Artificial Intelligence and Entrepreneurship Within Academic Ecosystems: A Bibliometric Analysis (2019–2025)

Résumé

This article addresses a specific gap in the bibliometric literature on artificial intelligence and higher education. While prior reviews by Kavitha et al. (2024), Flores-Velásquez et al. (2024), and Sahar and Munawaroh (2025) have broadly mapped AI adoption in higher education, none has jointly examined AI's role within university entrepreneurial ecosystems, nor systematically tracked the generative AI inflection point that emerged after 2022 in this sub-field. To address this gap, the study applies a bibliometric protocol grounded in the PRISMA framework to an initial pool of 222 Scopus-indexed documents published between 2019 and 2025, reduced to a final corpus of 165 articles after applying disciplinary, temporal, and document-type exclusion criteria. The corpus was processed with VOSviewer to map publication trends, leading journals, country contributions, and keyword co-occurrence networks. Results reveal a pronounced geographic concentration of research output, with China alone accounting for 37 percent of the corpus and the nine most productive countries jointly contributing nearly four-fifths of all publications. Keyword co-occurrence analysis identifies artificial intelligence, students, and entrepreneurship education as the field's dominant thematic anchors, with artificial intelligence acting as the principal structural connector bridging pedagogical and entrepreneurial sub-themes. These patterns point to pedagogical innovation, entrepreneurial competency-building, and generative AI integration as the field's core research clusters. From a managerial perspective, the findings call on university incubators and technology transfer offices to embed AI literacy and generative AI tools into entrepreneurship curricula and venture-support processes, consistent with the conceptualization of AI as a general-purpose technology. The study's main limitations stem from its exclusive reliance on the Scopus database and its purely quantitative bibliometric design; future research should combine multiple databases and incorporate qualitative approaches to further explore innovation dynamics within university entrepreneurial ecosystems.

Classification JEL: I23, L26, O30.

Paper type: Empirical Research