Tracing The Digital Transformation: A Bibliometric Investigation Of Artificial Intelligence Adoption In Higher Education

This study aimed to track the digital transformation journey through the lens of AI adoption in higher education from 2010 to 2025. Using a data-based bibliometric method from Scopus, this study identified the dominant theories used in AI adoption intention studies and conceptual structures. The literature selection process was carried out systematically using the PRISMA method to ensure transparency and accuracy in document selection. Data analysis used bibliometric techniques to analyse the research landscape quantitatively and was conducted using VosViewer Software. The analysis results show that research on AI adoption intention has experienced an annual growth of 34.15%, with most publications using the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB) approaches. Network visualisation revealed fragmentation in this research, where several clusters of theories develop separately without strong integration. Overlay visualisation showed a shift from technology acceptance model-based studies to exploration of ethical impacts, algorithm transparency, and AI regulation in higher education. Density visualisation confirmed that although technical factors have been widely studied, AI's social and policy aspects are still underexplored. This research provides a more comprehensive conceptual mapping and identifies research gaps that future studies can fill.