Inteligencia Artificial Generativa y Aprendizaje Autorregulado en Educación Superior: Una Revisión Narrativa

Janeth Rosio  Soto-Gonzáles; Mercedes O.  Palomino; Verónica  Rodríguez-Velásquez; Elva Edith  Demanuel-Cencia; Víctor Manuel Reyes

doi:10.47606/ACVEN/PH0523

Authors

Janeth Rosio Soto-Gonzáles Escuela de Educación Superior Pedagógica Pública. Perú https://orcid.org/0000-0001-5990-3907
Mercedes O. Palomino Escuela de Educación Superior Pedagógica Pública. Perú https://orcid.org/0000-0002-8400-7239
Verónica Rodríguez-Velásquez Escuela de Educación Superior Pedagógica Pública. Perú https://orcid.org/0000-0002-8605-4944
Elva Edith Demanuel-Cencia Escuela de Educación Superior Pedagógica Pública. Perú https://orcid.org/0009-0003-8543-0199
Víctor Manuel Reyes Universidad Nacional Mayor de San Marcos. Perú https://orcid.org/0000-0002-8336-0444

DOI:

https://doi.org/10.47606/ACVEN/PH0523

Keywords:

generative artificial intelligence, self-regulated learning, higher education, pedagogical mediation

Abstract

The emergence of generative artificial intelligence (GAI) in higher education has sparked one of the most fruitful debates in contemporary pedagogy: Can a tool that automatically generates elaborate responses help students learn to learn with greater depth and autonomy? This narrative review addresses this question by analyzing 35 indexed academic sources, organized into five thematic categories ranging from the theoretical foundations of self-regulated learning (SRL) to the most recent empirical evidence on the impact of tools such as ChatGPT on the metacognitive processes of higher education students. The analysis revealed a central paradox: while AI-generated responses have genuine potential to strengthen the monitoring and self-assessment of learning when used actively and strategically, they can undermine autonomous planning and critical thinking when students adopt a passive or dependent pattern of use. Five critical gaps in the literature were identified: the absence of longitudinal studies, the lack of instruments adapted to IAG contexts, the scarcity of Latin American research, the unexamined role of the teacher as a mediator, and the risk of cognitive dependence. These gaps constitute an urgent research agenda. It is considered that integrating IAG into initial teacher education requires a pedagogical design that places the student’s metacognitive agency as the guiding principle.

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References

AL-Jahwari, M., & Yousif, M. J. (2024). The impact of AI tools on education: ChatGPT in focus. Artificial Intelligence & Robotics Development Journal, 4(4), 314–336. https://doi.org/10.52098/airdj.202452 DOI: https://doi.org/10.52098/airdj.20244430

Bishwakarma, S., & Bista, S. (2025). Economics of digital learning and artificial intelligence in higher education: Impact on library use and student achievement in urban Nepal. Interdisciplinary Journal of Education Research, 7, 1–17. https://doi.org/10.38140/ijer-2025.vol7.11

Broadbent, J., & Fuller-Tyszkiewicz, M. (2018). Profiles in self-regulated learning and their correlates for online and blended learning students. Educational Technology Research and Development, 66(6), 1435–1458. https://doi.org/10.1007/s11423-018-9595-9 DOI: https://doi.org/10.1007/s11423-018-9595-9

Coymak, A. (2019). An experimental study of the effect of computer assisted learning on metacognitive performance development in psychology teaching. Contemporary Educational Technology, 10(1), 94–105. https://doi.org/10.30935/cet.512224 DOI: https://doi.org/10.30935/cet.512539

Jubair, A. A. (2024). The generative AI landscape in education: Mapping the terrain of opportunities, challenges and student perception. IEEE Access, 12, 147023–147050. https://doi.org/10.1109/ACCESS.2024.3471759 DOI: https://doi.org/10.1109/ACCESS.2024.3461874

Kearns-Sixsmith, D. (2024). The hallmarks of high-quality online tutoring: A higher ed MM-GT study. Mentoring and Tutoring: Partnership in Learning, 32(2), 206–224. https://doi.org/10.1080/13611267.2024.2322965 DOI: https://doi.org/10.1080/13611267.2024.2323787

