Mapping artificial-intelligence-driven innovation in higher education: A bibliometric review

Abstract

Artificial intelligence (AI) is reshaping higher education worldwide, creating new opportunities for teaching and learning while introducing fresh challenges., yet the empirical evidence on its didactic impact remains fragmented and largely unsynthesized. In particular, there is a lack of comprehensive, quantitative analyses that capture how AI is being integrated into university teaching practices and how ethical, social, and equity concerns are represented in the literature. This study addresses these gaps by conducting a bibliometric analysis of 482 scholarly articles on AI in higher education didactics published between 2003 and 2024. The results show a rapid growth of publications in recent years, with research concentrated on personalized learning, learning analytics, and intelligent tutoring, while ethical issues (e.g., data privacy, algorithmic bias, and equity) remain comparatively underexplored and unevenly integrated into didactic discussions. By providing a number-driven overview of the intellectual structure and development of this field, the study clarifies what is currently known about AI in higher education didactics, highlights persistent gaps and challenges, and outlines future research directions for more effective, ethical, and inclusive uses of AI in university teaching. • Literature frames AI as boosting personalization, automation, and inclusion. • Ethical, equity, and teacher-preparation gaps emerge as persistent concerns. • Evidence remains claim-based; robust empirical studies are still lacking. • Future work should test AI's impact and ensure fair, trustworthy implementation.

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