Tracing the Evolution of Artificial Intelligence in English Teaching: A Bibliometric Analysis

Amid the accelerating integration of Artificial Intelligence (AI) into English teaching, existing research remains fragmented and lacks a holistic understanding of the field’s evolution. To address this gap, the present study offers a pioneering bibliometric analysis that integrates performance analysis with science mapping to systematically outline the research landscape of AI in English teaching. Specifically, it seeks to answer four research questions concerning publication trends, highly cited documents, influential contributors and dominant research themes. Drawing on data extracted from the Scopus database spanning 1982 to 2025, screened in accordance with the PRISMA methodology, and analysed using VOSviewer, Biblioshiny, and Bibliomagika. The study identifies a sharp increase in publication activity in recent years, although citation growth has remained comparatively moderate. Seminal contributions, such as Hsu (2017) and influential source titles like Computers and Education, are found to play pivotal roles in shaping the field. In terms of geographical distribution, China emerges as the most prolific and internationally collaborative contributor. Further analysis reveals a discernible shift from conventional AI-assisted tutoring systems towards more interactive, adaptive, and personalised learning environments, accompanied by increasing interdisciplinary integration. By offering a comprehensive synthesis among the earliest in this research domain, the study not only deepens understanding of AI’s pedagogical potential but also provides valuable insights and strategic directions for future empirical research and practical applications across diverse educational contexts.