AI-ASSISTED ADAPTIVE IN TEACHING AND LEARNING: A BIBLIOMETRIC REVIEW

Abstract

The rapid advancement with regard to Artificial Intelligence (AI) has transformed multiple educational domains, particularly in adaptive teaching and learning, where AI technologies play a crucial role in personalizing instruction and enhancing learning outcomes. Despite its increasing significance, there remains a lack of comprehensive bibliometric evidence that maps the intellectual structure, research patterns, and emerging trends in this field. This study addresses this gap by conducting a bibliometric analysis of AI-assisted adaptive teaching and learning to identify publication dynamics, research hotspots, and global collaboration networks. Data were collected from the Scopus database via advanced search strategies incorporating the keywords “Artificial Intelligence,” “Adaptive,” “Teaching,” and “Learning,” which yielded 866 relevant publications. The dataset was cleaned and harmonized using OpenRefine to ensure consistency, while statistical and graphical analyses were performed through the Scopus analyzer. Visualization of co-authorship, keyword co-occurrence, and thematic clustering was generated using VOSviewer to offer a deeper comprehension regarding the field’s conceptual and intellectual structures. The results reveal a significant growth trajectory of publications over the past decade, dominated by contributions from China, the United States, India, and the United Kingdom, highlighting the global nature of research in this area. Keyword co-occurrence analysis identified core themes, for example, intelligent tutoring systems, machine learning (ML), adaptive learning environments, and collaborative learning, with emerging interest in areas like deep learning, educational data mining, and personalized learning pathways. These research findings demonstrate that AI-assisted adaptive teaching and learning research is highly interdisciplinary, bridging education, computer science, and cognitive sciences, and is increasingly shaped by international collaborations. In conclusion, this study provides an overview of the knowledge structure in AI-driven adaptive education and offers valuable insights for scholars, practitioners, and policymakers. These insights help identify future research directions, strengthen cross-border partnerships, and advance innovative pedagogical frameworks that leverage AI for inclusive and effective learning.