Examining the Expansion and Collaborative Patterns of Artificial Intelligence in Education: A Bibliometric Study

Abstract

Purpose – The purpose of this study is to conduct a comprehensive bibliometric analysis of the existing literature on the use of artificial intelligence (AI) in education. The study aims to map the scholarly network in this emerging field and identify trends in publication output, influential contributors, core research themes and areas that require further investigation. Design/methodology/approach – The study conducts a bibliometric analysis of scholarly articles on AI in education indexed in the Scopus database. A total of 1,192 publications meeting the selection criteria were analysed using bibliometric mapping and visualization tools including VOSviewer, Microsoft Excel and biblioshiny. Frequency analyses, network mapping and citation metrics were used to analyse publication trends, collaborations, and impact. Findings – The findings indicate a significant exponential growth in publications since 2010, establishing AI in education as a vibrant field. Prolific contributors include individual authors, institutions such as the Education University of Hong Kong, and countries like China and the US. Network analyses revealed extensive collaborations through co-authorship within and between regions. Core themes centred around AI's role in transforming pedagogy and learning experiences. Research limitations/implications – The study is limited to publications indexed in Scopus. Future research could expand the analysis to other databases and languages. Insights from the bibliometric maps have implications for focusing efforts to strengthen collaborative ties and under-represented areas Originality/value – This is the first comprehensive bibliometric study to map the scholarly network in this emerging field. The systematic analysis provides a holistic view of trends, influencers and conceptual themes, with value for informing future research directions in AI-enhanced education.