Inteligencia artificial aplicada a la investigación científica universitaria en los procesos de enseñanza y aprendizaje. Una revisión sistemática
DOI:
https://doi.org/10.26820/reciamuc/9.(4).diciembre.2025.155-177Keywords:
Inteligencia artificial (IA), Educación superior, Revisión sistemática, Gobernanza algorítmica, Procesos de enseñanza-aprendizajeAbstract
La rápida expansión de la Inteligencia Artificial (IA) exige una comprensión sistemática de su impacto en la educación superior, lo que hace urgente investigar sus implicaciones en la investigación científica y los procesos de enseñanza-aprendizaje universitarios. El objetivo de este estudio fue analizar y sintetizar la evidencia científica más reciente (2020-2025) sobre la aplicación de la IA en este contexto, identificando bases teóricas, tendencias, beneficios y desafíos. Se ejecutó una revisión sistemática de la literatura siguiendo las directrices PRISMA 2020, examinando artículos originales de bases de datos de alto impacto. Los resultados revelan que la implementación de la IA se basa en el Aprendizaje Adaptativo y se materializa en Sistemas de Tutoría Inteligente (ITS) y en herramientas de IA Generativa, reportando un aumento en la eficiencia del aprendizaje y la calidad de la producción científica. La conclusión principal subraya que, si bien la IA es un motor de transformación y eficiencia, su avance está críticamente limitado por la falta de gobernanza algorítmica y los desafíos éticos relacionados con el sesgo y la privacidad de los datos, lo que requiere un foco institucional en marcos de responsabilidad y equidad.
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