Uso de la inteligencia artificial en estrategias de repetición espaciada para la educación médica y el aprendizaje significativo: revisión sistemática.
Medical education faces the challenge of managing large amounts of information while preventing superficiallearning. Spaced repetition, grounded in the forgetting curve, strengthens long-term retention and promotesmeaningful learning. Its integration with artificial intelligence (AI) enables persona...
| Autores: | , , |
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| Formato: | artículo |
| Fecha de publicación: | 2025 |
| País: | España |
| Recursos: | Universidad de Murcia |
| Repositorio: | DIGITUM. Depósito Digital Institucional de la Universidad de Murcia |
| OAI Identifier: | oai:digitum.um.es:10201/202621 |
| Acesso em linha: | https://doi.org/10.6018/edumed.685191 http://hdl.handle.net/10201/202621 |
| Access Level: | acceso abierto |
| Palavra-chave: | Educación médica Inteligencia artificial Aprendizaje significativo Spaced repetition Medical education Artificial intelligence Meaningful learning Repetición espaciada No relacionado con ningún objetivo de desarrollo sostenible |
| Resumo: | Medical education faces the challenge of managing large amounts of information while preventing superficiallearning. Spaced repetition, grounded in the forgetting curve, strengthens long-term retention and promotesmeaningful learning. Its integration with artificial intelligence (AI) enables personalized review intervals,automated generation of learning materials, and immediate feedback, thereby expanding the pedagogicalpotential of this strategy. Objective: To evaluate the effectiveness and applicability of AI-assisted spacedrepetition in Health Sciences education. Methods: A descriptive systematic review was conducted in accordancewith PRISMA 2020. Searches were performed in Google Scholar and Web of Science (2020–2025) using the terms“spaced repetition,” “medical education,” “learning,” and “artificial intelligence.” Original studies, reviews, andapplied reports addressing spaced repetition with or without AI were included. From 1870 initial records, 18 studies met the inclusion criteria and were analyzed qualitatively. Results: Direct evidence showed that AIenhances the personalization of review intervals, improves feedback quality, and supports knowledgeconsolidation. Indirect evidence confirmed the effectiveness of traditional spaced repetition, with sustainedbenefits in academic performance and memory in standardized examinations. Complementary evidencehighlighted that AI strengthens other educational processes, such as automated tutoring, clinical simulation, andmicrolearning. Conclusions: AI-assisted spaced repetition represents an innovative pedagogical strategyaligned with competency-based medical education. It facilitates personalized learning, strengthens retention,and promotes student autonomy. However, methodological limitations in the available studies highlight theneed for longitudinal and multicenter research to assess its educational and clinical impact, along with ethicalstrategies to ensure equity and human verification in the use of these technologies. |
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