Artificial Intelligence in Higher Education: A Bibliometric Analysis of Generative AI, Adaptive Curriculum, and Human–AI Collaboration

Abstract

The rapid development of artificial intelligence (AI), particularly generative AI, is reshaping curriculum design, teaching, and learning in higher education. This study maps the recent development of this research landscape through a bibliometric analysis of 265 publications indexed in the Web of Science Core Collection. The archived dataset covers 2006–2026; 2026 is treated as a partial year because the data were retrieved on 26 January 2026. Using the Bibliometrix R package and Biblioshiny interface, the study combines quantitative bibliometric techniques with qualitative thematic interpretation to examine publication trends, citation patterns, geographical distribution, and thematic structures. Findings show a marked increase in research output after 2022, broad dispersion across publication venues, substantial differences in affiliation-based country frequencies, and differentiated citation-based scholarly visibility. Keyword-frequency and co-occurrence patterns were interpreted through author-defined areas concerning curriculum design, personalized and adaptive learning, educational modelling and frameworks, ethical and governance considerations, and human–AI collaboration. The study contributes to curriculum and instruction by providing an integrated map of AI research and emphasizing pedagogically grounded, ethically informed, and context-sensitive approaches to AI integration in higher education.