Artificial intelligence in higher education: a bibliometric analysis of research trends (2015–2024)

Abstract

Purpose Previous research on Artificial Intelligence in Education (AIEd) has predominantly focused on specific tools and outcomes. Few studies provide data-driven insights into its evolution, particularly in the period after the launch of ChatGPT. This study addresses this gap through a bibliometric analysis of publications on Artificial Intelligence in Higher Education from 2015 to 2024, mapping research output, collaboration patterns, and citation trends. Design/methodology/approach A total of 1,671 peer-reviewed journal articles indexed in Scopus were analyzed based on the PRISMA framework. Using performance analysis and scientific mapping, the study examined the AIEd research landscape to offer document, author, country, institution, and keyword-level insights. Findings The publications depict a 67% annual growth rate, especially after 2022, with output tripling from 353 in 2023 to 927 in 2024, coinciding with the release of ChatGPT. Most highly cited articles focus on generative AI, signifying a thematic shift towards AI-driven academic concerns like academic integrity, assessment challenges, and AI ethics. Practical implications The findings can guide universities in implementing AI literacy initiatives, embedding ethical safeguards, and shaping evidence-based institutional and policy frameworks for responsible AI integration. Originality/value This study captures the trajectory of AI in higher education over the past decade. It highlights a significant thematic shift in research following the emergence of ChatGPT, which has not yet been comprehensively assessed. The findings of the study are poised to shape the direction of future research in the field.

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