Artificial intelligence in higher education management a bibliometric analysis of challenges opportunities and future research directions

Abstract

Abstract This study presents a bibliometric analysis to examine the intellectual structure, thematic evolution, and research dynamics of artificial intelligence (AI) in higher education management. Using 279 Scopus-indexed journal articles published between 1988 and 2025, the analysis employs Biblioshiny and VOSviewer to conduct performance analysis, co-citation analysis, bibliographic coupling, keyword co-occurrence, and thematic mapping. The results indicate a steady annual publication growth rate of 10.17%, with contributions from 735 authors across 181 academic journals, reflecting the expanding scholarly attention to this field. Influential outlets such as the International Journal of Management Education and the Journal of Management Education play a central role in knowledge dissemination, while highly cited publications serve as key intellectual references. Thematic mapping identifies artificial intelligence and management education as motor themes, indicating their central and mature position, supported by machine learning and deep learning as foundational methodological themes. Niche themes such as AI literacy and education quality demonstrate strong internal development but limited integration, whereas emerging themes including AI ethics and AI in education highlight growing managerial, ethical, and governance-related concerns. The thematic evolution analysis further reveals an increasing focus on generative AI applications, particularly ChatGPT, within higher education management contexts. By systematically mapping research patterns and thematic trajectories, this study provides a structured overview of the field and identifies research gaps relevant to institutional governance, ethical regulation, and strategic management of AI in higher education.

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