Inteligencia artificial generativa en la evaluación del aprendizaje inclusivo: oportunidades, sesgos y desafíos para estudiantes con discapacidad
DOI:
https://doi.org/10.68503/13hz4r49Keywords:
inteligencia artificial generativa, evaluación inclusiva, discapacidad, educación superior, sesgo algorítmicoAbstract
La inteligencia artificial generativa puede ampliar los formatos de participación y retroalimentación, pero también introducir barreras, sesgos y riesgos en la evaluación inclusiva. El objetivo fue analizar oportunidades, accesibilidad, bajo sesgo, condiciones de implementación y evaluación inclusiva percibida, contrastando estudiantes con discapacidad y docentes universitarios. Se realizó un estudio observacional, transversal-retrospectivo y de métodos mixtos con una base anonimizada declarada como real por el cliente. Se analizaron 350 registros: 187 estudiantes y 163 docentes, recopilados entre 2022 y 2024. Se calcularon estadísticos descriptivos, alfa de Cronbach, correlaciones de Spearman y comparaciones U de Mann–Whitney con ajuste de Holm; las respuestas abiertas se clasificaron mediante reglas temáticas reproducibles. Las escalas mostraron consistencia interna alta (α=0,87–0,91). Oportunidades obtuvo las medias más altas en estudiantes (M=3,87) y docentes (M=3,86), mientras implementación presentó los valores más bajos (M=3,31 y M=3,39). Las cuatro dimensiones predictoras se correlacionaron positiva y significativamente con la evaluación inclusiva en ambos grupos (ρ=0,35–0,50; p<0,001). Las diferencias entre grupos no permanecieron significativas tras el ajuste de Holm. Se concluye que el potencial inclusivo depende de accesibilidad verificable, supervisión humana, control de sesgos, privacidad y capacidad institucional.
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