從文獻計量學看AI在英語教學中的發展脈絡與未來方向－－基於WoS資料庫的分析

Development Context and Future Directions of AI in EFL Teaching Through Bibliometric Analysis: Based on the Web of Science Database

本研究基於Web of Science（WoS）資料庫，針對2014～2024年間有關人工智慧（Artificial Intelligence，AI）應用於英語教學的研究文獻進行系統性的文獻計量學分析。共檢索出135篇相關期刊論文，同時，本研究亦從國家/地區分布、期刊、高被引文獻、研究者與關鍵字等多元面向進行深入探究與分析。研究結果顯示，中國大陸和臺灣是該領域研究發表量最多的國家/地區，其中中國的研究人員和機構在該領域展現顯著影響力。在研究成果發表量方面，名列前三的學術機構依序為香港中文大學、國立臺灣科技大學和溫州大學，顯示這些機構在AI與英語教學領域中的領先地位。在主要期刊方面，研究顯示Frontiers in Psychology與Education and Information Technologies是發表相關研究成果的主要平臺，並自高被引用論文分析進一步確認這些期刊的核心地位，揭示其在引領AI與英語教學整合研究中的關鍵作用。
從研究內容演變分析，2014～2021年間AI在英語學習研究中主要集中於基礎性軟體工程研究，如智能輔助教學工具開發和學習平臺優化，此階段研究重點放在技術可行性及基礎架構建設方面。隨著技術成熟與應用場景擴展，2020～2024年間相關研究產出逐漸轉向實證研究和综述型研究，關注AI如何有效提高英語學習效率與效果，包括個性化學習體驗、智能評估系統與AI技術在不同學習環境中的適用性。整體而言，研究結果顯示AI在英語教學應用呈現日益多元化趨勢，研究熱點隨著時間推移從技術導向轉向以應用效果為核心的實證研究。
本研究不僅揭示AI在英語教學領域的發展脈絡，還為未來的研究方向提供有價值的參考。本研究成果可為政策制定者和研究者提供AI應用於英語教學領域的最新研究趨勢，並有助於引導教育政策制定與學術研究規劃。

This study systematically analyzes academic literature on applying artificial intelligence (AI) in English language teaching using bibliometric methods, utilizing data from the Web of Science (WoS) database from 2014 to 2024. 135 relevant journal articles were identified and examined across multiple dimensions, including countries/regions, prominent journals, highly cited papers, authors, and keywords. The findings indicate that Mainland China and Taiwan have the highest publication volumes in this field, with Chinese researchers and institutions demonstrating significant influence. The Chinese University of Hong Kong, National Taiwan University of Science and Technology, and Wenzhou University are the top three institutions in the publication volume, highlighting their prominent roles in AI and English language teaching. Regarding critical journals, the study identifies Frontiers in Psychology and Education and Information Technologies as primary platforms for publishing relevant research findings. The analysis of highly cited papers further underscores the importance of these journals, highlighting their essential role in advancing the incorporation of AI in English language teaching research.
Analysis of the evolution of research content reveals that, from 2014 to 2021, AI research in English learning predominantly focused on foundational software engineering topics, such as developing intelligent tutoring tools and optimizing learning platforms. During this phase, the research emphasized technical feasibility and the construction of foundational infrastructures. As AI technology matures and its application scenarios expand, research results from 2020 to 2024 will gradually shift to empirical and retrospective studies, focusing on how AI can effectively improve the efficiency and effectiveness of English learning, including personalized learning experience, intelligent assessment systems, and the applicability of AI technology in diverse learning environments. Overall, the findings indicate a growing diversification in how AI is utilized in English language education, with research hotspots shifting from a technology-oriented approach to empirical studies focused on practical outcomes.
This study not only clarifies the developmental trajectory of AI in the field of English language teaching but also offers valuable insights for future research directions. The findings provide policymakers and researchers with crucial references, highlighting the latest AI application trends in English teaching, thereby supporting the formulation of educational policies and academic research planning.