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Artículos originales

Vol. 5 Núm. 12 (2025): Simbiosis, Revista de Educación y Psicología

Impacto psicoeducativo de los tutores inteligentes: Una revisión sistemática

Psychoeducational Impact of Intelligent Tutoring Systems: A Systematic Review
Publicado
2025-10-31

La investigación examinó el impacto psicoeducativo de los Tutores Inteligentes (ITS) mediante una revisión sistemática de estudios publicados entre 2015 y 2025. Se analizaron 78 investigaciones empíricas que evaluaron la influencia de los ITS en la autoeficacia, el engagement académico y el bienestar subjetivo de los estudiantes. Los resultados mostraron que los ITS promovieron mayores niveles de autoeficacia al ofrecer retroalimentación adaptativa y experiencias de éxito temprano. Asimismo, la personalización algorítmica y la adaptabilidad emocional favorecieron el engagement cognitivo y conductual, potenciando la motivación y la autorregulación del aprendizaje. En cuanto al bienestar, se observó una reducción de la ansiedad y un aumento de la satisfacción académica, aunque la evidencia resultó limitada por la falta de estudios longitudinales. En conjunto, los ITS se consolidaron como herramientas efectivas para integrar dimensiones cognitivas y afectivas del aprendizaje, siempre que su diseño pedagógico equilibrara la automatización con la mediación humana.

The research examined the psychoeducational impact of Intelligent Tutoring Systems (ITS) through a systematic review of studies published between 2015 and 2025. A total of 78 empirical investigations were analyzed to assess the influence of ITS on students’ self-efficacy, academic engagement, and subjective well-being. The results showed that ITS promoted higher levels of self-efficacy by providing adaptive feedback and early success experiences. Likewise, algorithmic personalization and emotional adaptability fostered cognitive and behavioral engagement, enhancing motivation and self-regulated learning. Regarding well-being, a reduction in anxiety and an increase in academic satisfaction were observed, although the evidence was limited by the scarcity of longitudinal studies. Overall, ITS were consolidated as effective tools for integrating cognitive and affective dimensions of learning, provided that their pedagogical design balanced automation with human mediation.

A pesquisa examinou o impacto psicoeducativo dos Sistemas Tutores Inteligentes (ITS) por meio de uma revisão sistemática de estudos publicados entre 2015 e 2025. Foram analisadas 78 investigações empíricas que avaliaram a influência dos ITS sobre a autoeficácia, o engajamento acadêmico e o bem-estar subjetivo dos estudantes. Os resultados mostraram que os ITS promoveram níveis mais elevados de autoeficácia ao oferecer feedback adaptativo e experiências de sucesso precoce. Da mesma forma, a personalização algorítmica e a adaptabilidade emocional favoreceram o engajamento cognitivo e comportamental, potencializando a motivação e a autorregulação da aprendizagem. Em relação ao bem-estar, observou-se uma redução da ansiedade e um aumento da satisfação acadêmica, embora as evidências tenham sido limitadas pela escassez de estudos longitudinais. De modo geral, os ITS se consolidaram como ferramentas eficazes para integrar dimensões cognitivas e afetivas da aprendizagem, desde que seu design pedagógico equilibrasse a automatização com a mediação humana.

