Artificial intelligence in higher education: a bibliometric analysis and topic modeling approach

Abstract

Artificial intelligence (AI) has brought unprecedented growth and productivity in every socioeconomic sector. AI adoption in education is transformational through reduced teacher workload, individualized learning, intelligent tutors, profiling and prediction, high-precision education, collaboration, and learner tracking. This paper highlights the trajectory of AI research in higher education (HE) through bibliometric analysis and topic modeling approaches. We used the PRISMA guidelines to select 304 articles published in the Scopus database between 2012 and 2021. VOSviewer was used for visualization and text-mining to identify hotspots in the field. Latent Dirichlet Allocation analysis reveals distinct topics in the dynamic relationship between AI and HE. Only 9.6% of AI research in HE was achieved in the first seven years, with the last three years contributing 90.4%. China, the United States, Russia and the United Kingdom dominated publications. Four themes emerged – data as the catalyst, the development of AI, the adoption of AI in HE and emerging trends and the future of AI in HE. Topic modeling on the abstracts revealed the 10 most frequent topics and the top 30 most salient terms. This research contributes to the literature by synthesizing AI adoption opportunities in HE, topic modeling and future research areas.