Artificial Intelligence-Driven Leadership in Higher Education: A Bibliometric Analysis of Research Trends and Development
Keywords:
artificial intelligence; leader; higher education; bibliometric analysisAbstract
This study aimed to map AI-driven leadership research in higher education published between 2014 and 2025. This study adopts a bibliometric analysis approach to map publication trends and productivity of studies on Artificial intelligence-driven leadership in the Scopus database over 11 years. A total of 2,097 publications were retrieved using a comprehensive search string executed on March 1, 2025. The analysis was conducted using Microsoft Excel and VOSviewer, focusing on publication trends, influential authors, collaboration networks, and thematic clusters. The results of the bibliometric analysis revealed that there has been a consistent increase in the publication trends in AI-driven leadership research in higher education between 2014-2025. It was found that the highest research output was recorded in 2024 with 604 publications. While the United States produced the highest document (f=462), the Sustainability Journal is the most impactful. This study also found that some authored documents individually attracted high citations, and no authors exhibited significant link in co-authorship networks. While the dominant theme is machine learning, Improta emerged as the most influential author in AI-driven leadership research in higher education. The outcome of this study indicates the growing academic interest in AI-driven leadership research in higher education, owing to the benefits of AI in effective school administration. The outcome of this study can guide policymakers and the direction of the future in the field of artificial intelligence in order to pursue higher education leadership. Bibliometric analysis provides information regarding the trends of AI-driven leadership studies.
https://doi.org/10.26803/ijlter.24.7.31
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