A Bibliometric Analysis of Artificial Intelligence in EFL Education: Trends, Collaborations, Thematic Evolution, and Pedagogical Implications
Abstract
Background. Given the rapid advances in technology, artificial intelligence (AI) offers significant potential in educational contexts by enabling personalized learning and enhancing learner engagement. However, the development of AI-related scholarship in English as a Foreign Language (EFL) education, including its publication trajectory, collaborative networks, citation patterns, and thematic distribution, remains insufficiently explored.
Purpose. This study examines the extant research on AI in EFL education, spanning from the early developments in 1996 to mid-2025, with the aim of identifying structural patterns, thematic concentrations, and under-examined areas to inform future research priorities and pedagogical directions.
Method: By analyzing 829 publications from the Web of Science (WoS) and Scopus databases, screened by a single researcher, and utilizing the VOSviewer software and Microsoft Excel, the study identifies and interprets significant trends, including publication trends, international collaborative networks, citation impact and thematic developments across the literature. Bibliometric indicators, country collaboration networks, journal citation patterns, and keyword co-occurrence structures were analysed; results should be interpreted in light of English-language database coverage, single-researcher screening, and threshold-dependent keyword mapping.
Results. The findings reveal an exponential rise in research output since 2022, with China leading in publication volume, citation impact, and collaborative centrality. Highly impactful journals demonstrate substantial engagement across disciplinary areas, with Computer Assisted Language Learning emerging as the most influential source. The co-occurrence analysis of Keywords highlights a concentration on generative AI tools, particularly ChatGPT, and AI-enhanced writing, while areas such as speaking, listening, reading, and self-regulated learning appeared underrepresented in the keyword structure of the corpus, suggesting potential gaps in the thematic scope of current AI-related EFL research.
Conclusion. This study provides the first bibliometric account of AI in EFL education to encompass the generative AI period, simultaneously mapping collaboration networks, citation trajectories, and keyword structures. The findings offer evidence-based insights into research trends and thematic imbalances, with implications for future inquiry and pedagogical innovation in AI-enhanced EFL education.
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