Kim, M. C., & Hannafin, M. J. (2011). Scaffolding problem solving in technology-enhanced learning environments (TELEs): Bridging research and theory with practice. Computers & Education, 56(2), 403–417. https://doi.org/10.1016/j.compedu.2010.08.024 DOI: https://doi.org/10.1016/j.compedu.2010.08.024

Köhler, C., & Hartig, J. (2024). ChatGPT in higher education: Measurement instruments to assess student knowledge, usage, and attitude. Contemporary Educational Technology, 16(4), ep528. https://doi.org/10.30935/cedtech/14870 DOI: https://doi.org/10.30935/cedtech/15144

Özturan, T. (2025). Self-regulated language learning and generative AI: A systematic review. Bogazici University Journal of Education, 42(3), 177–194. https://doi.org/10.52597/buje.1751534 DOI: https://doi.org/10.52597/buje.1751534

Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71 DOI: https://doi.org/10.1136/bmj.n71

Paris, S. G., & Paris, A. H. (2001). Classroom applications of research on self-regulated learning. Educational Psychologist, 36(2), 89–101. https://doi.org/10.1207/S15326985EP3602_4 DOI: https://doi.org/10.1207/S15326985EP3602_4

Pintrich, P. R. (1999). The role of motivation in promoting and sustaining self-regulated learning. International Journal of Educational Research, 31(6), 459–470. https://doi.org/10.1016/S0883-0355(99)00015-4 DOI: https://doi.org/10.1016/S0883-0355(99)00015-4

Pintrich, P. R. (2004). A conceptual framework for assessing motivation and self-regulated learning in college students. Educational Psychology Review, 16(4), 385–407. https://doi.org/10.1007/s10648-004-0006-x DOI: https://doi.org/10.1007/s10648-004-0006-x

Pintrich, P. R., Smith, D. A. F., Garcia, T., & McKeachie, W. J. (1993). Reliability and predictive validity of the Motivated Strategies for Learning Questionnaire (MSLQ). Educational and Psychological Measurement, 53(3), 801–813. https://doi.org/10.1177/0013164493053003024 DOI: https://doi.org/10.1177/0013164493053003024

Pintrich, P. R., & Zusho, A. (2002). The development of academic self-regulation: The role of cognitive and motivational factors. En A. Wigfield & J. S. Eccles (Eds.), Development of achievement motivation (pp. 249–284). Elsevier. https://doi.org/10.1016/B978-012750053-9/50012-7 DOI: https://doi.org/10.1016/B978-012750053-9/50012-7

Puustinen, M., & Pulkkinen, L. (2001). Models of self-regulated learning: A review. Scandinavian Journal of Educational Research, 45(3), 269–286. https://doi.org/10.1080/00313830120074206 DOI: https://doi.org/10.1080/00313830120074206

Reeves, P. M., & Sperling, R. A. (2015). A comparison of technologically mediated and face-to-face help-seeking sources. British Journal of Educational Psychology, 85(4), 570–584. https://doi.org/10.1111/bjep.12093 DOI: https://doi.org/10.1111/bjep.12088

Ren, L., Lee, K., & May, L. (2025). A systematic review exploring AI's role in self-regulated learning within education contexts. IEEE Access, 13, 109771–109782. https://doi.org/10.1109/ACCESS.2025.3561528 DOI: https://doi.org/10.1109/ACCESS.2025.3582600

Richardson, J. C., Caskurlu, S., Castellanos-Reyes, D., Duan, S., Duha, M. S. U., Fiock, H., & Long, Y. (2022). Instructors' conceptualization and implementation of scaffolding in online higher education courses. Journal of Computing in Higher Education, 34(2), 427–453. https://doi.org/10.1007/s12528-021-09299-z DOI: https://doi.org/10.1007/s12528-021-09300-3

Sardi, J., Darmansyah, Candra, O., Yuliana, D. F., Habibullah, Yanto, D. T. P., & Eliza, F. (2025). How generative AI influences students' self-regulated learning and critical thinking skills? A systematic literature review. Heliyon, 11(2), e41893. https://doi.org/10.1016/j.heliyon.2025.e41893 DOI: https://doi.org/10.1016/j.heliyon.2025.e41893

