Generative Artificial Intelligence and Academic Productivity: A Bibliometric Analysis of Global Research Trends (2020–2025)

Abstract

The generative artificial intelligence (GenAI) tools, particularly large language models (LLMs) such as ChatGPT, have attracted substantial scholarly attention regarding their implications for academic productivity in higher education. Despite the exponential growth of publications in this field, a consolidated bibliometric mapping of the intersection between GenAI and academic productivity remains limited. This study presents a bibliometric analysis of 1,234 relevant Scopus-indexed publications from 2020 to 2025. The analysis examines publication trends, leading countries, journals, authors, institutions, and thematic clusters. The study reveals that the United States, China, and the United Kingdom emerge as dominant contributors in this research stream. The study also reveals three principal thematic clusters: (1) GenAI applications in educational settings, (2) user adoption frameworks and technology acceptance, and (3) research productivity and ethical implications. The study identifies persistent challenges including academic integrity concerns, regional disparities in AI tool access, and insufficient longitudinal investigation. Implications for researchers, institutions, and policymakers are discussed.