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

Authors

Keywords:

Artificial intelligence, explainable artificial intelligence, machine learning, education

Abstract

The adoption of machine learning (ML) algorithms in education is increasing, aiming to enhance teaching, learning, and administrative processes. These algorithms are crucial in personalized learning, student performance prediction, and curriculum design. However, their widespread use can lead to challenges like bias, lack of transparency, and excessive reliance on automated decisions. Educators often need help understanding the inner workings of ML models. This paper examines Explainable AI (XAI) as a solution to these issues in education. XAI techniques can provide educators and administrators with valuable insights into ML algorithms, facilitating more informed decision-making. We discuss the difference between transparent and opaque algorithmic choices and demonstrate the tangible benefits of XAI in education. Transparent models enable educators to leverage their expertise effectively, discover hidden patterns, and improve student outcomes

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Author Biographies

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. 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: 3 dec. 2024.

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v. 19 (2024): Revista Intersaberes

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