Autonomous learning and metacognition in AI-mediated educational environments in high school students: A systematic review
Keywords:
Autonomous learning; Self-regulated learning; Metacognition; Epistemic agency; Artificial Intelligence; High school; PRISMA 2020.Abstract
The integration of artificial intelligence (AI)-based tools in secondary education has transformed the dynamics of interaction with knowledge, although its effects on autonomous learning and metacognitive processes in adolescent students remain insufficiently systematized. This systematic review examines the relationship between autonomous learning strategies, metacognitive control, and the use of AI-based tools among high school students (aged 14 to 19). Following PRISMA 2020 guidelines, systematic searches were conducted in Scopus, Web of Science, and SciELO to identify empirical studies published between January 2021 and March 2026. Through the application of a strict inclusion protocol and methodological quality assessment using the Mixed Methods Appraisal Tool (MMAT), a final set of 18 papers was selected. The results indicate that Intelligent Tutoring Systems incorporating visual analytics can improve the accuracy of metacognitive monitoring and increase time self-management by an average of 18 % to 24 %. Conversely, the unguided use of generative AI—specifically conversational language models—is associated with an increased "illusion of knowing" (a false perception of understanding) and a reduction in deep, reflective processing. In conclusion, the impact of AI does not determine performance in isolation; rather, it acts as a moderator of students' existing self-regulation competencies.
References
Alvarez Terán, J. M. (2026). Modelo Aula invertida en el desempeño académico de estudiantes universitarios en Sudamérica: Una revisión sistemática. Revista Santiago, (168), 807–817. https://santiago.uo.edu.cu/index.php/stgo/article/view/29123
Becker, T., Knoop, J., & Richter, S. (2025). Restricted language models in high school writing: Fostering self-regulation through constrained AI interactions. Journal of Educational Psychology, 117(2), 205–221. https://doi.org/10.1037/edu0000845
Chai, C. S., Lin, P. Y., Jong, M. S. Y., & Dai, Y. (2024b). Secondary school students' perceptions and usage of generative artificial intelligence in self-regulated learning environments. Computers & Education, 208, 104932. https://doi.org/10.1016/j.compedu.2023.104932
Chai, C. S., Zhou, N., Wang, X., & Zhang, Y. (2024a). Fostering self-regulated learning in AI-mediated secondary education: A mixed-methods inquiry. Computers & Education, 210, 104958. https://doi.org/10.1016/j.compedu.2023.104958
Chen, L., & Zhang, Y. (2022). Deferred vs. immediate automated feedback in intelligent tutoring systems: Effects on metacognitive restructuring and conceptual transfer. Journal of Computer Assisted Learning, 38(5), 1412–1425. https://doi.org/10.1111/jcal.12689
Fan, Y., Tang, L., Le, H., Shen, K., Tan, S., Zhao, Y., Shen, Y., Li, X., & Gašević, D. (2025). Beware of metacognitive laziness: Effects of generative artificial intelligence on learning motivation, processes, and performance. British Journal of Educational Technology, 56(2), 489–530. https://doi.org/10.1111/bjet.13544
Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive-developmental inquiry. American Psychologist, 34(10), 906–911. https://doi.org/10.1037/0003-066X.34.10.906
Formanek, M., Trempler, K., & Scheiter, K. (2023). Intelligent tutoring systems in secondary mathematics: Promoting autonomous learning, time management, and task persistence. Learning and Instruction, 85, 101740. https://doi.org/10.1016/j.learninstruc.2023.101740
Gómez, J., & Andrade, L. (2024). Ansiedad, autoeficacia y autorregulación en el uso de tutores inteligentes de matemáticas en estudiantes de secundaria. Revista Colombiana de Educación, 89, 45–67.
