Artificial Intelligence, Higher Education, and University Social Responsibility: A Bibliometric Mapping Study of Current Knowledge and Emerging Trends

Hanene Ben Messaoudi  Essoussi; Aziza Saïda  Slimane

Authors

Hanene Ben Messaoudi Essoussi
Aziza Saïda Slimane

Keywords:

artificial intelligence; higher education; university social responsibility; bibliometric analysis; governa

Abstract

This study presents a bibliometric analysis of research linking artificial intelligence (AI) in higher education (HE) with university social responsibility (USR) concerns between 2015 and 2025. The analysis was based on 330 documents retrieved from Scopus and Web of Science. Using Biblioshiny and VOSviewer, the study analyzes publication trends, citation performance, collaboration structures, and keyword co?occurrence networks to examine how AI-USR research in HE is structured and evolving. Findings indicate that the field remains recent, fragmented, and only partially consolidated. Keyword mapping positions AI adoption, learning applications, assessment, and capability?oriented themes at the center of the knowledge structure, while responsibility-related themes—including governance, privacy, academic integrity, inclusion, and sustainability—remain comparatively peripheral and less centrally integrated. However, these findings reflect thematic visibility within bibliometric metadata rather than the full conceptual depth of responsibility-oriented discussions developed in the reviewed publications. Geographically, production is concentrated in North America, Europe, and East Asia, although several Global South countries also show growing visibility and citation impact. Collaboration networks remain largely nationally organized, with international collaboration relying on a limited number of highly connected countries and institutions. Overall, the study identifies a knowledge-structure gap between AI adoption and institutional responsibility integration in HE and provides a reproducible baseline for future interdisciplinary and context-sensitive research on responsible AI integration in HE.

https://doi.org/10.26803/ijlter.25.6.36

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