Mapping Artificial Intelligence for Personalised Learning based on Learning Style: Scopus-Based Bibliometric Analysis (2006-2026)
Year: 2026
Type: article
Abstract: The aims of this study is to conduct a bibliometric analysis of the development of learning style-based personalisation. The rapid advancement of AI-based learning personalisation has been accompanied by growing research attention to the role of learning style in the modern learning ecosystem. The research methodology involves a bibliometric analysis of 604 Scopus-indexed publications from the period 2006 to 2026 that were analyzed and visualized using Biblioshiny, VOSviewer and Open Refine. The results of the study indicate a significant increase in publications beginning in 2020, reflecting growing research attention to AI-based personalisation., the keywords patterns indicate an increasing association between learning style and machine learning analytics. Random Forest is one of the most commonly reported machine learning methods in the literature to identify learning styles. The findings further indicate that AI and learning style are increasingly discussed with the same research landscape. This study contributes by mapping the evolution of this field and demonstrating the convergence of educational theory and computational approaches. Further research should be extended beyond the Scopus database, combining bibliometric analysis and systematic literature review, and investigating the integration of learning styles with learner behavioral and cognitive data in AI-powered personalised learning systems.
Institution: Diponegoro University
Language: English
Subfield: Developmental and Educational Psychology
Field: Psychology
Domain: Social Sciences
SDG: Quality education
Open Access status: gold
Estimated APC paid: $32
OpenAlex ID: W7223412915