Hwang, G. J., & Tu, Y. F. (2023). Roles and research trends of artificial intelligence in mathematics education: A systematic review and metacognitive framework. Interactive Learning Environments, 31(8), 5120–5138. https://doi.org/10.1080/10494820.2021.2018615
Jiménez Jiménez, J. (2026). Implementación de programas STEAM en escuelas primarias con recursos limitados de la República Dominicana. Revista Santiago, (168), 490–501. https://santiago.uo.edu.cu/index.php/stgo/article/view/29107
Kim, S., Park, J., & Lee, H. (2024). Socratic AI scaffolding for inquiry-based learning in secondary schools. Educational Technology Research and Development, 72(3), 145–168.
Lodge, J. M., Yang, S., Furze, A., & Dawson, P. (2023). It's not about cheating, it's about learning: Generative AI, metacognitive monitoring, and the illusion of knowing in secondary and higher education. Australasian Journal of Educational Technology, 39(5), 1–17. https://doi.org/10.14742/ajet.8872
López-Martínez, A., García-Cabrera, J., & Sánchez-Moya, A. (2022). Percepción de la autonomía y la autorregulación en entornos virtuales de aprendizaje mediados por IA. Revista de Educación, 398, 89–112.
Molenaar, I., Horvers, A., & Baker, R. S. (2021). Towards hybrid human-system regulation: Scaffolding young learners' self-regulated learning with personalized dashboards. Computers in Human Behavior, 124, 106894. https://doi.org/10.1016/j.chb.2021.106894
Nguyen, T., & Richter, S. (2023). Metacognitive awareness and goal management through learning analytics dashboards in secondary education. Learning and Individual Differences, 105, 102312.
Ocaña-Fernández, Y., Valenzuela-Fernández, L., & Garro-Aburto, L. (2023). Inteligencia artificial y desarrollo del pensamiento crítico en estudiantes de educación secundaria en América Latina. Revista Electrónica Educare, 27(2), 1–18. https://doi.org/10.15359/ree.27-2.15823
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., ... & Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
Park, J., & Lee, S. (2022). AI-powered spaced practice for long-term retention in high school students. Journal of Educational Psychology, 114(4), 789–805.
Santos, M., Oliveira, R., & Costa, F. (2023). Adaptive reading platforms and metacognitive monitoring in Brazilian high schools. Computers & Education, 195, 104712.
Seufert, T., Schmeck, A., & Rathgeb, M. (2024). Cognitive load, executive functions, and dynamic AI scaffolding in secondary school students' self-regulation. Educational Psychology Review, 36(1), 12. https://doi.org/10.1007/s10648-024-09852-w
Silva, P., & Morales, R. (2023). Agencia epistémica y uso de ChatGPT en asignaturas humanísticas: un estudio fenomenológico. Estudios Pedagógicos, 49(2), 201–218.
Wang, Y., Zhang, L., & Chen, X. (2024). Multimodal adaptive environments and metacognitive monitoring in Chinese secondary schools. British Journal of Educational Technology, 55(2), 456–478.
Zimmerman, B. J., & Schunk, D. H. (Eds.). (2011). Self-regulated learning and academic achievement: Theoretical perspectives (2nd ed.). Routledge. https://doi.org/10.4324/9781410601032
Zubiran-Escudero, J., Gómez-Zermeño, M. G., & Alemán-de la Garza, L. (2024). Autorregulación, autoeficacia y agencia epistémica en el uso de entornos virtuales mediados por inteligencia artificial en bachillerato. Apertura: Revista de Innovación Educativa, 16(1), 34–49. https://doi.org/10.32870/Ap.v16n1.2415
Published
Issue
Section
License
Copyright (c) 2026 José Luis Rugel Llongo, Miguel Alberto Velez Sancarranco
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
CC Reconocimiento-NoComercial-SinObrasDerivadas 4.0
.jpg)
![](https://clustrmaps.com/site/1bhn8" title="Visit tracker"><img src="//www.clustrmaps.com/map_v2.png?d=2WTJsN7QOuRFRD2DOJOZtvZ6v94bxB6RU96I-iXx-_s&cl=ffffff"></a>)
