Artificial Intelligence-Enabled Smart Learning Environments :Building Adaptive and Personalized Education Systems

With the rapid advancement of machine learning (ML), large-scale data collection has become essential for building accurate models. However, the use of sensitive data introduces significant privacy risks, including data leakage, unauthorized access, and inference attacks. Privacy-Preserving Machine Learning (PPML) has emerged as a crucial research area aimed at enabling data-driven learning while protecting individual privacy. This paper provides a comprehensive overview of major PPML techniques such as homomorphic encryption, differential privacy, secure multi-party computation, and federated learning. It also discusses key challenges including computational overhead, privacy-utility trade-offs, scalability issues, and regulatory concerns. Finally, future research directions are highlighted to guide the development of secure and efficient machine learning systems.