Artificial intelligence in higher education with bibliometric and content analysis for future research agenda
Abstract 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 ana...
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| Format: | Article |
| Language: | English |
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Springer
2025-05-01
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| Series: | Discover Sustainability |
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| Online Access: | https://doi.org/10.1007/s43621-025-01086-z |
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| author | Rahmanwali Sahar Munjiati Munawaroh |
| author_facet | Rahmanwali Sahar Munjiati Munawaroh |
| author_sort | Rahmanwali Sahar |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-348a5951e8e842018fa7f04de2c73536 |
| institution | DOAJ |
| issn | 2662-9984 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Sustainability |
| spelling | doaj-art-348a5951e8e842018fa7f04de2c735362025-08-20T03:10:18ZengSpringerDiscover Sustainability2662-99842025-05-016113210.1007/s43621-025-01086-zArtificial intelligence in higher education with bibliometric and content analysis for future research agendaRahmanwali Sahar0Munjiati Munawaroh1Master of Management, Postgraduate Program, Universitas Muhammadiyah YogyakartaDepartment of Management, Faculty of Economics and Business, Universitas Muhammadiyah YogyakartaAbstract 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.https://doi.org/10.1007/s43621-025-01086-zArtificial IntelligenceHigher EducationBibliometric AnalysisContent AnalysisVOSviewer |
| spellingShingle | Rahmanwali Sahar Munjiati Munawaroh Artificial intelligence in higher education with bibliometric and content analysis for future research agenda Discover Sustainability Artificial Intelligence Higher Education Bibliometric Analysis Content Analysis VOSviewer |
| title | Artificial intelligence in higher education with bibliometric and content analysis for future research agenda |
| title_full | Artificial intelligence in higher education with bibliometric and content analysis for future research agenda |
| title_fullStr | Artificial intelligence in higher education with bibliometric and content analysis for future research agenda |
| title_full_unstemmed | Artificial intelligence in higher education with bibliometric and content analysis for future research agenda |
| title_short | Artificial intelligence in higher education with bibliometric and content analysis for future research agenda |
| title_sort | artificial intelligence in higher education with bibliometric and content analysis for future research agenda |
| topic | Artificial Intelligence Higher Education Bibliometric Analysis Content Analysis VOSviewer |
| url | https://doi.org/10.1007/s43621-025-01086-z |
| work_keys_str_mv | AT rahmanwalisahar artificialintelligenceinhighereducationwithbibliometricandcontentanalysisforfutureresearchagenda AT munjiatimunawaroh artificialintelligenceinhighereducationwithbibliometricandcontentanalysisforfutureresearchagenda |