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Artificial Intelligence in Higher Education: A Bibliometric and Visual Analysis of a Filtered Web of Science Corpus

Authors

  • Irum Zeb ORCID School of Political Science and Public Administration, Henan Normal University, Xinxiang 453007, China
  • Bibi Saima ORCID Department of Pakistan Studies, Hazara University, Mansehra 21120, Pakistan
  • Aashiq Khan Email ORCID School of Political Science and Public Administration, Henan Normal University, Xinxiang 453007, China

DOI:

https://doi.org/10.30564/jiep.v9i2.117078
Received: 29 May 2026 | Revised: 17 August 2026 | Accepted: 26 August 2026 | Published Online: 13 October 2026

Abstract

This study reports a bibliometric and visual analysis of a deliberately filtered Web of Science Core Collection corpus retrieved on May 29, 2025. The search used the all-fields expression “artificial intelligence” AND “higher education” and was progressively restricted by publication year, document type, Sustainable Development Goal (SDG) categories, language, research area, country/region, and Citation Topic Micro classifications. The final export contained 1,214 unique English-language records. VOSviewer 1.6.20 was used to examine productivity, co-authorship, keyword co-occurrence, citation, co-citation, and bibliographic-coupling patterns. Within this selected corpus, China (395 records), the United States (174), and England (136) contributed 705 records, equivalent to 58.1% of the dataset. Central China Normal University was the most productive organization. Keyword mapping identified clusters concerned with AI adoption and technology acceptance, generative AI and large language models, and technical methods including machine learning and deep learning. These results describe the structure of the filtered corpus rather than the complete global literature on AI in higher education. The restrictive topical, geographic, disciplinary, and SDG filters limit coverage and may admit technically related records with weak substantive relevance to higher education. Accordingly, citation counts are interpreted as database-specific citation frequencies, and bibliographic coupling is interpreted as similarity in reference use rather than direct collaboration or research quality.

Keywords:

Artificial Intelligence, Higher Education, Bibliometric Analysis, VOSviewer, Web of Science, Science Mapping

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Issue

2026 Vol.9 No.2

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Review

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Copyright © 2026 Irum Zeb, Bibi Saima, Aashiq Khan

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