Themes, Knowledge Evolution, and Emerging Trends Related to the Application of Artificial Intelligence in Personalized Learning: A Scientometric Analysis in CiteSpace

Artificial intelligence (AI) plays a pivotal role in the development and growth of personalized learning. Therefore, it is necessary to conduct scientific and in-depth research on the application of AI in this field, systematically analyzing its development trends and research hotspots to provide references for researchers. This study employs a visual bibliometric analysis as the primary research approach, selecting 922 articles from the Web of Science spanning from 2004 to 2024. A comprehensive analysis was conducted using CiteSpace, primarily covering major highly co-cited literature and keywords. The notable findings of this study are as follows: Firstly, the study reveals three major themes related to the application methodology of AI in personalized learning, namely, machine learning, large language models, and personalized recommendation systems. Secondly, the visual timeline reveals the application of Personalized Learning in AI across three phases: Part A (2016–2022), Part B (2019–2024), and Part C (2011–2024). Thirdly, the burst analysis illustrates the hotspots, especially “intelligent tutoring system”. This study deepens our understanding of the fundamentals and cutting-edge research in the application of AI to personalized learning, thus facilitating the identification of future research directions, as well as teaching practice patterns and trends.