Unveiling the thematic landscape of generative AI: a bibliometric analysis of emergingresearch trends

Abstract

Purpose This study aims to explore the evolving research landscape of generative artificial intelligence (Gen-AI) by conducting a comprehensive bibliometric analysis. It seeks to identify prevailing themes, influential contributors and underexplored areas within the Gen-AI domain. Design/methodology/approach A bibliometric approach was adopted using data extracted from the Scopus database, focusing on publications from 2019–2024. The study used the Bibliometrix R package to analyze keyword trends, publication output, citation impact, country-wise productivity and thematic evolution. Findings The results reveal a significant rise in academic interest in Gen-AI, particularly since the release of models like ChatGPT. Key research areas include natural language processing, AI ethics, chatbots and education. However, several gaps persist – particularly in ethical use, bias mitigation and governance frameworks – suggesting the need for more focused and interdisciplinary research. Research limitations/implications The study is limited to publications indexed in the Scopus database and written in English. Valuable insights from other databases or non-English literature may have been excluded. The rapidly evolving nature of the field may also outpace the analysis over time. Originality/value This research provides a timely and data-driven overview of Gen-AI scholarship, offering actionable insights for researchers, practitioners and policymakers. It also highlights critical areas requiring further inquiry, contributing to a more balanced and ethically grounded advancement of generative technologies.

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