the effect of AI usage strategy and self-regulated learning (SRL) on academic integrity, and to examine the moderating role of AI ethics perception. This quantitative study employed a survey design involving 158 active students of the Accounting Education Study Program at Universitas Negeri Surabaya, selected through purposive sampling. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results show that AI usage strategy and SRL have a positive and significant effect on academic integrity, while AI ethics perception is the strongest direct predictor and also moderates the relationship between SRL and academic integrity. The research model explains 73.2% of the variance in academic integrity. The effects of SRL and AI ethics perception are strong and stable, whereas the moderating effect is weak. These findings confirm that ethical disposition and self-regulation capacity are the main determinants of academic integrity in the generative AI era, implying that AI literacy curriculum development should emphasize ethical dimensions and the strengthening of learning autonomy.
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Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice-Hall.
Bates, T., Cobo, C., Mariño, O., & Wheeler, S. (2020). Can artificial intelligence transform higher education? International Journal of Educational Technology in Higher Education, 17, 42. https://doi.org/10.1186/s41239-020-00218-x
Bertram Gallant, T. (2017). Academic integrity as a teaching and learning issue: From theory to practice. Theory Into Practice, 56(2), 88–94. https://doi.org/10.1080/00405841.2017.1308173
Chan, C. K. Y. (2024). Students' perceptions of 'AI-giarism': Investigating changes in understandings of academic misconduct. Education and Information Technologies, 30(6), 8087–8108. https://doi.org/10.1007/s10639-024-13151-7
Eaton, S. E. (2023). Postplagiarism: Transdisciplinary ethics and integrity in the age of artificial intelligence and neurotechnology. International Journal for Educational Integrity, 19, 23. https://doi.org/10.1007/s40979-023-00144-1
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P., & Vayena, E. (2018). AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689–707. https://doi.org/10.1007/s11023-018-9482-5
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
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., & Chin, W. W. (2010). A comparison of approaches for the analysis of interaction effects between latent variables using partial least squares path modeling. Structural Equation Modeling, 17(1), 82–109. https://doi.org/10.1080/10705510903439003
International Center for Academic Integrity. (2021). The fundamental values of academic integrity (3rd ed.). Clemson University. https://academicintegrity.org/resources/fundamental-values
Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389–399. https://doi.org/10.1038/s42256-019-0088-2
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., … Kasneci, G. (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
Kock, N., & Hadaya, P. (2016). Minimum sample size estimation in PLS-SEM: The inverse square root and gamma-exponential methods. Information Systems Journal, 28(1), 227–261. https://doi.org/10.1111/isj.12131
Maulidiyah, E. F., Hidayah, M., Sholikhah, A., & Rasyada, D. A. (2024). Peran self regulated learning dalam meningkatkan prestasi akademik pada mahasiswa [The role of self-regulated learning in improving students' academic achievement]. Character: Jurnal Penelitian Psikologi, 11(2), 996–1008. https://ejournal.unesa.ac.id/index.php/character/article/view/59394
Ng, D. T. K., Leung, J. K. L., Su, J., Ng, R. C. W., & Chu, S. K. W. (2023). Teachers' AI digital competencies and twenty-first century skills in the post-pandemic world. Educational Technology Research and Development, 71, 137–161. https://doi.org/10.1007/s11423-024-10369-7
Pintrich, P. R. (2004). A conceptual framework for assessing motivation and self-regulated learning in college students. Educational Psychology Review, 16(4), 385–407.
Pratiwi, N. A. S., & Munari. (2024). Dimensi fraud hexagon dalam mempengaruhi kecurangan akademik pada mahasiswa akuntansi [Fraud hexagon dimensions influencing academic cheating among accounting students]. Jambura Economic Education Journal, 6(2), 455–468. https://ejurnal.ung.ac.id/index.php/jeej
Richardson, M., Abraham, C., & Bond, R. (2012). Psychological correlates of university students' academic performance: A systematic review and meta-analysis. Psychological Bulletin, 138(2), 353–387. https://doi.org/10.1037/a0026838
Saduk, L. M., & Chariri, A. (2024). Ketidakjujuran akademik pada mahasiswa akuntansi yang dibantu oleh Artificial Intelligence (AI): Perspektif fraud triangle [AI-assisted academic dishonesty among accounting students: A fraud triangle perspective]. Jurnal Akuntansi Manado (JAIM), 5(1), 57–71. https://doi.org/10.53682/jaim.vi.8141
Tameno, M. L., Muskanan, M. W., & Angi, Y. F. (2025). Pengaruh fraud hexagon terhadap perilaku kecurangan akademik mahasiswa akuntansi di Kota Kupang [The effect of the fraud hexagon on academic cheating behavior of accounting students in Kupang City]. Jurnal Mahasiswa Manajemen dan Akuntansi (JUMMA'45), 4(1), 368–386. https://doi.org/10.30640/jumma45.v4i1.4654
Tseng, W., & Warschauer, M. (2023). AI-writing tools in education: If you can't beat them, join them. Journal of China Computer-Assisted Language Learning, 3(2), 258–262. https://doi.org/10.1515/jccall-2023-0008
Warschauer, M., Tseng, W., Yim, S., Webster, T., Jacob, S., Du, Q., & Tate, T. (2023). The affordances and contradictions of AI-generated text for writers of English as a second or foreign language. Journal of Second Language Writing, 62, 101071. https://doi.org/10.1016/j.jslw.2023.101071
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
Published
2026-08-11
How to Cite
Sirojul Anwar, & Rochmawati. (2026). The Effect of AI Usage Strategy and Self-Regulated Learning on Students’ Academic Integrity: The Moderating Role of AI Ethics Perception. International Journal of Economics, Business and Innovation Research, 5(05), 885-894. https://doi.org/10.63922/ijebir.v5i05.5961
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