De la asistencia generativa a la autonomía algorítmica: impacto de los agentes de inteligencia artificial sobre la autorregulación del aprendizaje en educación superior
DOI:
https://doi.org/10.65415/76z52510Palabras clave:
agentes de inteligencia artificial; autorregulación del aprendizaje; metacognición; autonomía algorítmica; educación superior.Resumen
La inteligencia artificial que llega a la universidad ya no se limita a responder: los agentes basados en modelos de lenguaje planifican, recuerdan, utilizan herramientas y ejecutan tareas de varios pasos con supervisión mínima. Este artículo de reflexión sostiene que ese tránsito modifica la naturaleza de lo que el estudiante delega. La asistencia generativa descarga sobre todo la ejecución de la tarea; el agente, en cambio, asume las funciones de previsión, monitoreo y autoevaluación que constituyen el núcleo metacognitivo de la autorregulación del aprendizaje. A partir de la integración del modelo cíclico de Zimmerman, la perspectiva agéntica de Bandura y la teoría de los niveles de automatización, se propone el constructo de desplazamiento regulatorio, un modelo de cuatro niveles de autonomía algorítmica y cuatro proposiciones contrastables. El análisis identifica una paradoja de la supervisión: supervisar a un agente exige precisamente la competencia autorreguladora que su uso intensivo impide consolidar. La evidencia reciente sobre retirada de la asistencia y sobre diseños con salvaguardas indica que el efecto no está determinado por la tecnología, sino por la finalidad que gobierna su diseño. Se derivan principios para la educación superior orientados a que la regulación retorne al estudiante.
Descargas
Referencias
Bandura, A. (2001). Social cognitive theory: An agentic perspective. Annual Review of Psychology, 52, 1–26. https://doi.org/10.1146/annurev.psych.52.1.1
Bastani, H., Bastani, O., Sungu, A., Ge, H., Kabakcı, Ö., & Mariman, R. (2025). Generative AI without guardrails can harm learning: Evidence from high school mathematics. Proceedings of the National Academy of Sciences, 122(26), e2422633122. https://doi.org/10.1073/pnas.2422633122
Darvishi, A., Khosravi, H., Sadiq, S., Gašević, D., & Siemens, G. (2024). Impact of AI assistance on student agency. Computers & Education, 210, 104967. https://doi.org/10.1016/j.compedu.2023.104967
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
Järvelä, S., Nguyen, A., & Hadwin, A. (2023). Human and artificial intelligence collaboration for socially shared regulation in learning. British Journal of Educational Technology, 54(5), 1057–1076. https://doi.org/10.1111/bjet.13325
Kırcaburun, K. (2026). Generative AI use and critical thinking dispositions in higher education: A cross-sample study of the sequential role of metacognitive weakness and epistemic laziness. Journal of Intelligence, 14(7), 147. https://doi.org/10.3390/jintelligence14070147
Kosmyna, N., Hauptmann, E., Yuan, Y. T., Situ, J., Liao, X.-H., Beresnitzky, A. V., Braunstein, I., & Maes, P. (2025). Your brain on ChatGPT: Accumulation of cognitive debt when using an AI assistant for essay writing task [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2506.08872
Lee, H.-P., Sarkar, A., Tankelevitch, L., Drosos, I., Rintel, S., Banks, R., & Wilson, N. (2025). The impact of generative AI on critical thinking: Self-reported reductions in cognitive effort and confidence effects from a survey of knowledge workers. En Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (pp. 1–22). Association for Computing Machinery. https://doi.org/10.1145/3706598.3713778
Lodge, J. M., de Barba, P., & Broadbent, J. (2023). Learning with generative artificial intelligence within a network of co-regulation. Journal of University Teaching and Learning Practice, 20(7). https://doi.org/10.53761/1.20.7.02
Molenaar, I. (2022a). Towards hybrid human-AI learning technologies. European Journal of Education, 57(4), 632–645. https://doi.org/10.1111/ejed.12527
Molenaar, I. (2022b). The concept of hybrid human-AI regulation: Exemplifying how to support young learners’ self-regulated learning. Computers and Education: Artificial Intelligence, 3, 100070. https://doi.org/10.1016/j.caeai.2022.100070
Panadero, E. (2017). A review of self-regulated learning: Six models and four directions for research. Frontiers in Psychology, 8, 422. https://doi.org/10.3389/fpsyg.2017.00422
Parasuraman, R., & Manzey, D. H. (2010). Complacency and bias in human use of automation: An attentional integration. Human Factors, 52(3), 381–410. https://doi.org/10.1177/0018720810376055
Parasuraman, R., Sheridan, T. B., & Wickens, C. D. (2000). A model for types and levels of human interaction with automation. IEEE Transactions on Systems, Man, and Cybernetics – Part A: Systems and Humans, 30(3), 286–297. https://doi.org/10.1109/3468.844354
Risko, E. F., & Gilbert, S. J. (2016). Cognitive offloading. Trends in Cognitive Sciences, 20(9), 676–688. https://doi.org/10.1016/j.tics.2016.07.002
Salomon, G., Perkins, D. N., & Globerson, T. (1991). Partners in cognition: Extending human intelligence with intelligent technologies. Educational Researcher, 20(3), 2–9. https://doi.org/10.3102/0013189X020003002
Sapkota, R., Roumeliotis, K. I., & Karkee, M. (2026). AI agents vs. agentic AI: A conceptual taxonomy, applications and challenges. Information Fusion, 126, 103599. https://doi.org/10.1016/j.inffus.2025.103599
Wang, L., Ma, C., Feng, X., Zhang, Z., Yang, H., Zhang, J., Chen, Z., Tang, J., Chen, X., Lin, Y., Zhao, W. X., Wei, Z., & Wen, J. (2024). A survey on large language model based autonomous agents. Frontiers of Computer Science, 18(6), 186345. https://doi.org/10.1007/s11704-024-40231-1
Xu, X., Qiao, L., Cheng, N., Liu, H., & Zhao, W. (2025). Enhancing self-regulated learning and learning experience in generative AI environments: The critical role of metacognitive support. British Journal of Educational Technology, 56(5), 1842–1863. https://doi.org/10.1111/bjet.13599
Yan, L., Sha, L., Zhao, L., Li, Y., Martinez-Maldonado, R., Chen, G., Li, X., Jin, Y., & Gašević, D. (2024). Practical and ethical challenges of large language models in education: A systematic scoping review. British Journal of Educational Technology, 55(1), 90–112. https://doi.org/10.1111/bjet.13370
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
Descargas
Publicado
Número
Sección
Licencia
Derechos de autor 2026 Raúl Andrés Pinela Cárdenas, Carla Isabel Lozano Alvarado, Billy Rafael Rivas Reyes , Rodrigo José Pazmiño Pérez
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.
