Intervención académica proactiva mediante agentes inteligentes y permanencia de estudiantes universitarios en modalidad en línea: mediación serial de la participación y el desempeño académico
DOI:
https://doi.org/10.65415/tca65x09Palabras clave:
agentes inteligentes; permanencia estudiantil; educación en línea; participación académica; analítica del aprendizajeResumen
La permanencia en la educación superior en línea depende menos de detectar el riesgo que de convertirlo, a tiempo, en apoyo pedagógico pertinente. Este artículo analiza cómo una intervención académica proactiva mediante agentes inteligentes podría incidir en la permanencia estudiantil a través de una cadena de mediación serial: participación académica y desempeño. Se realizó una revisión integrativa crítica de 30 fuentes académicas y normativas verificadas, con énfasis en publicaciones de 2022–2026, sobre analítica del aprendizaje, agentes educativos, participación, rendimiento, retención y gobernanza de inteligencia artificial. La síntesis distingue al agente proactivo del chatbot reactivo: el primero monitorea señales autorizadas del entorno virtual, identifica cambios de trayectoria, inicia apoyos contextualizados, adapta su actuación y deriva casos sensibles a tutores humanos. Los resultados conceptuales sostienen que la oportunidad y personalización de la intervención pueden estimular conductas de participación; esa participación amplía las oportunidades de aprendizaje y favorece el desempeño; y el desempeño, junto con la integración académica, reduce la intención de abandono. Se propone un modelo contrastable X→M1→M2→Y, cinco hipótesis y un esquema de implementación con supervisión humana, minimización de datos, explicabilidad y auditoría de equidad. La evidencia disponible respalda la plausibilidad del mecanismo, pero no demuestra todavía el efecto causal de la cadena completa. La contribución es una arquitectura teórica y metodológica para evaluarla longitudinalmente sin confundir predicción de riesgo con intervención efectiva
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Atalla, S., Daradkeh, M., Gawanmeh, A., Khalil, H., Mansoor, W., Miniaoui, S., & Himeur, Y. (2023). An intelligent recommendation system for automating academic advising based on curriculum analysis and performance modeling. Mathematics, 11(5), 1098. https://doi.org/10.3390/math11051098
Banihashem, S. K., Noroozi, O., van Ginkel, S., Macfadyen, L. P., & Biemans, H. J. A. (2022). A systematic review of the role of learning analytics in enhancing feedback practices in higher education. Educational Research Review, 37, 100489. https://doi.org/10.1016/j.edurev.2022.100489
Bean, J. P., & Metzner, B. S. (1985). A conceptual model of nontraditional undergraduate student attrition. Review of Educational Research, 55(4), 485–540. https://doi.org/10.3102/00346543055004485
Bergdahl, N., Bond, M., Sjöberg, J., Dougherty, M., & Oxley, E. (2024). Unpacking student engagement in higher education learning analytics: A systematic review. International Journal of Educational Technology in Higher Education, 21, 63. https://doi.org/10.1186/s41239-024-00493-y
Bilquise, G., Ibrahim, S., & Salhieh, S. M. (2024). Investigating student acceptance of an academic advising chatbot in higher education institutions. Education and Information Technologies, 29(5), 6357–6382. https://doi.org/10.1007/s10639-023-12076-x
Chen, Y., Jensen, S., Albert, L. J., Gupta, S., & Lee, T. (2023). Artificial intelligence (AI) student assistants in the classroom: Designing chatbots to support student success. Information Systems Frontiers, 25(1), 161–182. https://doi.org/10.1007/s10796-022-10291-4
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
Hayes, A. F. (2022). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach (3rd ed.). Guilford Press.
