Aprendizaje automático e inteligencia artificial generativa en la analítica del aprendizaje universitario: aplicaciones, tendencias y desafíos desde una revisión narrativa
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https://doi.org/10.63969/hw5sbz05Palabras clave:
analítica del aprendizaje, aprendizaje automático, inteligencia artificial generativa, educación superior, ética algorítmicaResumen
Introducción: La analítica del aprendizaje universitario ha evolucionado desde la descripción de registros digitales hacia modelos predictivos y sistemas generativos capaces de interpretar y comunicar información educativa. Su expansión plantea oportunidades pedagógicas y riesgos éticos. Metodología: Se desarrolló una revisión narrativa de literatura científica sobre aprendizaje automático, inteligencia artificial generativa y analítica del aprendizaje en educación superior. La síntesis documental se organizó en fundamentos conceptuales, aplicaciones, tendencias, desafíos y condiciones de adopción institucional, sin efectuar metaanálisis ni atribuir resultados experimentales propios. Resultados: La literatura describe aplicaciones del aprendizaje automático para predecir desempeño, identificar riesgo de abandono, caracterizar participación y apoyar decisiones tutoriales. La inteligencia artificial generativa amplía las posibilidades de resumir interacciones, interpretar discursos y producir retroalimentación contextualizada; sin embargo, sus resultados requieren validación y supervisión humana. Se identifican limitaciones relacionadas con calidad y representatividad de datos, sesgos algorítmicos, explicabilidad, privacidad, autonomía estudiantil e integración curricular. Los tableros y las alertas adquieren utilidad cuando se articulan con intervenciones pedagógicas oportunas y evaluables. Conclusiones: La combinación de aprendizaje automático e inteligencia artificial generativa puede fortalecer una analítica orientada al aprendizaje, siempre que se prioricen la pertinencia pedagógica, la transparencia, la protección de datos y la evaluación de impactos reales. Se necesitan investigaciones longitudinales y contextualizadas para establecer sus beneficios educativos y sus efectos distributivos.
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Derechos de autor 2026 Luis Iván Porras Ramirez (Autor/a)
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
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