Artificial Intelligence in higher education: a decade’s bibliometric snapshot, emerging themes and future research

Abstract

Artificial intelligence has become an integral part of higher education, significantly transforming the landscape of higher education. This study aims to identify, analyse and visualise peer-reviewed academic research output on artificial intelligence (AI) and graduate attributes in higher education. Data was gathered from the Scopus database over a decade (2014-2024), with search terms related to artificial intelligence, graduate attributes, and higher education. Following the PRISMA method guidelines, 106 articles were deemed necessary for review. Bibliometric methods, content and thematic analysis were used to identify main themes, and VoSviewer software was used to analyse the data. The findings revealed research productivity, citation overview, the main subjects, the territory of the leading researchers, thematic choices and future research opportunities and directions. Themes such as the impacts of AI on graduate attributes emerged, which may assist policymakers, educational institutions, teachers and students in their strategies and choices for adopting and using AI. The study recognised research trends, provided insights into the current state of AI and higher education research, and identified potential gaps in the literature on the research landscape of AI, graduate attributes, and higher education. The study can guide future researchers on emerging thematic opportunities.