From Opacity to Clarity: Demystifying Machine Learning Decisions in Education with Explainable AI

Autores/as

Palabras clave:

Artificial intelligence, explainable artificial intelligence, machine learning, education

Resumen

La adopción de algoritmos de aprendizaje automático (ML) en la educación es cada vez mayor, con el objetivo de mejorar los procesos de enseñanza, aprendizaje y administración. Estos algoritmos son cruciales para el aprendizaje personalizado, la predicción del rendimiento de los alumnos y el diseño de planes de estudio. Sin embargo, su uso generalizado puede plantear problemas como la parcialidad, la falta de transparencia y la dependencia excesiva de las decisiones automatizadas. A menudo, los educadores necesitan ayuda para comprender el funcionamiento interno de los modelos de ML. Este artículo examina la IA explicable (XAI) como solución a estos problemas en la educación. Las técnicas de XAI pueden proporcionar a los educadores y administradores información valiosa sobre los algoritmos de ML, facilitando una toma de decisiones más informada. Discutimos la diferencia entre opciones algorítmicas transparentes y opacas y demostramos los beneficios tangibles de la XAI en la educación. Los modelos transparentes permiten a los educadores aprovechar su experiencia de forma eficaz, descubrir patrones ocultos y mejorar los resultados de los estudiantes.

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Biografía del autor/a

Wellington Rodrigo Monteiro, Universidade Positivo

Ph.D. in Industrial and Systems Engineering from PUCPR (Pontifical Catholic University of Parana), a Master's in Industrial and Systems Engineering from PUCPR, and a Bachelor's in Computer Engineering from PUCPR. He has over ten years of experience working as a data scientist in large international corporations and startups. His interests are rooted in the adoption and perception of artificial intelligence inside organizations.

Eduardo Ayrosa, Universidade Positivo - UP

Ph.D. in Management from the London Business School (University of London), a Master's degree in Management from UFRJ (Federal University of Rio de Janeiro), and a Bachelor's in Civil Engineering from the UFRJ. Specializing in Management, his emphasis lies in Consumer Studies, Marketing, and Epistemology and Research Methodology. In the realm of Epistemology and Research Methodology, his interests are rooted in the philosophy of social sciences and interpretative research methods.

Citas

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Publicado

2024-09-07

Cómo citar

MONTEIRO, W. R.; AYROSA, E. From Opacity to Clarity: Demystifying Machine Learning Decisions in Education with Explainable AI. REVISTA INTERSABERES, [S. l.], v. 19, p. e24en5002, 2024. Disponível em: https://revistasuninter.com/intersaberes/index.php/revista/article/view/2661. Acesso em: 21 nov. 2024.

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v. 19 (2024): Revista Intersaberes

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