BIBLIOMETRIC ANALYSIS OF STUDIES ON THE CONCEPT OF USING ARTIFICIAL INTELLIGENCE IN ENGLISH LANGUAGE TEACHING USING VOSVIEWER

510 Vol. 5, No. 11 – Special Issue (EJAR) ISSN: 2181-2020 LPTIENP International Scientific-Practice Conference on “Linguistics and Pedagogical Technologies: International Experience and National Practice” Denau, November 20, 2025 in-academy.uz/index.php/ejar BIBLIOMETRIC ANALYSIS OF STUDIES ON THE CONCEPT OF USING ARTIFICIAL INTELLIGENCE IN ENGLISH LANGUAGE TEACHING USING VOSVIEWER Prof. Dr. Mehmet Nuri GÖMLEKSİZ Fırat Üniversitesi, Eğitim Fakültesi, [email protected] – ORCID: 0000-0002-8268-0163 Tarık İNCE Malatya Turgut Özal Üniversitesi, Yabancı Diller Yüksekokulu, [email protected] – ORCID: 0000-0002-7085-2706 https://doi.org/ 10.5281/zenodo.17935057 Abstract: Artificial intelligence (AI) is significantly shaping English language teaching (ELT), but there is still limited comprehensive bibliometric research on this topic. This study maps the intellectual structure, collaboration models, and emerging themes of AI-related ELT research indexed in the Web of Science (WoS) between 2021 and 2025. Following a structured screening process 7,976 records were analyzed using bibliometric techniques and visualized with VOSviewer. Findings reveal a growing and globally widespread literature with significant collaboration and citation patterns. Coauthorship clustering (≥2 publications; ≥2 citations) revealed 43 authors clustered into 9 groups with 78 links; the most prolific authors included Hwang (18), Kohnke & Lucas (18), and Tarrayano (10). Citation analysis of authors (≥5 publications; ≥5 citations) revealed 73 authors connected to each other in 11 clusters (total connection strength = 585), and Hwang received the highest number of citations (18). By country citation impact analysis (≥5 publications; ≥5 citations) shows that the US is the leader in citation impact (19,391), China is the leader in number of publications (1,074) and second in number of citations (14,720), followed by the UK (7,951), Australia (6,130), and Taiwan (5,178). The keyword co-occurrence (≥10 repetitions) uncovered 287 keywords across 8 clusters (total link strength = 12,749), with “artificial intelligence,” “chatbot,” “ChatGPT,” and “large language models” leading the list. A corporate review highlighted the high productivity of Hong Kong universities and Johns Hopkins University's high citation impact. These findings suggest a blueprint for future research priorities, inter-country collaboration, and evidence-based integration of artificial intelligence into English language teaching. Keywords: AI, English Language Teaching, ELT, Bibliometric Analysis, INTRODUCTION The dramatic transformation in information and communication technologies in the twenty-first century has profoundly impacted educational paradigms, mandating a shift from traditional teaching methods to technology-supported learning environments (Liang vd., 2023). Essential technologies like PCs and internet access are used almost everywhere in foreign language learning in many developed countries (Golonka vd., 2014). It is reasonable to expect an increase in the number of studies discussing ways to implement artificial intelligence in educational settings and potential approaches to promoting and teaching artificial intelligence knowledge at all levels of education (Hwang vd., 2020; Yu, 2025). Especially when it demonstrates its strong potential in facilitating artificial intelligence training applications, language education has also been supported by various educational technologies (W. Liu vd., 2024). The diversity in both technologies and pedagogies offers versatile options for implementing technology-enhanced language learning (TELL) in in-class teaching and learning as well as out-of-class learning experiences (Zou vd., 2018). The trend of integrating artificial intelligence into education has accelerated the need to analyze artificial intelligence research (Hidayat vd., 2025; Huang vd., 2023). 511 Vol. 5, No. 11 – Special Issue (EJAR) ISSN: 2181-2020 LPTIENP International Scientific-Practice Conference on “Linguistics and Pedagogical Technologies: International Experience and National Practice” Denau, November 20, 2025 in-academy.uz/index.php/ejar In the context of English language teaching (ELT), AI has gone beyond being merely a supplementary tool to become a central actor that transforms students' language acquisition processes (Huang vd., 2023). AI-based systems are integrated into pedagogical processes through various applications such as Intelligent Tutoring Systems (ITS), Automated Writing Evaluation (AWE), and chatbots, with the aim of developing students' writing, reading, listening, and speaking skills (Paek & Kim, 2021). A review of the literature reveals that academic interest in the use of AI in education has steadily increased over the past twenty years, with a dramatic rise in the number of publications (Kismoyo vd., 2023). Therefore, comprehensive analyses that address global research trends, collaboration networks, and intellectual structures in AI-assisted English language teaching from