Inteligencia artificial generativa en la educación superior: Análisis empírico y modelado predictivo con Python
Palabras clave:
Inteligencia Artificial Generativa, Educación Superior, Percepciones Estudiantiles, Pragmatismo Crítico, Machine LearningSinopsis
Este estudio aborda la brecha crítica entre la rápida y masiva adopción de la inteligencia artificial generativa por parte de los estudiantes y la lenta capacidad de respuesta de las instituciones de educación superior. Para informar el desarrollo de políticas y estrategias pedagógicas efectivas, se realizó una investigación cuantitativa mediante una encuesta validada, aplicada a una muestra de 474 estudiantes universitarios en Ecuador. El análisis de datos combinó estadísticas descriptivas e inferenciales con técnicas de machine learning en Python, incluyendo el algoritmo de agrupamiento K-Means para la segmentación de perfiles y modelos de clasificación como Random Forest para la predicción. Los resultados identifican el perfil dominante del "pragmático crítico", un usuario que valora los beneficios de la IA mientras es agudamente consciente de sus riesgos, y segmentan la población en tres arquetipos (Escéptico, Pragmático Crítico y Tecno-Optimista). Además, se desarrolló un modelo que predice la intención de uso a largo plazo con un 88.1% de exactitud. Se concluye que el estudiantado expresa una demanda clara y transversal por una mayor guía institucional, recomendando a las universidades transitar de una postura reactiva a una estrategia proactiva que fomente un uso crítico y ético.
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25 November 2025
Derechos de autor 2025 Espinoza-Mina, Marcos Antonio, Colina-Vargas, Alejandra Mercedes
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Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.
