Mapping the Landscape: Bibliometric Insights into Artificial Intelligence in English as a Foreign Language (EFL) Education

Abstract

This study presents a comprehensive exploration of the bibliometric landscape surrounding the integration of AI in EFL education. The study spans 30 years (1994-2023), involving meticulous filtering based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. From an initial pool of 694 publications, rigorous screening criteria result in 661 relevant documents. The study utilizes VosViewer, a bibliometric analysis software tool, to visually represent co-occurrence relationships among authors, keywords, and publications. The analysis of the retrieved literature demonstrates a substantial proliferation in research productivity from 2017 onwards, particularly after 2020, with the number of publications experiencing a burgeoning increase. Some prominent trending themes, including “teaching mode”, “E-learning”, and “engineering education” were noted. Furthermore, the emerging hot-spots of research, e.g. speech recognition, Chatbot, machine-learning, are identified via overlaying visualization. This study contributes by providing a road-map of literature published over three decades (1994-2023), updating the existing body of knowledge in the field of AI integration in EFL education, and revealing the research hot-spots in this field. The findings have valuable implications for EFL practitioners, administrators as well as researchers.