Desarrollo y validación de la escala de Aprendizaje Autorregulado con IA (AI-SRL)
DOI:
https://doi.org/10.5944/ried.45452Palabras clave:
IA, AIED, aprendizaje autorregulado, AI-SRL, escala AI-SRLResumen
La integración de la Inteligencia Artificial (IA) en entornos educativos brinda nuevas oportunidades para mejorar el aprendizaje autorregulado (SRL) en los estudiantes. Sin embargo, las herramientas actuales carecen de precisión para evaluar cómo la IA influye en las capacidades autorregulatorias de los estudiantes. Este estudio presenta la Escala de Aprendizaje Autorregulado Mejorado por IA (AI-SRL), diseñada para evaluar el aprendizaje autorregulado en entornos educativos asistidos por IA. A través de un riguroso proceso de desarrollo de escala, que incluyó revisión de literatura, consulta con expertos y validación sistemática, se construyó un instrumento de medición integral. En el desarrollo se empleó un enfoque de validación en dos fases con muestras separadas para análisis factoriales exploratorios y confirmatorios. La escala final validada consta de 22 ítems organizados en cinco dimensiones: Competencia en IA, Conciencia de Aprendizaje, Estrategias de Aprendizaje, Implicación y Eficiencia, y Colaboración Ética. Estos factores capturan la naturaleza multifacética de cómo los estudiantes regulan su aprendizaje al usar herramientas de IA, desde competencia técnica hasta consideraciones éticas. La escala demostró sólidas propiedades psicométricas, con excelente consistencia interna y robusta validez de constructo. Este instrumento validado tiene aplicaciones prácticas para docentes que buscan optimizar la integración de IA en sus aulas, investigadores que investigan la intersección de IA y aprendizaje autorregulado, e instituciones desarrollando currículos mejorados con IA. La Escala AI-SRL proporciona un marco confiable para evaluar en qué medida los estudiantes aprovechan las herramientas de IA mientras mantienen sus capacidades autorregulatorias, contribuyendo así a una implementación de IA más efectiva y responsable.
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Derechos de autor 2026 Mehmet Ali Ayaz, Fatih Karataş, Erkan Yüce
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