Managing the modern classroom: intra-learner, teacher-learner, and AI-learner interactions in secondary education: A systematic review.
DOI:
https://doi.org/10.51168/ks5fvs76Keywords:
Tri-Interactive Classroom Management, Self-Regulated Learning (SRL),, Teacher-Learner Interaction, , AI-Learner Interaction, Secondary EducationAbstract
The wide-ranging integration of artificial intelligence (AI) technology has had a significant impact on classroom management in secondary education. While traditional classroom management models focused on interactions between teachers and learners, the modern classroom environment requires consideration of intra-learner interactions and AI-learner interactions in addition to traditional teacher-learner interactions. Methods: A systematic literature search was conducted on Google Scholar, Scopus, and Web of Science to identify peer-reviewed studies on secondary education, classroom management, and AI-assisted learning. Based on the search results and after applying pre-defined inclusion criteria, 29 studies were included in the synthesis. The synthesis was conducted to identify three interaction domains: intra-learner interactions, teacher-learner interactions, and AI-learner interactions. Results: The studies indicate that behavioral regulation, cognitive engagement, and learner autonomy are optimized when intra-learner interactions, teacher-learner interactions, and AI-learner interactions occur in a synergistic manner. While AI-assisted learning enhances learner self-regulation and provides feedback to learners, it also poses a risk of digital addiction and a decrease in teacher-learner interaction if not balanced in an appropriate manner. Conclusion: The concept of tri-interactive classroom management provides an integrated framework for modern secondary education. It is imperative for teachers to develop skills that promote learner autonomy while simultaneously utilizing AI technology to function as co-regulators of the learning process. Future studies should investigate the application of this framework in diverse cultural contexts and its long-term effects on learners, including the role of AI in motivating and cognitively enhancing learners.
References
1. Bandura, A. (1997). Self-efficacy: The exercise of control. W. H. Freeman.
2. Brenner, C. A. (2022). Self-regulated learning, self-determination theory, and teacher candidates. Smart Learning Environments, 9, 5. https://doi.org/10.1186/s40561-021-00184-5
3. Brophy, J. (2006). History of research on classroom management. In C. M. Evertson & C. S. Weinstein (Eds.), Handbook of classroom management: Research, practice, and contemporary issues (pp. 17-43). Routledge.
4. Brophy, J. (2010). Assessing teacher-student interaction processes. In Handbook of Research on Teaching (5th ed., pp. 399-422).
5. Chang, W. L., & Sun, J. C. Y. (2024). Evaluating AI's impact on self-regulated language learning: A systematic review. System, 126, 103484. https://doi.org/10.1016/j.system.2024.103484
6. Chen, X., Xie, H., Zou, D., & Hwang, G. J. (2020). Application and theory gaps during the rise of artificial intelligence in education. Computers and Education: Artificial Intelligence, 1(1), 100002.
https://doi.org/10.1016/j.caeai.2020.100002
7. Dent, A. L., & Koenka, A. C. (2016). The relation between self-regulated learning and academic achievement across childhood and adolescence: A meta-analysis. Educational Psychology Review, 28(3), 425-474.
https://doi.org/10.1007/s10648-015-9320-8
8. Dignath, C., & Büttner, G. (2008). Components of fostering self-regulated learning among students: A meta-analysis on intervention studies at primary and secondary school level. Metacognition and Learning, 3(3), 231-264. https://doi.org/10.1007/s11409-008-9029-x
9. Dignath-van Ewijk, C., & van der Werf, G. (2016). Which components of teacher competence determine whether teachers enhance self-regulated learning? Frontline Learning Research, 4(5), 83-105.
https://doi.org/10.14786/flr.v4i5.247
10. Efklides, A. (2011). Interactions of metacognition with motivation and affect in self-regulated learning: The MASRL model. Educational Psychologist, 46(1), 6-25. https://doi.org/10.1080/00461520.2011.538645
11. Evertson, C. M., & Weinstein, C. S. (Eds.). (2006). Handbook of classroom management: Research, practice, and contemporary issues. Routledge.
