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ABSTRACT Adaptive Education Systems (AES) have become a key part of modern education. They try to meet the needs of all kinds of learners and address the problems posed by one‐size‐fits‐all educational approaches. In this context, AI‐driven methods have enabled personalization, adaptation, and data‐driven decision‐making in education at the content, learning path, and assessment levels. But using AI in AES is hard because it relies on data, can't be scaled up, raises ethical questions, and lacks sufficient, consistent empirical evidence. This paper offers a thorough, systematic overview of novel concepts, models, and trends in AI‐driven AES. The main objective of this study is to analyze the dominant research directions with a special focus on Pathway Personalization with 32% and Evaluation Evidence with 14% as the two dominant axes of recent studies. The findings show that AI‐driven approaches effectively design adaptive learning pathways, enhance intelligent educational support, and improve the accuracy of educational decision‐making. At the same time, the equity and ethical governance dimensions remain underdeveloped. Altogether, this research, by providing a coherent analytical framework, can serve as a conceptual basis for researchers, educational system designers, and policymakers in advancing targeted, generalizable development of AES.
Sun et al. (Thu,) studied this question.