BIBLIOMETRIC ANALYSIS OF STUDIES ON THE USE OF ARTIFICIAL INTELLIGENCE IN FOREIGN LANGUAGE EDUCATION
TL;DR
The results reveal that China, the United States, and the UAE lead the field in publication volume and the absence of sustained international co-authorship highlights the fragmented and locally bound nature of research collaboration in this field.
Abstract
This study aims to analyze the scientific literature on artificial intelligence (AI) applications in foreign language education using a bibliometric approach. A total of 1,188 articles published between 2020 and 2025 in the Web of Science Core Collection were examined based on the keywords “AI-assisted language learning,” “NLP in education,” and “intelligent tutoring systems.” The analysis was conducted using VOSviewer software, which enabled the examination of country-author-institution distributions, citation networks, keyword clusters, and co-authorship structures. The results reveal that China, the United States, and the UAE lead the field in publication volume. Influential scholars such as Barrot (2023), Davis (2022), and Bandura (2021) were identified with high citation impacts. Frequently recurring keywords include “ChatGPT,” “critical thinking,” “learning motivation,” and “writing skills.” Co-authorship mapping reveals limited interdisciplinary collaboration and predominantly region-based partnerships, while the absence of sustained international co-authorship highlights the fragmented and locally bound nature of research collaboration in this field. The findings highlight the evolving role of AI in language education, particularly in adaptive learning, student perception, and instructional innovation. This study contributes to the field by quantitatively identifying epistemological patterns and research gaps, offering a strategic roadmap for future studies in AI-supported language learning.