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Beyond the Algorithm: Educational Justice for Plurilingual Students in AI-Assisted Classrooms
Abstract
This chapter examines how artificial intelligence (AI), particularly large language models (LLMs) and automated assessment systems, reshapes teaching and evaluation practices in ways that may disadvantage plurilingual learners. While AI tools promise scalable feedback and personalization, they often reproduce monolingual norms embedded in their training data, leading to biased feedback, misclassification of linguistic diversity, and diminished learner agency. The chapter explores the cognitive, identity-based, and legal ramifications of algorithmic misjudgment. It highlights how AI bias affects metacognition, assessment validity, and the rights of students to express knowledge in culturally situated ways. Through international case studies and institutional examples, the chapter offers concrete pedagogical strategies and policy frameworks for teachers, developers, and institutions. It argues for inclusive design, multilingual training datasets, and participatory governance as essential steps toward a more equitable educational AI ecosystem.
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