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<p>This study investigates the integration of artificial intelligence in higher education, aiming to identify trends, key contributors, highly cited papers, collaboration, and thematic areas in research published between (2016–2025) for future research direction. A bibliometric and content analysis was employed, combining quantitative descriptive methods and network analysis with qualitative content analysis of the most-cited articles. Data was extracted from Scopus, yielding 276 refined documents after excluding duplicates, editorials, and notes. Analytical techniques included co-word analysis, citation analysis, co-authorship analysis, and bibliographic coupling, supported by VOSviewer for visualization. Key findings include Symbiosis International Deemed University and Bucharest University of Economic Studies as leading affiliations, with China, India, and the UK as top contributing countries. The most significant journals are Lecture Notes <i>in</i> Networks and Systems and Education and Information Technologies, while authors like Crawford and Păun contribute. Co-authorship analysis highlights strong collaboration among developed countries, while co-word analysis reveals themes like adaptive learning, predictive analytics, and ChatGPT. Bibliometric coupling identifies influential works, including studies by Chatterjee and Bhattacharjee, emphasizing AI adoption. Content analysis underscores the transformative potential of AI in enhancing learning, administrative efficiency, and Innovation. This study provides managerial and practical recommendations for students, universities, and policymakers. This study has several limitations that future studies will consider.</p>

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Artificial intelligence in higher education with bibliometric and content analysis for future research agenda

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月14日

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  • Rahmanwali Sahar,
  • Munjiati Munawaroh

摘要

This study investigates the integration of artificial intelligence in higher education, aiming to identify trends, key contributors, highly cited papers, collaboration, and thematic areas in research published between (2016–2025) for future research direction. A bibliometric and content analysis was employed, combining quantitative descriptive methods and network analysis with qualitative content analysis of the most-cited articles. Data was extracted from Scopus, yielding 276 refined documents after excluding duplicates, editorials, and notes. Analytical techniques included co-word analysis, citation analysis, co-authorship analysis, and bibliographic coupling, supported by VOSviewer for visualization. Key findings include Symbiosis International Deemed University and Bucharest University of Economic Studies as leading affiliations, with China, India, and the UK as top contributing countries. The most significant journals are Lecture Notes in Networks and Systems and Education and Information Technologies, while authors like Crawford and Păun contribute. Co-authorship analysis highlights strong collaboration among developed countries, while co-word analysis reveals themes like adaptive learning, predictive analytics, and ChatGPT. Bibliometric coupling identifies influential works, including studies by Chatterjee and Bhattacharjee, emphasizing AI adoption. Content analysis underscores the transformative potential of AI in enhancing learning, administrative efficiency, and Innovation. This study provides managerial and practical recommendations for students, universities, and policymakers. This study has several limitations that future studies will consider.