A Bibliometric Analysis of Artificial Intelligence Research in Higher Education: Growth, Trends, and Collaborations

Vollständiger Abstract

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Objective: This research aims to reveal the development of scientific output in the field of artificial intelligence in higher education, its thematic trends, influential publications, and international collaboration networks. The study aims to contribute to future research and policy-making processes by mapping the intellectual structure of the field.Method: The research was conducted using a bibliometric analysis design. Data were obtained from 196 articles scanned in the Scopus database and meeting the specified criteria. VOSviewer (v1.6.20) software was used for data analysis. Publication trends, co-authorship, citations, keyword association, and international collaboration networks were analyzed. To increase validity and reliability, pre-specified inclusion and exclusion criteria were applied, the analysis process was reported in detail, and cross-validation was performed among researchers.Results: The findings show that artificial intelligence research in higher education has increased rapidly, especially since 2022, and that publications constitute approximately two-thirds of the total studies in 2024. Forty-five percent of the studies fall within the social sciences, followed by computer science and engineering. Thematic analysis identified ethics, ChatGPT, generative AI, learning, teaching, and educational technologies as the most frequently co-occurring concepts.. Collaboration analyses revealed that the United States, Australia, and the United Kingdom hold leading positions in international research networks.Conclusion and Recommendations: The research shows that AI studies in higher education have an interdisciplinary structure and that technological developments are addressed together with pedagogical, ethical, and managerial dimensions. The findings reveal that AI is not only a technological innovation but also that publications from 2024 alone account for approximately two-thirds of the dataset. Future research should incorporate comparative bibliometric studies across multiple databases and expand qualitative and mixed-methods research into the long-term pedagogical and cognitive effects of AI.