Uso de la inteligencia artificial en estrategias de repetición espaciada para la educación médica y el aprendizaje significativo: revisión sistemática.
Resumo
La educación médica enfrenta el reto de gestionar grandes volúmenes de información y prevenir el aprendizaje superficial. La repetición espaciada, basada en la curva del olvido, fortalece la retención a largo plazo y favorece el aprendizaje significativo. Su integración con la inteligencia artificial (IA) permite personalizar los intervalos de repaso, automatizar la generación de materiales y ofrecer retroalimentación inmediata, ampliando el potencial pedagógico de esta estrategia. Objetivo: Evaluar la efectividad y aplicabilidad de la repetición espaciada asistida por IA en la docencia de Ciencias de la Salud. Métodos: Se realizó una revisión sistemática descriptiva conforme a PRISMA 2020. La búsqueda se llevó a cabo en Google Scholar y Web of Science (2020–2025) utilizando los términos “spaced repetition”, “medical education”, “learning” y “artificial intelligence”. Se incluyeron estudios originales, revisiones y reportes aplicados que abordaran la repetición espaciada con o sin IA. De 1870 registros iniciales, 18 estudios cumplieron los criterios de inclusión y fueron analizados cualitativamente. Resultados: La evidencia directa mostró que la IA mejora la personalización de los intervalos de repaso, la calidad de la retroalimentación y la consolidación del conocimiento. La evidencia indirecta confirmó la eficacia de la repetición espaciada tradicional, con beneficios sostenidos en rendimiento académico y memoria en exámenes estandarizados. La evidencia complementaria destacó que la IA potencia otros procesos formativos, como la tutoría automatizada, la simulación clínica y el microaprendizaje. Conclusiones: la repetición espaciada asistida por IA representa una estrategia pedagógica innovadora y coherente con la educación médica basada en competencias. Facilita la personalización del aprendizaje, fortalece la retención y promueve la autonomía estudiantil. Sin embargo, las limitaciones metodológicas de los estudios disponibles subrayan la necesidad de investigaciones longitudinales y multicéntricas que evalúen su impacto educativo y clínico, e incorporen estrategias éticas que garanticen la equidad y la verificación humana en el uso de estas tecnologías.
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