12. Greene, J. A. (2021). Teacher support for metacognition and self-regulated learning: A compelling story and a prototypical model. Metacognition and Learning, 16(3), 651-666. https://doi.org/10.1007/s11409-021-09283-7
13. Hattie, J. (2008). Visible learning: A synthesis of over 800 meta-analyses relating to achievement. Routledge.
14. 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
15. Karlen, Y., Hirt, C. N., Jud, J., Rosenthal, A., & Eberli, T. D. (2023). Teachers as learners and agents of self-regulated learning: The importance of teacher competence for promoting metacognition. Teaching and Teacher Education, 125, 104055. https://doi.org/10.1016/j.tate.2023.104055
16. Korpershoek, H., Harms, T., de Boer, H., van Kuijk, M., & Doolaard, S. (2016). A meta-analysis of the effects of classroom management strategies on academic, behavioral, emotional, and motivational outcomes. Review of Educational Research, 86(3), 643-680. https://doi.org/10.3102/0034654315626799
17. Marzano, R. J. (2003). Classroom management that works: Research-based strategies for every teacher. ASCD.
18. Moos, D. C. (2012). Self-regulated learning in the classroom: A literature review. ISRN Education, 2012, 423284.
https://doi.org/10.1155/2012/423284
19. Noroozi, O., Schunn, C., Schneider, B., & Banihashem, S. K. (2025). Advancing peer learning with learning analytics and artificial intelligence. International Journal of Educational Technology in Higher Education, 22, 62.
https://doi.org/10.1186/s41239-025-00559-5
20. Ouyang, Z. (2025). Self-regulated learning and engagement as serial mediators between AI- driven adaptive learning platform characteristics and educational quality. Frontiers in Psychology, 16. https://doi.org/10.3389/fpsyg.2025.1646469
21. Perry, N. E., Phillips, L. M., & Hutchinson, L. R. (2004). Examining features of tasks and their potential to promote self-regulated learning. Teachers College Record, 106(9), 1854- 1878. https://doi.org/10.1111/j.1467-9620.2004.00408.x https://doi.org/10.1177/016146810410600909
22. Pianta, R. C., & Hamre, B. K. (2009). Conceptualizing classroom management: The role of social-emotional predictors in teacher-student relationships. In C. M. Evertson & C. S. Weinstein (Eds.), Handbook of classroom management (pp.109-119).
23. Ryan, R. M., & Deci, E. L. (2017). Self-determination theory: Basic psychological needs in motivation, development, and wellness. Guilford. https://doi.org/10.1521/978.14625/28806
24. Schunk, D. H., & Greene, J. A. (Eds.). (2018). Handbook of self-regulation of learning and performance (2nd ed.). Routledge. https://doi.org/10.4324/9781315697048
25. Tekir, S. (2025). A post-pandemic review of empirical studies on classroom management. SAGE Open, 15, 21582440251377321. https://doi.org/10.1177/21582440251377321
26. VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist, 46(4), 197-221. https://doi.org/10.1080/00461520.2011.611369
27. Weinstein, C. S., Tomlinson-Clarke, S., & Curran, M. (2004). Toward a conception of culturally responsive classroom management. Journal of Teacher Education, 55(1), 25-36. https://doi.org/10.1177/0022487103259812
28. 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
29. Zimmerman, B. J., & Schunk, D. H. (Eds.). (2011). Self-regulated learning and academic achievement: Theoretical perspectives (2nd ed.). Routledge.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Albert Byiringiro (Author)

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
SJ-Education publishes under the Attribution-Noncommercial-NoDerivatives 4.0 international (CCBY-NC-ND 4.0) license which allows you to Share, Copy, and redistribute the materials in any medium or format. The licensor cannot revoke these freedoms as long as you follow the license terms; 1. Attribution: You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. 2. Non-commercial: You may not use the material for commercial purposes. Commercial use is one primarily intended for commercial advantage or monetary compensation. 3. No Derivatives: if you remix, transform, or build upon the material, you may not distribute the modified material. 4. No additional restrictions: You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
