Generative AI in Law and Legal Education: A Scientometric Mapping of Research Evolution and Emerging Frontiers

Abstract

Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) are reshaping legal research, practice, and education, yet scholarly research at their intersection remains dispersed across legal, educational, and computing literatures. This study presents a scientometric mapping of research on GenAI, law, and legal education. Following the SPAR-4-SLR protocol, 887 Scopus-indexed publications retained from an initial retrieval of 1,207 records published between 2023 and 2026 were analysed using Biblioshiny and VOSviewer. The corpus comprises 530 sources and 2,218 authors, with an annual growth rate of 49.38%, a mean of 13.56 citations per document, and 20.74% international co-authorship. Records for 2026 are incomplete because the search was conducted on 26 June 2026. The United States, China, and the United Kingdom were the leading contributing countries, while authorship remained decentralised. Keyword co-occurrence, Multiple Correspondence Analysis, thematic mapping, and hierarchical clustering identified four knowledge domains: AI-enhanced legal education, AI governance and regulation, copyright and intellectual property, and AI-supported legal practice. Research priorities shifted from technological capability towards governance, legal reasoning, AI literacy, and regulatory frameworks, positioning the field as a consolidating interdisciplinary domain with implications for scholarship, curriculum design, and policy development.