Aplicación de Modelos de Inteligencia Artificial en Pruebas Estandarizadas para la Optimización del Rendimiento Académico en Educación Superior
DOI:
https://doi.org/10.31637/epsir-2024-1605Palabras clave:
Inteligencia Artificial, rendimiento escolar, prueba de conocimiento, educación superior, aprendizaje, calidad de la educación, revisión, evaluaciónResumen
Introducción: Aunque, la IA ha demostrado potencial para predecir resultados académicos, diseñar programas de aprendizaje personalizados y apoyar la orientación académica, se encuentran desafíos significativos como la necesidad de datos de alta calidad, problemas de interpretabilidad de algunos modelos y el riesgo de perpetuar sesgos existentes. El objetivo de la presente revisión sistemática es explorar el uso de la inteligencia artificial en el ámbito educativo, específicamente en el contexto de las pruebas estandarizadas. Metodología: Para ello, se lleva a cabo una revisión exhaustiva de la literatura científica siguiendo las directrices de la declaración PRISMA, con una muestra de 17 artículos publicados entre el 2019 y 2023 en revistas indexadas en Scopus. Resultados: Se encontró que los modelos predictivos más utilizados en los estudios fueron: Redes Neuronales Artificiales, Árboles de Decisión, Máquinas de Soporte Vectorial (SVM) y Random Forest, Discusión: identificando beneficios que incluyen la optimización del rendimiento académico, individualización del aprendizaje y mejora en la toma de decisiones educativas. Conclusiones: Se concluye que la IA tiene un gran potencial para mejorar la medición de la calidad educativa, pero es crucial abordar estas limitaciones y consideraciones éticas para garantizar su aplicación efectiva y responsable en el ámbito educativo.
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