Pedagogía Aumentada por Inteligencia Artificial en la Educación Superior
Un Marco Ético y Equitativo para la Gobernanza y la Integración Operativa de la IA en la Enseñanza y el Aprendizaje
DOI:
https://doi.org/10.55420/2693.9193.v16.n2.369Palabras clave:
inteligencia artificial, pedagogía aumentada por IA, sistemas de aprendizaje adaptativo, andamiaje metacognitivo, rendición de cuentas algorítmica, equidad educativa, fluidez epistémica en IA, educación superiorResumen
La educación superior enfrenta una convergencia de presiones que ninguna respuesta institucional aislada ha logrado resolver: una creciente diversidad demográfica, la fragilidad estructural expuesta por la pandemia y la persistente inequidad en cuanto a quién recibe apoyo para aprender y de qué manera. La inteligencia artificial ha ingresado en este escenario no como una mejora opcional, sino como una realidad infraestructural —integrada en plataformas adaptativas, sistemas de retroalimentación y protocolos de alerta temprana en instituciones de todo el mundo—. Sin embargo, la adopción ha superado sistemáticamente a la gobernanza, y las herramientas celebradas por democratizar el acceso conllevan riesgos reales de codificar y amplificar las inequidades que prometen abordar. Este artículo presenta el marco de Pedagogía Aumentada por Inteligencia Artificial (PAIA), desarrollado mediante un diseño secuencial explicativo de métodos mixtos, fundamentado en una síntesis sistemática de 312 estudios empíricos revisados por pares, 68 documentos de política institucional y 26 fuentes de literatura gris (literatura no convencional). El marco está teóricamente fundamentado y empíricamente informado; sus afirmaciones causales directas requieren validación prospectiva. Siete pilares estructuran el marco: Simbiosis Cognitiva, Modulación Cognitiva Dinámica, Inteligencia Interpretable, Síntesis Transdisciplinaria, Andamiaje Metacognitivo, Cognición Moral Incorporada y Conocimiento Generativo con Co-creación Crítica. Los estudios sintetizados señalan efectos de retroalimentación adaptativa mediada por IA con tamaños de efecto de d = 0,40–0,76, ganancia promedio de retención, a través de las distintas modalidades, de 15 puntos porcentuales, y una reducción aproximada del 38 % del tiempo instruccional rutinario. Entre las contribuciones originales se incluyen un Modelo de Progresión del Desarrollo, un mandato de transparencia de espectro completo, un protocolo de evaluación de herramientas alineado con HECVAT y un Índice de Fluidez Epistémica en IA propuesto para facilitar la comparación entre estudios.
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