Revisión Sistemática sobre la Inteligencia Artificial Generativa y Aprendizaje Autorregulado en Educación Superior

Autores/as

DOI:

https://doi.org/10.70881/hnj/v4/n1/108

Palabras clave:

inteligencia artificial generativa, aprendizaje autorregulado, educación superior, metacognición, andamiaje adaptativo

Resumen

La inteligencia artificial generativa ha transformado profundamente la educación superior desde el lanzamiento de ChatGPT en noviembre de 2022, generando tanto oportunidades como riesgos para el desarrollo de competencias de aprendizaje autónomo. La relación entre estas herramientas y la capacidad de los estudiantes para planificar, monitorear y regular sus propios procesos cognitivos es compleja y dependiente del diseño pedagógico: mientras un andamiaje instruccional estructurado potencia la planificación estratégica y el control metacognitivo, el uso no guiado favorece la "pereza metacognitiva" y la dependencia tecnológica. La alfabetización en IA, la autoeficacia y la disciplina académica modulan significativamente estos efectos, y la producción científica sobre el tema crece de manera exponencial, concentrando el 71.4% de la evidencia disponible en 2025. Integrar la IA generativa en la universidad no es una decisión tecnológica sino pedagógica, que exige rediseñar intencionalmente los entornos de aprendizaje para preservar y fortalecer la autorregulación estudiantil

Descargas

Los datos de descarga aún no están disponibles.

Referencias

Bandura, A. (1997). Self-efficacy: The exercise of control. W. H. Freeman.

Bjork, E. L., & Bjork, R. A. (2011). Making things hard on yourself, but in a good way: Creating desirable difficulties to enhance learning. En M. A. Gernsbacher, R. W. Pew, L. M. Hough, & J. R. Pomerantz (Eds.), Psychology and the real world: Essays illustrating fundamental contributions to society (pp. 56-64). Worth Publishers.

Chang, D., Lin, M., Hajian, S., & Wang, Q. Q. (2023). Educational design principles of using AI chatbot that supports self-regulated learning in education: Goal setting, feedback, and personalization. Sustainability, 15(17), Article 12921. https://doi.org/10.3390/su151712921 DOI: https://doi.org/10.3390/su151712921

Chen, S. Y. (2023). Generative AI, learning and new literacies. Journal of Educational Technology Development and Exchange, 16(2), 1-14. https://doi.org/10.18785/jetde.1602.01 DOI: https://doi.org/10.18785/jetde.1602.01

Fan, Y., Tang, L., Le, H., Shen, K., Tan, S., Zhao, Y., Zhang, H., Zhang, Y., Wang, J., & Gašević, D. (2025). Beware of metacognitive laziness: Effects of generative artificial intelligence on learning motivation, processes, and performance. British Journal of Educational Technology, 56(1), 144-168. https://doi.org/10.1111/bjet.13513 DOI: 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 DOI: https://doi.org/10.1037/0003-066X.34.10.906

Garrison, D. R. (1997). Self-directed learning: Toward a comprehensive model. Adult Education Quarterly, 48(1), 18-33. https://doi.org/10.1177/074171369704800103 DOI: https://doi.org/10.1177/074171369704800103

Ji, Y., Zhong, M., Lyu, S., Li, T., Niu, S., & Zhan, Z. (2025). How does AI literacy affect individual innovative behavior? The mediating role of psychological need satisfaction, creative self-efficacy, and self-regulated learning. Education and Information Technologies, 30, 1847-1872. https://doi.org/10.1007/s10639-025-13437-4 DOI: https://doi.org/10.1007/s10639-025-13437-4

Knowles, M. S. (1975). Self-directed learning: A guide for learners and teachers. Association Press.

Lee, H., Chen, P., Wang, W., Huang, Y., & Wu, T. (2024). Empowering ChatGPT with guidance mechanism in blended learning: Effect of self-regulated learning, higher-order thinking skills, and knowledge construction. International Journal of Educational Technology in Higher Education, 21, Article 47. https://doi.org/10.1186/s41239-024-00454-4 DOI: https://doi.org/10.1186/s41239-024-00447-4

Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson Education.

