Abstract

The application of Artificial Intelligence (AI) in educational analytics has ushered in unprecedented enhancement in student learning prediction, learning at scale, auto-grading, and institution-level decision-making. However, the increased generation and processing of student information precipitate unprecedented concerns in privacy and security, spanning breaches and inference attacks through adversarial manipulations, unauthorized third-party information extraction, and AI model explainability restrictions. In this article, we provide a critical overview of privacy-preserving AI-based educational analytics databases, from state-of-the-art approaches such as Differential Privacy (DP), Federated Learning (FL), Homomorphic Encryption (HE), Secure Multi-Party Computation (SMPC), and Blockchain. Global regulation compliance regimes such as the General Data Protection Regulation (GDPR), the Family Educational Rights and Privacy Act (FERPA), and the California Consumer Privacy Act (CCPA) are reviewed, with the ethical trade-offs and conflicts between utility and privacy preservation laid bare. Projected future directions from Zero-Knowledge Proofs (ZKP) and decentralized AI platforms through hybrid AI-privacy architecture and explainable AI (XAI) are discussed.