Artificial Intelligence in Higher Education: Insights From a Bibliometric Analysis and Systematic Review of Highly Cited Articles

ABSTRACT This study integrates bibliometric analysis and systematic review methodologies to provide a comprehensive overview of research on artificial intelligence (AI) in higher education. Drawing on 621 publications and a focused analysis of 27 highly cited empirical studies, the study addresses six research questions concerning publication trends, prolific authors, influential documents, methodological approaches, research gaps and ethical considerations. The integrated design combines macro‐level field mapping with micro‐level synthesis to reveal how AI scholarship has evolved and where critical gaps persist. The findings indicate a surge in AI‐related publications since 2021, driven by generative AI in 2023–2024. Research remains concentrated in economically advantaged regions, reflecting limited participation from the Global South. Thematically, studies cluster around AI in teaching and assessment, institutional readiness and ethical implications, while theoretical fragmentation and methodological homogeneity persist. A three‐layer framework comprising technological, pedagogical, and institutional dimensions is proposed to explain these patterns and to guide more inclusive, ethical and pedagogically grounded AI integration in higher education.