Herodotou, C., Rienties, B., Hlosta, M., Boroowa, A., Mangafa, C., & Zdrahal, Z. (2020). The scalable implementation of predictive learning analytics at a distance learning university: Insights from a longitudinal case study. The Internet and Higher Education, 45, 100725. https://doi.org/10.1016/j.iheduc.2020.100725
Heung, Y. M. E., & Chiu, T. K. F. (2025). How ChatGPT impacts student engagement from a systematic review and meta-analysis study. Computers and Education: Artificial Intelligence, 8, 100361. https://doi.org/10.1016/j.caeai.2025.100361
Jin, Y., Yang, K., Martinez-Maldonado, R., Gašević, D., & Yan, L. (2026). The agency gap in AI-supported writing: How reactive and proactive agent designs shape multimodal reasoning. Computers and Education: Artificial Intelligence, 11, 100655. https://doi.org/10.1016/j.caeai.2026.100655
Kahu, E. R., & Nelson, K. (2018). Student engagement in the educational interface: Understanding the mechanisms of student success. Higher Education Research & Development, 37(1), 58–71. https://doi.org/10.1080/07294360.2017.1344197
Kamalov, F., Calonge, D. S., Smail, L., Azizov, D., Thadani, D. R., Kwong, T., & Atif, A. (2025). Evolution of AI in education: Agentic workflows. arXiv. https://doi.org/10.48550/arXiv.2504.20082
Karaoğlan Yılmaz, F. G., & Yılmaz, R. (2022). Learning analytics intervention improves students’ engagement in online learning. Technology, Knowledge and Learning, 27, 449–460. https://doi.org/10.1007/s10758-021-09547-w
Kuhail, M. A., Alturki, N., Alramlawi, S., & Alhejori, K. (2023). Interacting with educational chatbots: A systematic review. Education and Information Technologies, 28, 973–1018. https://doi.org/10.1007/s10639-022-11177-3
Liu, Y., Wang, W., & Xu, E. (2025). The effectiveness of learning analytics-based interventions in enhancing students’ learning effect: A meta-analysis of empirical studies. SAGE Open, 15(2). https://doi.org/10.1177/21582440251336707
Nurshatayeva, A., Page, L. C., White, C., & Gehlbach, H. (2021). Are artificially intelligent conversational chatbots uniformly effective in reducing summer melt? Evidence from a randomized controlled trial. Research in Higher Education, 62, 392–402. https://doi.org/10.1007/s11162-021-09633-z
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, 100991. https://doi.org/10.1016/j.iheduc.2024.100991
Ramaswami, G., Susnjak, T., & Mathrani, A. (2023). Effectiveness of a learning analytics dashboard for increasing student engagement levels. Journal of Learning Analytics, 10(3), 115–134. https://doi.org/10.18608/jla.2023.7935
Rets, I., Herodotou, C., & Gillespie, A. (2023). Six practical recommendations enabling ethical use of predictive learning analytics in distance education. Journal of Learning Analytics, 10(1), 149–167. https://doi.org/10.18608/jla.2023.7743
Rotar, O. (2022). Online student support: A framework for embedding support interventions into the online learning cycle. Research and Practice in Technology Enhanced Learning, 17, 2. https://doi.org/10.1186/s41039-021-00178-4
Sudarshan, V. K., Sisodia, A., Ramachandra, R. A., Batra, S., & Leng, J. C. L. (2026). Agentic AI ecosystems in higher education: A perspective on AI agents to emerging inclusive, agentic multi-agent AI framework for learning, teaching and institutional intelligence. arXiv. https://doi.org/10.48550/arXiv.2605.14266
Susnjak, T., Ramaswami, G. S., & Mathrani, A. (2022). Learning analytics dashboard: A tool for providing actionable insights to learners. International Journal of Educational Technology in Higher Education, 19, 12. https://doi.org/10.1186/s41239-021-00313-7
Tamascelli, M., Bunch, O., Fowler, B., Taeb, M., & Cohen, A. (2025). Academic advising chatbot powered with AI agent. In Proceedings of the 2025 ACM Southeast Conference (pp. 195–202). Association for Computing Machinery. https://doi.org/10.1145/3696673.3723065
Tinto, V. (1975). Dropout from higher education: A theoretical synthesis of recent research. Review of Educational Research, 45(1), 89–125. https://doi.org/10.3102/00346543045001089
UNESCO. (2023). Guidance for generative AI in education and research. https://unesdoc.unesco.org/ark:/48223/pf0000386693
UNESCO. (2024). AI competency framework for students. https://unesdoc.unesco.org/ark:/48223/pf0000391105
UNESCO. (2025). AI and education: Protecting the rights of learners. https://unesdoc.unesco.org/ark:/48223/pf0000395373
Yildirim, D., & Gülbahar, Y. (2022). Implementation of learning analytics indicators for increasing learners’ final performance. Technology, Knowledge and Learning, 27, 479–504. https://doi.org/10.1007/s10758-021-09583-6
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Derechos de autor 2026 RODRIGO JOSE PAZMIÑO PEREZ, FRANCISCO PAOLO ESPINEL OBREGOSO, Yajaira Natali Luque Soriano, CRISTHIAN LEON PEREZ, ELISA FLOR GUAMAN TROYA

Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.








