Personalized Learning System Based on Artificial Intelligence to Enhance Learning Effectiveness: A Bibliometric Analysis

Abstract

The integration of artificial intelligence (AI) in personalized learning systems has emerged as a transformative approach to address diverse educational needs and enhance learning effectiveness. However, comprehensive insights into the research landscape, trends, and challenges remain underexplored. This study aims to systematically map and analyse the development of AI-driven personalized learning systems over the past decade to understand their evolution, thematic focus, and future directions. To achieve this, a bibliometric analysis was conducted on 368 Scopus-indexed publications (2015–2025). Utilizing VOSviewer, the analysis reveals a significant surge in research output post-2021, with conference papers and articles dominating scholarly communication. Key themes include adaptive learning, machine learning algorithms, and educational innovation, while emerging clusters highlight advancements in generative AI (e.g., ChatGPT) and language models. Findings indicate that AI-based systems improve academic performance, engagement, and retention through tailored content and real-time feedback. However, challenges such as data privacy, algorithmic bias, and accessibility disparities persist. This study provides a data-driven synthesis of the field’s intellectual structure, offering actionable insights for educators, policymakers, and researchers to optimize AI’s potential in creating equitable and effective learning environments.

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