Enhancing Personalized E-Learning Systems in Higher Education Through Artificial Intelligence: A Rapid Systematic Literature Review

Abstract

Traditional teaching and learning approaches have been supplanted by artificial intelligence (AI) in the education sector. The existing AI technologies that can be used for personalized learning, however, are not well understood. Moreover, a systematic literature review (SLR) of AI technology for personalized learning is not widely available. By performing a thorough literature review and presenting the findings of AI technologies for improving personalized learning, this work fills this research gap. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) model served as the basis for the SLR. This study included 32 English-language journal articles that were published in the Google scholar, IEEE and Springer Nature Link databases between 2020 and 2026. The study's findings indicate that a number of issues with the current e-learning systems, including critical thinking, dependency syndrome, data privacy issues, and a lack of data privacy, make it challenging to accomplish personalized learning. The study also identified a number of AI technologies that fall within the general categories of deep learning (DL) and machine learning (ML). Both educational practice and the application of educational policies are impacted by these findings.