Da opacidade à clareza: desmistificando o aprendizado de máquina na educação com IA explicável

Autores

Palavras-chave:

inteligência artificial,, inteligência artificial explicável,, aprendizado de máquina, educação.

Resumo

A adoção de algoritmos de aprendizado de máquina (ML) na educação está aumentando, com o objetivo de aprimorar os processos de ensino, aprendizado e administração. Esses algoritmos são cruciais para a aprendizagem personalizada, a previsão do desempenho dos alunos e a elaboração de ementas. No entanto, seu uso generalizado pode levar a desafios como parcialidade, falta de transparência e dependência excessiva de decisões automatizadas. Os educadores precisam geralmente de ajuda para entender o funcionamento interno dos modelos de ML. Este artigo examina a IA explicável (XAI) como uma solução para esses problemas na educação. As técnicas de XAI podem fornecer aos educadores e administradores percepções valiosas sobre os algoritmos de ML, facilitando a tomada de decisões mais informadas. Discutimos a diferença entre escolhas algorítmicas transparentes e opacas, demonstrando os benefícios tangíveis da XAI na educação. Os modelos transparentes permitem que os educadores aproveitem seus conhecimentos de forma eficaz, descubram padrões ocultos e melhorem os resultados dos alunos.

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Biografia do Autor

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.

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2024-09-07

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MONTEIRO, W. R.; AYROSA, E. Da opacidade à clareza: desmistificando o aprendizado de máquina na educação com IA explicável. REVISTA INTERSABERES, [S. l.], 2024. Disponível em: https://revistasuninter.com/intersaberes/index.php/revista/article/view/2661. Acesso em: 25 out. 2024.

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v. 19 (2024): Revista Intersaberes

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