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Published 2026 | Version v1
Journal article Embargoed

Resolución de problemas: la Inteligencia Artificial y su influencia en la metacognición de los aprendizajes, una revisión sistemática

Authors/Creators

  • 1. Universidad Nacional José Faustino Sánchez Carrión, Huacho, Perú
  • 2. Universidad Nacional de San Antonio Abad del Cusco, Cusco, Perú
  • 3. Universidad Católica de Santa María, Arequipa, Perú

Description

La incorporación de la inteligencia artificial (IA) en los procesos educativos no constituye un fenómeno neutral, sino una transformación sociotécnica que reconfigura las formas en que los estudiantes aprenden, regulan su pensamiento y afrontan situaciones problemáticas. En este marco, el presente artículo tuvo como objetivo analizar, desde la perspectiva de la educación crítica, la influencia de la IA en los procesos metacognitivos implicados en la resolución de problemas. Para ello, se realizó una revisión sistemática de 25 estudios publicados entre 2022 y 2026, sustentada en un análisis cualitativo mediante una matriz manual de síntesis. Los hallazgos se organizaron en tres dimensiones analíticas: andamiaje adaptativo, interacción epistémica y regulación ejecutiva. Los resultados muestran que la IA puede fortalecer el Aprendizaje Basado en Problemas (ABP) cuando su integración pedagógica promueve la validación crítica de la información, la toma de decisiones reflexiva y la soberanía cognitiva del estudiante. Se concluye que estas tecnologías desplazan el ABP tradicional hacia un modelo de indagación híbrida, en el que el uso de herramientas inteligentes exige mayores niveles de conciencia metacognitiva, de modo que su función consista en potenciar el pensamiento y no en sustituir la actividad intelectual del sujeto.

