Inteligencia artificial generativa para la enseñanza de matemáticas y programación en educación superior: Revisión sistemática
Palabras clave:
Inteligencia artificial generativa, ChatGPT, educación superior, enseñanza de matemáticas, enseñanza de programación.
Resumen
La rápida expansión de la inteligencia artificial generativa ha despertado un creciente interés en su aplicación educativa, especialmente en áreas que exigen razonamiento lógico y abstracción. El objetivo de esta revisión sistemática fue analizar el impacto de la inteligencia artificial generativa en el aprendizaje de estudiantes universitarios, así como en la preparación de clases por parte de los docentes. Se aplicaron las directrices PRISMA, definiendo criterios de inclusión y exclusión con base a preguntas PICO y PCC. La búsqueda en Scopus, Web of Science e IEEE Xplore permitió analizar 710 registros, de los cuales 67 cumplieron con los criterios establecidos. Los resultados muestran una literatura dominada por estudios descriptivos y con predominio de ChatGPT, donde se reportan beneficios en motivación y productividad, aunque sin evidencias concluyentes de mejoras en el rendimiento académico. La investigación se concentra en pocos países con ausencia de producción latinoamericana. En conclusión, el uso de inteligencia artificial generativa ofrece un potencial pedagógico prometedor, pero su efectividad depende de la integración crítica con la labor docente y de la generación de estudios experimentales más robustos que permitan establecer evidencias sólidas y generalizables.
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https://doi.org/10.48550/arXiv.2308.04309
Publicado
2026-05-11
Cómo citar
Erazo-Arteaga, V. A., Vacas Palacios, S. M., & Cisneros Rúales, M. B. (2026). Inteligencia artificial generativa para la enseñanza de matemáticas y programación en educación superior: Revisión sistemática. Revista De Ciencias Sociales, 32(2), 320-340. https://doi.org/10.31876/rcs.v32i2.45578
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Esta obra está bajo licencia internacional Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0.
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