Preserving Student Privacy While Leveraging Generative AI in Higher Education

The integration of Generative Artificial Intelligence (GenAI) in higher education offers significant opportunities for personalized learning and the development of dynamic educational materials.However, the use of GenAI often involves processing sensitive student data, raising concerns about privacy and regulatory compliance.This paper examines these challenges, highlighting key risks such as data breaches and unauthorized data sharing.A comprehensive solution is proposed involving privacy-preserving technologies and robust data governance frameworks.By integrating anonymization techniques and hybrid AI models, institutions can balance local data processing with cloud-based capabilities, ensuring compliance and accountability.The findings underscore the necessity of strong institutional policies to protect student privacy and foster trust in AI-driven educational innovations.

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Publication Details

Published
2025-04-29
DOI
https://doi.org/10.18260/1-2-119-54995
Primary Topic
Privacy-Preserving Technologies in Data
Type
article
Field-Weighted Citation Impact
0.00
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article

Preserving Student Privacy While Leveraging Generative AI in Higher Education

Tariq Alshugran, Lina H. Kloub
Privacy-Preserving Technologies in Data
article

Preserving Student Privacy While Leveraging Generative AI in Higher Education

Tariq Alshugran, Lina H. Kloub
article en

Abstract

The integration of Generative Artificial Intelligence (GenAI) in higher education offers significant opportunities for personalized learning and the development of dynamic educational materials.However, the use of GenAI often involves processing sensitive student data, raising concerns about privacy and regulatory compliance.This paper examines these challenges, highlighting key risks such as data breaches and unauthorized data sharing.A comprehensive solution is proposed involving privacy-preserving technologies and robust data governance frameworks.By integrating anonymization techniques and hybrid AI models, institutions can balance local data processing with cloud-based capabilities, ensuring compliance and accountability.The findings underscore the necessity of strong institutional policies to protect student privacy and foster trust in AI-driven educational innovations.

University of Connecticut (US), Center for Independent Living (US)
Quality Education
Openalex Percentile: Top 96%
Privacy-Preserving Technologies in Data
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