Year: 2025
Type: conference-paper
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.
Authors: Tariq A. Alshugran, Lina H. Kloub
Institution: University of Connecticut
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: gold
OpenAlex ID: W4409905221