Ma, W., Ma, W., Hu, Y., & Bi, X. (2025). The who, why, and how of AI-based chatbots for learning and teaching in higher education: A systematic review. Education and Information Technologies, 30, 2845-2879. https://doi.org/10.1007/s10639-024-13128-6 DOI: https://doi.org/10.1007/s10639-024-13128-6

Nguyen, A., Hong, Y., Dang, B., & Huang, X. (2024). Human-AI collaboration patterns in AI-assisted academic writing. Studies in Higher Education, 49(8), 1456-1475. https://doi.org/10.1080/03075079.2024.2323593 DOI: https://doi.org/10.1080/03075079.2024.2323593

Ou, A. W., Khuder, B., Franzetti, S., & Negretti, R. (2024). Conceptualising and cultivating critical GAI literacy for academic writing. Journal of Second Language Writing, 66, Article 101156. https://doi.org/10.1016/j.jslw.2024.101156 DOI: https://doi.org/10.1016/j.jslw.2024.101156

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, Article n71. https://doi.org/10.1136/bmj.n71 DOI: https://doi.org/10.1136/bmj.n71

Pan, M., Lai, C., & Guo, K. (2025). Effects of GenAI-empowered interactive support on university EFL students' self-regulated strategy use and engagement in reading. The Internet and Higher Education, 65, Article 100991. https://doi.org/10.1016/j.iheduc.2024.100991 DOI: https://doi.org/10.1016/j.iheduc.2024.100991

Roll, I., & Wylie, R. (2016). Evolution and revolution in artificial intelligence in education. International Journal of Artificial Intelligence in Education, 26(2), 582-599. https://doi.org/10.1007/s40593-016-0110-3 DOI: https://doi.org/10.1007/s40593-016-0110-3

Schunk, D. H., & Zimmerman, B. J. (2012). Motivation and self-regulated learning: Theory, research, and applications. Routledge. DOI: https://doi.org/10.4324/9780203831076

Van der Wal, J. (2024). Generative AI for writing support: Opportunities and challenges for equitable access. Journal of Writing Research, 16(1), 105-134.

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30, 5998-6008.

Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.

Weng, C. H., Hsieh, J. S. C., & Tsai, C. C. (2024). The role of personality traits and AI anxiety in predicting university students' usage of generative AI tools. Education and Information Technologies. Advance online publication. https://doi.org/10.1007/s10639-024-12856-z DOI: https://doi.org/10.1007/s10639-024-12856-z

Winne, P. H., & Hadwin, A. F. (1998). Studying as self-regulated learning. En D. J. Hacker, J. Dunlosky, & A. C. Graesser (Eds.), Metacognition in educational theory and practice (pp. 277-304). Lawrence Erlbaum Associates.

Wood, D., Bruner, J. S., & Ross, G. (1976). The role of tutoring in problem solving. Journal of Child Psychology and Psychiatry, 17(2), 89-100. https://doi.org/10.1111/j.1469-7610.1976.tb00381.x DOI: https://doi.org/10.1111/j.1469-7610.1976.tb00381.x

Zhang, Y., & Xu, J. (2025). The paradox of AI-enhanced self-efficacy: Technological dependence in academic learning. Computers in Human Behavior, 152, Article 108247. https://doi.org/10.1016/j.chb.2024.108247 DOI: https://doi.org/10.1016/j.chb.2024.108247

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. (2008). Investigating self-regulation and motivation: Historical background, methodological developments, and future prospects. American Educational Research Journal, 45(1), 166-183. https://doi.org/10.3102/0002831207312909 DOI: https://doi.org/10.3102/0002831207312909

Descargas

Publicado

2026-03-16

Cómo citar

Calderón-Pineda, F. V., & Palacios-Meléndez, J. G. (2026). Revisión Sistemática sobre la Inteligencia Artificial Generativa y Aprendizaje Autorregulado en Educación Superior. Horizon Nexus Journal, 4(1), 183-199. https://doi.org/10.70881/hnj/v4/n1/108

Artículos similares

21-30 de 82

También puede Iniciar una búsqueda de similitud avanzada para este artículo.

Artículos más leídos del mismo autor/a

1 2 3 4 5 6 7 8 9 > >>