A Bibliometric Analysis of Adaptive Learning in K-12 Education
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Abstract
Adaptive learning has emerged as a transformative approach in K-12 education, leveraging artificial intelligence and data-driven methodologies to personalize learning experiences. This study conducts a bibliometric analysis of research on adaptive learning, utilizing data from Scopus and network analysis through VOSviewer to identify key trends, influential authors, and thematic developments. The findings reveal a significant evolution from traditional adaptive learning models to AI-powered systems, highlighting the growing emphasis on machine learning, neural networks, and intelligent tutoring systems. Additionally, the study identifies critical challenges, including data privacy concerns, teacher preparedness, and the digital divide, which impact the effective implementation of adaptive learning technologies. Collaboration patterns indicate strong interdisciplinary and international research efforts, yet disparities remain in global research contributions. Future directions suggest the need for longitudinal studies on learning outcomes, ethical considerations in AI-driven education, and scalable adaptive learning solutions for diverse educational contexts. This research provides valuable insights for educators, policymakers, and researchers striving to enhance adaptive learning frameworks and their integration into K-12 education.
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