Data privacy risks in the use of generative AI in digital educational settings

Abstract

The integration of generative artificial intelligence (GenAI) tools into educational environments has raised serious concerns about the confidentiality and security of student data. This article examines the principal categories of data privacy risks arising GenAI use in education, analyses the existing regulatory landscape, and proposes a structured risk typology to guide institutional policy. Drawing on a qualitative synthesis of peer-reviewed literature (2024-2025), the study identifies training data exposure, unauthorised third-party data sharing, inadequate consent mechanisms, and regulatory non-compliance as the primary risk categories. The findings are presented in a comparative framework mapping GenAI tools against key risk dimensions. The study concludes that educational institutions require robust governance frameworks and targeted digital literacy programmes to mitigate GenAI-related privacy risks.