Data Privacy in AI-Driven Education

Abstract

This chapter examines the data privacy challenges posed by AI-driven education and offers strategic solutions to protect student information. The authors explore how AI systems are collecting various types of student data, from test scores to social interactions, and what this means for privacy. Through real-world examples, the authors shed light on worrying trends, like excessive surveillance and potential data breaches. The authors also tackle the legal and ethical questions that arise when AI meets education and point out how current laws often fall short in this rapidly developing field. Key findings reveal the inadequacy of current regulations and the potential for AI to exacerbate existing educational inequalities. The authors recommend implementing comprehensive data governance policies, investing in educator training on AI and privacy, and incorporating data literacy into curricula. The chapter emphasizes the need for a balanced approach that harnesses AI's benefits while protecting students' privacy through technical solutions, policy reforms, and enhanced digital literacy.

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