Schunk, D. H., & Ertmer, P. A. (1999). Self-regulatory processes during computer skill acquisition: Goal and self-evaluative influences. Journal of Educational Psychology, 91(2), 251–260. https://doi.org/10.1037/0022-0663.91.2.251 DOI: https://doi.org/10.1037/0022-0663.91.2.251

Schunk, D. H., & Zimmerman, B. J. (1997). Social origins of self-regulatory competence. Educational Psychologist, 32(4), 195–208. https://doi.org/10.1207/s15326985ep3204_1 DOI: https://doi.org/10.1207/s15326985ep3204_1

She, H.-C., Cheng, M.-T., Li, T.-W., Wang, C.-Y., Chiu, H.-T., Lee, P.-Z., Chou, W.-C., & Chuang, M.-H. (2012). Web-based undergraduate chemistry problem-solving: The interplay of task performance goals, task learning goals and self-regulation strategies. Computers & Education, 58(4), 1222–1229. https://doi.org/10.1016/j.compedu.2011.12.013 DOI: https://doi.org/10.1016/j.compedu.2011.12.013

Shin, D. D. (2024). Curiosity promotes self-regulated learning and achievement in online courses for students with varying self-efficacy levels. Educational Psychology, 44(4), 455–474. https://doi.org/10.1080/01443410.2024.2342340 DOI: https://doi.org/10.1080/01443410.2024.2372302

Tbaishat, D., Al Fandi, O., Hamad, F., Bukhari, S. M. S., & Al Muhaissen, S. (2026). Modeling generative AI adoption in higher education: An integrated TAM–TPB–SDT framework with SEM validation. Computers in Human Behavior, 163, 108502. https://doi.org/10.1016/j.chb.2025.108502 DOI: https://doi.org/10.1016/j.caeai.2026.100541

Van Harsel, M., Hoogerheide, V., Janssen, E., Verkoeijen, P., & Van Gog, T. (2022). How do higher education students regulate their learning with video modeling examples, worked examples, and practice problems? Instructional Science, 50(2), 297–324. https://doi.org/10.1007/s11251-022-09576-5 DOI: https://doi.org/10.1007/s11251-022-09589-2

Yang, Y.-F. (2010). Students' reflection on online self-correction and peer review to improve writing. Computers & Education, 55(3), 1202–1210. https://doi.org/10.1016/j.compedu.2010.05.017 DOI: https://doi.org/10.1016/j.compedu.2010.05.017

Zimmerman, B. J. (1986). Becoming a self-regulated learner: Which are the key subprocesses? Contemporary Educational Psychology, 11(4), 307–313. https://doi.org/10.1016/0361-476X(86)90027-5 DOI: https://doi.org/10.1016/0361-476X(86)90027-5

Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. En M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 13–39). Elsevier. https://doi.org/10.1016/B978-012109890-2/50031-7 DOI: https://doi.org/10.1016/B978-012109890-2/50031-7

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 DOI: https://doi.org/10.1207/s15430421tip4102_2

Zimmerman, B. J. (2013). From cognitive modeling to self-regulation: A social cognitive career path. Educational Psychologist, 48(3), 135–147. https://doi.org/10.1080/00461520.2013.794676 DOI: https://doi.org/10.1080/00461520.2013.794676

Zimmerman, B. J., & Bandura, A. (1994). Impact of self-regulatory influences on writing course attainment. American Educational Research Journal, 31(4), 845–862. https://doi.org/10.3102/00028312031004845 DOI: https://doi.org/10.3102/00028312031004845

Zimmerman, B. J., Bandura, A., & Martinez-Pons, M. (1992). Self-motivation for academic attainment: The role of self-efficacy beliefs and personal goal setting. American Educational Research Journal, 29(3), 663–676. https://doi.org/10.3102/00028312029003663 DOI: https://doi.org/10.3102/00028312029003663

Zimmerman, B. J., & Kitsantas, A. (1996). Self-regulated learning of a motoric skill: The role of goal setting and self-monitoring. Journal of Applied Sport Psychology, 8(1), 60–75. https://doi.org/10.1080/10413209608406305 DOI: https://doi.org/10.1080/10413209608406308