How to bring personalized learning to all students and meet their personal needs remains the long-term dilemma of American education, which puts both personal achievement and national competitiveness in danger, so a shift away from one-size-fits-all learning becomes urgent. The paper present an overall infrastructure to implement intelligent algorithms to make learning spaces adaptive and personal. Based on several machine learning techniques, the framework utilizes Bayesian Knowledge Tracing to assess skill mastery levels, Natural Language Processing to provide real-time automatic feedback, and reinforcement learning to establish the best pathways to follow. The discussion is accompanied by a detailed survey of intelligent tutoring systems and the algorithms that they use. At the heart of the analysis lies the introducing of the Algorithm-Enhanced Personalized Learning (AEPL) Framework, providing a formal approach aimed at normalizing the development and the ethical process of such complicated systems. It is hoped that the widespread adoption of this framework will become a national effort, yielding significantly positive outcomes in terms of educational equity, student performance, and the development of a labor force ready to innovate in the global environment.
Hou et al. (2025) studied this question.