AI-Driven Digital Transformation in Higher Education: A Bibliometric Analysis of Research Trends

Abstract

Despite the rapid growth of academic literature at the intersection of artificial intelligence (AI), digital transformation, and higher education, comprehensive bibliometric syntheses that systematically map the intellectual structure and developmental trajectory of this domain remain limited. This study addresses this gap through a bibliometric analysis of 614 publications indexed in the Web of Science database between 1997 and 2026. Using the bibliometrix R package, the study examined annual publication trends, geographic distribution, citation impact, co-authorship networks, journal concentration, thematic structures, and research life cycle patterns. The findings suggest that the field has experienced three broad developmental phases: an initial lag period (1997–2015), an acceleration phase (2016–2022), and a possible transition toward maturation after 2023. The logistic growth model estimated an inflection point around 2023.5, although this projection should be interpreted cautiously due to the evolving nature of the field. China emerged as the leading country in publication output, while the United Kingdom demonstrated comparatively higher citation impact. Thematic analysis indicates a gradual shift from technology-oriented discussions toward competency-oriented perspectives, with AI and innovation appearing as persistent motor themes. In contrast, ethics, equity, and governance-related topics remain relatively underrepresented. Overall, the findings provide a broad bibliometric overview of the evolving AI-driven digital transformation literature in higher education and may offer implications for future research and policy development.

Keywords

• Artificial intelligence
• Higher education
• Digital transformation
• Bibliometric analysis
• Thematic analysis

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