Abstract
Every student brings an individual approach to learning, and recent advances in artificial intelligence have made it feasible to recommend a personalized learning path tailored to each one. This paper examines the application of AI-powered personalized learning systems in higher education, where a single syllabus delivered at a common pace fits the diversity of contemporary university cohorts poorly. Drawing on a review of the literature, an analysis of the underlying mechanism, and pedagogical reflection, it addresses three questions in turn: the theoretical foundations of personalized path recommendation, the architecture through which such recommendation is realized, and the strategies by which it can be implemented in practice. Particular attention is given to the learner model and the adaptive engine at the core of these systems, as well as to the evidence on their effectiveness and its limits. The paper argues that the value of recommendation must be realized without compromising learners’ autonomy of choice or their all-round development, and that data quality, algorithmic transparency, and ethical safeguards are preconditions rather than afterthoughts.
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How to cite this paper
Research on the Application of AI-powered Personalized Learning Systems in Higher Education
How to cite this paper: Ang Li. (2026) Research on the Application of AI-powered Personalized Learning Systems in Higher Education. Advances in Computer and Communication, 7(3), 126-129.
DOI: http://dx.doi.org/10.26855/acc.2026.09.002