Building Privacy and Preserving AI Models for Secure Student Data Management in Educational Technology Platforms

Abstract

Artificial Intelligence (AI) integrated with educational technology (EdTech) platforms revolutionizes personalized learning through adaptive assessments as well as provides real-time feedback. These innovative educational systems heavily depend on the collection of huge amounts of personal student information which creates acute data protection challenges and algorithmic mainframe problems together with ethical boundaries issues. The widespread application of AI models in digital education necessitates data protection systems which defend students especially underaged students against surveillance programs that could harm their privacy rights. The research investigates how AI development programs intersect with protected data operations in educational software systems through the technologies of differential privacy along with federated learning along with homomorphic encryption. This paper reviews regulatory structures from the US and EU together with worldwide Southern regions while using case studies to illustrate successful and unsuccessful applications. The paper develops an interdisciplinary framework which combines innovative practices with data protection mechanisms according to policy standards and design principles for achieving sustainable AI deployment in worldwide educational structures.