A Critical Review of Privacy, Bias, and Trust Challenges in Generative AI for Educational Contexts

Abstract

This paper explores the ethical challenges of inte-grating Generative AI (GenAI) tools in education, focusing on data privacy, algorithmic bias, and trust. While GenAI offers significant benefits su ch as pe rsonalised le arning an d increased accessibility, it also raises concerns regarding the privacy of sensitive student data, the perpetuation of biases in AI models, and the need for transparency to build trust. The review highlights the importance of implementing privacy preserving techniques, such as federated learning and differential privacy, to safeguard data. It also emphasises the need for diverse datasets and regular audits to address algorithmic bias and ensure fairness in GenAI driven educational tools. Building trust among educators, students, and institutions is critical, requiring clear communication, transparency, and institutional governance. The paper concludes with recommendations for ethical AI integration in educational settings, advocating for responsible practices, continuous oversight, and proactive policy development to ensure equitable and trustworthy use of GenAI tools in education.

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