Redefining personalized learning in the artificial intelligence era: an updated systematic review from 2019 to 2025

Abstract The integration of artificial intelligence (AI) in education is prompting a reevaluation of personalized learning terminology and its impact on teaching practices and learner engagement. Personalized learning (PL) involves various instructional strategies tailored to individual student needs and interests, utilizing data and technology to boost engagement and success. The evolving landscape requires a clear understanding of how AI can support personalized learning, distinguishing it from traditional methods. The variability in PL terminology reflects diverse interpretations of AI technologies in education, necessitating a common framework to clarify definitions and practices. This document presents an overview of the latest research literature on personalized learning, highlighting how technology is transforming the framework and effectiveness of individualized learning experiences. By analyzing reputable articles from 6 databases, the review seeks to provide insights into how AI can redefine personalized learning, enabling more precise definitions. The findings emphasize the use of PL terms in technological contexts and call for a unified term to enhance clarity and effectiveness in educational technology practices. Ultimately, the review aims to inform educators and policymakers about precise terms defining personalized learning in the AI context.