<p>Education is one of the most powerful forces for both personal growth and societal progress. In recent years, the rapid integration of artificial intelligence (AI) into classrooms and learning platforms has begun to reshape traditional models of teaching, offering opportunities for personalization, adaptive instruction, and more effective educational management. This paper presented a systematic review, conducted in line with PRISMA guidelines, examining research published between 2010 and 2024. From an initial pool of 65,813 records, 58 high-quality studies were analyzed across four key dimensions: AI modeling, applications, ethics and privacy, and datasets. The review identified persistent challenges, including dependence on limited institutional datasets, class imbalance, weak benchmarking practices, and insufficient attention to fairness and privacy. In response, the paper introduces LITE-Ed, a five-layer conceptual framework for Learning-Intelligent, Tuneable, and Explainable Education (LITE-Ed). LITE-Ed combines lightweight modeling, adaptive learner profiling, transparent recommendations, and privacy-preserving mechanisms aligned with regulatory standards. Designed for educators, developers, and policymakers, the framework prioritizes interpretability and ethical compliance while remaining resource-efficient, with success measured through outcomes such as learning gains, retention, equity, and trust.</p>