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Algorithmic Bias in Generative AI: Implications for Critical Pedagogy, Inclusivity, and Equity in Education
Abstract
This chapter examines the implications of algorithmic bias in generative AI for critical pedagogy, inclusion, and equity in education. This chapter explores how generative AI tools, increasingly prevalent in educational settings, can perpetuate cultural, gender, racial, socioeconomic, and accessibility biases due to skewed educational data and design choices. Through a critical pedagogy lens, they highlight the risks of reinforcing systemic inequalities and marginalizing diverse learners, drawing on real-world examples and theoretical frameworks from scholars such as Freire and Noble. The chapter suggests actionable strategies for reducing bias and aligning AI with educational justice, such as diversifying data sources, encouraging inclusive development teams, ensuring transparency, and implementing ongoing audits. The chapter concludes with a call for educators, policymakers, and technologists to collaboratively reimagine AI as an equitable learning tool, as well as directions for future research.
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