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References

  • Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81–112. https://doi.org/10.3102/003465430298487
  • Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
  • Hong, Q. N., Fàbregues, S., Bartlett, G., Boardman, F., Cargo, M., Dagenais, P., Gagnon, M.-P., Griffiths, F., Nicolau, B., O'Cathain, A., Rousseau, M.-C., Vedel, I., & Pluye, P. (2018). The Mixed Methods Appraisal Tool (MMAT) version 2018 for information professionals and researchers. Education for Information, 34(4), 285–291. https://doi.org/10.3233/EFI-180221
  • Hoßbach, C., & Isaksen, S. (2025). AI-augmented approaches to creative problem-solving: A metacognitive perspective. Creativity and Innovation Management, 34(4), 854–869. https://doi.org/10.1111/caim.70003
  • Jonassen, D. H. (2011). Learning to solve problems: A handbook for designing problem-solving learning environments. Routledge.
  • Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G. L., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., Stadler, M., Weller, J., Kuhn, J., & Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, Article 102274. https://doi.org/10.1016/j.lindif.2023.102274
  • Lai, J. W., Qiu, W., Thway, M., Zhang, L., Jamil, N. B., Su, C. L., Ng, S. S. H., & Lim, F. S. (2025). Leveraging process-action epistemic network analysis to illuminate student self-regulated learning with a Socratic chatbot. Journal of Learning Analytics, 12(1), 32–49. https://doi.org/10.18608/jla.2025.8549
  • Lee, Y., & Lee, S. (2025). Exploring the conceptual model and instructional design principles of intelligent problem-solving learning. Sustainability, 17(17), Article 7682. https://doi.org/10.3390/su17177682
  • Lengua-Cantero, C., Caro-Piñeres, M., & Pérez, J. (2024). Exploring the relationship between artificial intelligence, autonomous learning, and skills required for success in the 21st century. Revista de Gestão Social e Ambiental, 18(6), e07492. https://doi.org/10.24857/rgsa.v18n6-136
  • Levin, I., Marom, M., & Kojukhov, A. (2025). Rethinking AI in education: Highlighting the metacognitive challenge. BRAIN. Broad Research in Artificial Intelligence and Neuroscience, 16(S1), 250–263. https://doi.org/10.70594/brain/16.S1/21
  • Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson.
  • Mayer, R. E. (2014). The Cambridge handbook of multimedia learning (2nd ed.). Cambridge University Press.
  • McCalla, G. (2023). The history of artificial intelligence in education—The first quarter century. In B. du Boulay, A. Mitrovic, & K. Yacef (Eds.), Handbook of artificial intelligence in education (pp. 10–29). Edward Elgar Publishing. https://doi.org/10.4337/9781800375413.00010
  • Miao, F., Holmes, W., Huang, R., & Zhang, H. (2021). AI and education: Guidance for policy-makers. UNESCO. https://doi.org/10.54675/PCSP7350
  • Molenaar, I., van Boxtel, C. A. M., & Sleegers, P. J. C. (2011). Metacognitive scaffolding in an innovative learning arrangement. Instructional Science, 39(6), 785–803. https://doi.org/10.1007/s11251-010-9154-1
  • Moșoi, A. A., Maican, C. I., Cazan, A. M., & Sumedrea, S. (2025). Do students need to think hard? The interplay of AI and cognitive abilities in solving problems. Education and Information Technologies, 30, 24337–24364. https://doi.org/10.1007/s10639-025-13738-8
  • Nasongkhla, J., & Shieh, C.-J. (2025). AI robots as learning companions in PBL: Effects on cognitive and self-regulatory outcomes. Contemporary Educational Technology, 17(4), ep603. https://doi.org/10.30935/cedtech/17417
  • Oltramonti, R. (2024). Integración de la inteligencia artificial en la personalización del aprendizaje. Innova Science Journal, 2(4), 53–67. https://doi.org/10.63618/omd/isj/v2/n4/48
  • Organisation for Economic Co-operation and Development. (2025). AI adoption in the education system. OECD Publishing. https://www.oecd.org/en/publications/ai-adoption-in-the-education-system_69bd0a4a-en.html
  • Pineda, R., Novillo, B., Villamar, M., Cañizares, J., & Castro, K. (2025). Inteligencia artificial en educación: innovación radical para personalizar el aprendizaje y potenciar la autonomía estudiantil. Multidisciplinary Journal of Sciences, Discoveries, and Society, 2(3), e-230. https://doi.org/10.71068/45yja104
  • Quadir, B., Mostafa, K., Yang, J. C., Shen, J., & Akter, R. (2023). ARCS approach to PTA-based programming language practice sessions: Factors influencing programming problem-solving skills. Education and Information Technologies, 28, 13713–13735. https://doi.org/10.1007/s10639-023-11740-6
  • 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
  • Sapkota, B., & Bondurant, L. (2024). Assessing concepts, procedures, and cognitive demand of ChatGPT-generated mathematical tasks. International Journal of Technology in Education, 7(2), 218–238. https://doi.org/10.46328/ijte.677
  • Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Polity Press.
  • Sidorkin, A. (2025). Extended executive cognition, a learning outcome for the AI age. Computers and Education Open, 9, Article 100294. https://doi.org/10.1016/j.caeo.2025.100294
  • Silva-Fuentealba, E., Valdés-León, G., & Yáñez, R. O. (2025). Artificial intelligence in the classroom: Empowering problem solving through creative thinking. Revista de Comunicación de la SEECI, 58, e927. https://doi.org/10.15198/seeci.2025.58.e927
  • Skaug, S. H. (2023). Generative AI: Here to stay, but for good? Technology in Society, 75, Article 102372. https://doi.org/10.1016/j.techsoc.2023.102372
  • Sosa Zerna, R. K., Obando Melo, E. E., Pullotasig Yugcha, L. A., Mamarandi Llumiquinga, M. G., & Flores Miño, C. P. (2025). Desigualdad en el acceso a la educación digital: Desafíos y soluciones para la equidad. Ciencia Latina Revista Científica Multidisciplinar, 9(1), 10972–10990. https://doi.org/10.37811/cl_rcm.v9i1.16679
  • Steinert, S., Avila, K. E., Ruzika, S., Kuhn, J., & Küchemann, S. (2024). Harnessing large language models to develop research-based learning assistants for formative feedback. Smart Learning Environments, 11, Article 62. https://doi.org/10.1186/s40561-024-00354-1
  • Su, K.-D. (2022). Implementation of innovative artificial intelligence cognitions with problem-based learning guided tasks to enhance students' performance in science. Journal of Baltic Science Education, 21(2), 245–257. https://doi.org/10.33225/jbse/22.21.245
  • Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59(236), 433–460. https://doi.org/10.1093/mind/LIX.236.433
  • Ubal Camacho, M., Tambasco, P., Martínez, S., & García Correa, M. (2023). El impacto de la inteligencia artificial en la educación: Riesgos y potencialidades de la IA en el aula. RiiTE. Revista Interuniversitaria de Investigación en Tecnología Educativa, 15, 41–57. https://doi.org/10.6018/riite.584501
  • UNESCO. (2023). Tecnología en educación: ¿Una herramienta en los términos de quién? https://cfrd.udec.cl/sitio/uploads/2024/07/informe-unesco-2023.pdf
  • UNESCO. (2025a). AI and education: Protecting the rights of learners. https://www.unesco.org/en/articles/ai-and-education-protecting-rights-learners
  • UNESCO. (2025b). Encuesta global para mapear la adopción de la inteligencia artificial en la educación superior. UNESCO IESALC. https://www.iesalc.unesco.org/es/articles/encuesta-global-para-mapear-la-adopcion-de-la-inteligencia-artificial-en-la-educacion-superior
  • UNESCO. (2025c). Una inteligencia artificial ética e inclusiva en América Latina y el Caribe: La UNESCO presenta su propuesta. https://www.unesco.org/es/articles/una-inteligencia-artificial-etica-e-inclusiva-en-america-latina-y-el-caribe-la-unesco-presenta-su
  • Wang, S., Wang, F., Zhu, Z., Wang, J., Tran, T., & Du, Z. (2024). Artificial intelligence in education: A systematic literature review. Expert Systems with Applications, 252, 124167. https://doi.org/10.1016/j.eswa.2024.124167
  • Wu, X., Ng, D. T., & Chiu, T. K. (2026). Self-regulated learning with AI: A comparative analysis of general-purpose and task-specific platforms. International Journal of Technology in Education, 9(1), 279–302. https://doi.org/10.46328/ijte.5241
  • Yang, W. (2025). Redefining educational objectives in the age of artificial intelligence: The SCALE taxonomy. TAO, 1, Article 100018. https://doi.org/10.1016/j.tao.2025.100018
  • Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education: Where are the educators? International Journal of Educational Technology in Higher Education, 16, Article 39. https://doi.org/10.1186/s41239-019-0171-0
  • Zhai, C., Wibowo, S., & Li, L. D. (2024). The effects of over-reliance on AI dialogue systems on students' cognitive abilities: A systematic review. Smart Learning Environments, 11, Article 28. https://doi.org/10.1186/s40561-024-00316-7
  • Zhang, Y., Tian, H., & Lu, J. (2025). The relationship between higher-order thinking and the development of problem-solving skills among teacher trainees using generative AI: A moderated mediation analysis. BMC Psychology, 13, Article 1094. https://doi.org/10.1186/s40359-025-03404-6
  • 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