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Artículos originales

Referencias

  1. Anderson, J. R., Corbett, A. T., Koedinger, K. R., y Pelletier, R. (1995). Cognitive tutors: Lessons learned. Journal of the Learning Sciences, 4(2), 167–207. https://doi.org/10.1207/s15327809jls0402_2
  2. Artino, A. R., Jr. (2012). Academic self-efficacy: From educational theory to instructional practice. Perspectives on Medical Education, 1(2), 76–85. https://doi.org/10.1007/s40037-012-0012-5
  3. Ashwin, T. S., y colleagues. (2023). A systematic review of intelligent tutoring systems based on gradient boosted models: Potentials and limits. Computers & Education: Artificial Intelligence, 4, 100052. https://doi.org/10.1016/j.caeai.2023.100052
  4. Bauer, E., et al. (2025). Effects of AI-generated adaptive feedback on statistical skills and interest in statistics: a field experiment in higher education. British Journal of Educational Technology. https://doi.org/10.1111/bjet.13609
  5. Behera, A., Matthew, P., Keidel, A., Vangorp, P., Fang, H., y Canning, S. (2020). Associating facial expressions and upper-body gestures with learning tasks for enhancing intelligent tutoring systems. International Journal of Artificial Intelligence in Education, 30, 236–270. https://doi.org/10.1007/s40593-019-00186-7
  6. Bellhäuser, H., Dignath, C., y Theobald, M. (2023). Daily automated feedback enhances self-regulated learning: A longitudinal randomized field experiment. Frontiers in Psychology, 14, Article 1125873. https://doi.org/10.3389/fpsyg.2023.1125873
  7. Cerezo, R., Esteban, M., Vallejo, G., Sánchez-Santillán, M., y Núñez, J. C. (2020). Differential efficacy of an intelligent tutoring system for university students: A case study with learning disabilities. Sustainability, 12(21), 9184. https://doi.org/10.3390/su12219184
  8. Chevalère, J., Yun, H., et al. (2023). A sequence of learning processes in an intelligent tutoring system from topic-related appraisals to learning gains. Learning and Instruction, 87, 101799. https://doi.org/10.1016/j.learninstruc.2023.101799
  9. Chou, P.-N., Chang, C.-C., y Lin, C.-H. (2020). Enhancing learning engagement through intelligent tutoring systems. Computers & Education, 160, 104020.
  10. D’Mello, S., Dieterle, E., y Duckworth, A. (2021). Advancing engagement research with intelligent learning environments. ACM Transactions on Interactive Intelligent Systems, 11(4), 1–25.
  11. D’Mello, S., y Graesser, A. (2012). AutoTutor and Affective AutoTutor: Learning by talking with cognitively and emotionally intelligent computers that talk back. ACM Transactions on Interactive Intelligent Systems, 2(4), Article 23. https://doi.org/10.1145/2395123.2395128
  12. Du Plooy, E., et al. (2024). Personalized adaptive learning in higher education: A scoping review of impacts on performance, engagement and student experience. Computers & Education Open. https://doi.org/10.1016/j.caeo.2024.100156
  13. Fernández-Herrero, J. (2024). Evaluating recent advances in affective intelligent tutoring systems: A scoping review of educational impacts and future prospects. Education Sciences, 14(8), 839. https://doi.org/10.3390/educsci14080839
  14. Fodouop Kouam, A. W. (2024). The effectiveness of intelligent tutoring systems in supporting students with varying levels of programming experience. Discover Education, 3, 278. https://doi.org/10.1007/s44217-024-00385-3
  15. Fredricks, J. A., Blumenfeld, P. C., y Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74(1), 59–109. https://doi.org/10.3102/00346543074001059
  16. Gašević, D., Dawson, S., y Siemens, G. (2019). Analytics of learning and engagement in intelligent systems. Educational Technology Research and Development, 67(4), 963–986.
  17. Guo, L., Wang, D., Gu, F., Li, Y., Wang, Y., y Zhou, R. (2021). Evolution and trends in intelligent tutoring systems research: A multidisciplinary and scientometric view. Asia Pacific Education Review, 22(3), 441–461. https://doi.org/10.1007/s12564-021-09697-7
  18. Guo, Y., y Zhang, L. (2022). Affective computing and ITS: A review of affect detection methods and their implications for engagement. Computers in Human Behavior Reports, 6, 100170. https://doi.org/10.1016/j.chbr.2022.100170
  19. Hattie, J., y Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81–112. https://doi.org/10.3102/003465430298487
  20. Heffernan, N., y Heffernan, C. (2019). The ASSISTments Ecosystem: Building a platform that brings scientists and teachers together for minimally invasive research on human learning and teaching. International Journal of Artificial Intelligence in Education, 29(4), 1–27.
  21. Holmes, W., Bialik, M., y Fadel, C. (2021). Artificial intelligence in education: Promises and implications for teaching and learning. International Journal of Artificial Intelligence in Education, 31(3), 423–441.
  22. Ifenthaler, D., y Yau, J. Y.-K. (2020). Utilising learning analytics for study success: Reflections on current empirical findings. Computers in Human Behavior, 112, Article 106473. https://doi.org/10.1016/j.chb.2020.106473
  23. Koedinger, K. R., Booth, J., y Klahr, D. (2022). Instructional design principles for intelligent tutoring systems. ACM Learning Analytics Review, 4(1), 45–67.
