Nivelación inteligente: estrategia de aprendizaje adaptativo con IA en educación superior
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https://doi.org/10.5944/ried.45482Palabras clave:
aprendizaje adaptativo, inteligencia artificial, nivelación, educación superiorResumen
La nivelación académica es uno de los principales retos de la educación superior, particularmente en las disciplinas STEM, un desafío que se ha intensificado tras el confinamiento académico y ante el cual los cursos remediales tradicionales no han mostrado los resultados esperados. Este estudio evaluó el impacto de una estrategia de aprendizaje adaptativo (EAA) orientada a reforzar conocimientos previos en 1.309 estudiantes de primer año inscritos en las unidades formativas de Pensamiento Computacional, Modelación Matemática Fundamental, Razonamiento Matemático y Pensamiento Matemático de una universidad privada en México. A diferencia de los enfoques remediales convencionales, la EAA ofrece una experiencia de aprendizaje flexible y centrada en el estudiante mediante breves módulos adaptativos integrados en los cursos regulares. Estos módulos, respaldados por una plataforma de inteligencia artificial, generan rutas personalizadas de aprendizaje, adaptan contenidos, producen analíticas y facilitan la toma de decisiones docentes basadas en datos. La investigación adoptó un enfoque mixto (CUAN > CUAL) y un diseño cuasiexperimental con muestras emparejadas para asegurar la comparabilidad entre grupos. Los resultados mostraron diferencias estadísticamente significativas en el desempeño académico a favor de los grupos experimentales respecto a los grupos control. Además, estudiantes y profesores evaluaron positivamente la EAA en términos de utilidad y experiencia de aprendizaje. Los hallazgos sugieren que una estrategia adaptativa, integral y mediada por tecnología constituye una alternativa eficaz para abordar la nivelación académica en cursos universitarios iniciales, ofreciendo ventajas sustanciales sobre los métodos remediales tradicionales.
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Derechos de autor 2026 Patricia Aldape-Valdés, Elvira G Rincón-Flores, Leticia Castaño, Sadie Guerrero
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