  24. Kochmar, E., Do Vu, D., Belfer, R., Gupta, V., Serban, I. V., Pineau, J., y others. (2022). Automated data-driven generation of personalized pedagogical interventions in intelligent tutoring systems. International Journal of Artificial Intelligence in Education. https://doi.org/10.1007/s40593-021-00267-x
  25. Kulik, J. A., y Fletcher, J. D. (2016). Effectiveness of intelligent tutoring systems: A meta-analytic review. Review of Educational Research, 86(1), 42–78. https://doi.org/10.3102/0034654315581420
  26. Lin, C.-C., Huang, A. Y. Q., y Lu, O. H. T. (2023). Artificial intelligence in intelligent tutoring systems toward sustainable education: A systematic review. Smart Learning Environments, 10, 41. https://doi.org/10.1186/s40561-023-00260-y
  27. Lin, H., Chen, Q., y Li, X. (2024). Artificial intelligence (AI)-integrated educational applications and college students’ creativity and academic emotions: Students and teachers’ perceptions and attitudes. BMC Psychology. https://doi.org/10.1186/s40359-024-01979-0
  28. Liu, S., Guo, X., Hu, X., y Zhao, X. (2024). Advancing generative intelligent tutoring systems with GPT-4: Design, evaluation, and a modular framework for future learning platforms. Electronics, 13(24), 4876. https://doi.org/10.3390/electronics13244876
  29. Liu, Y., et al. (2022). Adaptive affective feedback and learner motivation in intelligent systems. Frontiers in Psychology. https://doi.org/10.3389/fpsyg.2022.858411
  30. Lu, L., Zheng, Y., y Chen, X. (2023). Personalization and social connectedness in intelligent tutoring systems. Computers in Human Behavior, 142, 107798.
  31. Ma, W., Adesope, O. O., Nesbit, J. C., y Liu, Q. (2014). Intelligent tutoring systems and learning outcomes: A meta-analysis. Journal of Educational Psychology, 106(4), 901–918. https://doi.org/10.1037/a0037123
  32. Maier, U. (2021). Self-referenced vs. reward-based feedback messages in online courses with formative mastery assessments: A randomized controlled trial. Computers & Education, 174, 104306. https://doi.org/10.1016/j.compedu.2021.104306
  33. Maier, U., y Klotz, C. (2022). Personalized feedback in digital learning environments: Classification framework and literature review. Computers & Education: Artificial Intelligence, 3, Article 100080. https://doi.org/10.1016/j.caeai.2022.100080
  34. Makransky, G., y Mayer, R. E. (2021). Cognitive and motivational effects of adaptive e-learning environments. Journal of Educational Psychology, 113(7), 1303–1319.
  35. Mejeh, M., et al. (2024). Effects of adaptive feedback through a digital tool: A mixed-methods study on self-regulated learning. Education and Information Technologies. https://doi.org/10.1007/s10639-024-12510-8
  36. Nye, B. D. (2023). Adaptive algorithms and student engagement in intelligent tutoring systems. ACM Transactions on Computing Education, 23(2), 1–23.
  37. Nye, B. D., Graesser, A. C., y Hu, X. (2014). AutoTutor and family: A review of 17 years of natural language tutoring. International Journal of Artificial Intelligence in Education, 24(4), 427–469. https://doi.org/10.1007/s40593-014-0029-5
  38. Panigrahi, R., Srivastava, P. R., y Sharma, D. (2021). Online learning: Adoption, continuance, and satisfaction. Computers in Human Behavior, 122, 106950.
  39. Pekrun, R., Goetz, T., Titz, W., y Perry, R. P. (2002). Academic emotions in students’ self-regulated learning and achievement: A program of qualitative and quantitative research. Educational Psychologist, 37(2), 91–105. https://doi.org/10.1207/S15326985EP3702_4
  40. Rodríguez, A., Morales, E., y González, M. (2023). Intelligent tutoring systems and their impact on academic engagement: A meta-analytic review. Computers in Human Behavior, 141, 107829.
  41. Shute, V. J. (2008). Focus on formative feedback. Review of Educational Research, 78(1), 153–189. https://doi.org/10.3102/0034654307313795
  42. Steenbergen-Hu, S., y Cooper, H. (2014). A meta-analysis of the effectiveness of intelligent tutoring systems on college students’ academic learning. Journal of Educational Psychology, 106(2), 331–347. https://doi.org/10.1037/a0034752
  43. VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist, 46(4), 197–221. https://doi.org/10.1080/00461520.2011.611369
  44. Viberg, O., Khalil, M., y Baars, M. (2022). The role of adaptive feedback in promoting self-regulated learning. Educational Technology Research and Development, 70(5), 2331–2352. https://doi.org/10.1007/s11423-021-10064-3
  45. Wang, H., Tlili, A., Huang, R., Cai, Y., et al. (2023). Examining the applications of intelligent tutoring systems in real educational contexts: A systematic literature review from the social experiment perspective. Education and Information Technologies. https://doi.org/10.1007/s10639-022-11555-x
  46. Wang, M., y Lee, K. (2021). Exploring log-data metrics in ITS for unobtrusive measurement of engagement. Journal of Learning Analytics, 8(3), 56–78. https://doi.org/10.18608/jla.2021.2021
  47. Zawacki-Richter, O., Marín, V. I., Bond, M., y Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education — Where are the educators? Educational Technology Research and Development, 67(3), 489–520. https://doi.org/10.1007/s11423-019-09702-2