a holistic perspective are needed (Guo vd., 2024). The purpose of this study is to conduct a systematic analysis of the development of academic research on the use of artificial intelligence in English language teaching, which is important for evaluating the existing knowledge base. Bibliometric analysis method provides robust tools for identifying core research trends, the most highly cited authors, collaborations, and knowledge networks within a field (Ahmed & Rashid, 2024; Kismoyo vd., 2023). This approach assists researchers in identifying their research trends and gauging the impact of scientific articles, while also enabling researchers, institutions, and countries to benchmark their academic performance (Q. Liu vd., 2025). In this study, research on the concept of AI usage in English language teaching was analyzed bibliometrically using VOSviewer software. The study investigated the publication distribution in the relevant literature, the most influential authors, institutions, and countries, keyword trends, and research collaborations. Using VOSviewer software, these analyses were visualized, revealing current research trends and potential areas for development in this field. It aimed to contribute to this field by visualizing the main themes, research trends, and collaboration structures in the literature. METHOD This study employed bibliometric analysis methods to map the evolution, trends, and conceptual framework of the scientific literature on artificial intelligence (AI) in English language teaching (ELT). Bibliometric analysis is a technique that measures scientific productivity and impact in a specific field using quantitative data, thereby objectively revealing research gaps and emerging themes (Hidayat vd., 2025). Search keywords were determined by referencing previous bibliometric studies and include the following key terms: The terms “artificial intelligence,” “machine learning,” and ‘chatbot’ were combined with the terms “English language teaching,” “EFL,” “ESL,” “language learning,” and “education.” The search query was performed on the title, abstract, and keywords (Topic/Title-Abs-Key). To ensure academic accuracy, the analysis is limited to peer-reviewed journal articles; book chapters, editor's notes, and conference papers (except for certain conferences of particular importance in the field) are excluded. The WOS database search using the keyword “AI in English Language Teaching” yielded 16,727 results. When articles were included, this number corresponded to 10,978 results. When considering studies conducted within the last 5 years (2021-2025), 7,976 results were obtained. In terms of disciplines, it was determined that the vast majority of studies were in the fields of Engineering Electrical Electronic 18,062, Computer Science Information Systems 15,037, Education Educational Research 14,910, Computer Science Artificial Intelligence 11,406, Telecommunications 8,737. The data obtained was examined through author-citation-journal, country-institution, and keyword analyses. Content indexed in Web of Science was used as the database criterion. The studies obtained are shown in Table 1. 512 Vol. 5, No. 11 – Special Issue (EJAR) ISSN: 2181-2020 LPTIENP International Scientific-Practice Conference on “Linguistics and Pedagogical Technologies: International Experience and National Practice” Denau, November 20, 2025 in-academy.uz/index.php/ejar Data Presentation Bibliometrics is defined as a statistical method that can quantitatively analyze articles related to a specific topic using mathematical methods (Kazak ve Kazak, 2023). The VOSviewer program was used in this study. Vosviewer is a program that creates network maps using bibliometric data (data obtained from databases such as Web of Science, Scopus, and PubMed). These maps visualize the relationships between elements such as authors, institutions, keywords, and articles. Table 1: The studies obtained from the database Data accessed through the Webos database using the concept of AI in Language Teaching (n: 16,727) When limited to documents in articles in the context of English language teaching (n: 10,978) Publication year of included articles: last five years 2021-2025 (n: 7,976) Graphic 1: Prism flow diagram FINDINGS Co-authorship of Authors The co-author analysis was conducted to identify authors with the most connections and collaborations. The network map in the analysis was created based on the criteria of at least 2 publications and at least 2 citation. Within the analysis of authors with the highest connections, 43 authors grouped into 9 clusters and a total of 78 connections were observed. The authors with the most citations are Kohnke & Lucas with 18 citations, Chai, Ching Sing with 15 citations, and Hwang, Gwo-jen with 10 citations, while the most connected authors are Kohnke & Lucas, Chai, Ching Sing, Hwang, Gwo-jen, Rezai & Afseen, and An Xin. The authors who produced the most works were Hwang, Gwo-jen (16), Kohnke & Lucas (13), and Tarrayano & Veronico (10). The co-author analysis image for the specified analysis is presented below in Figure 1. Figure 1: Co-author networks demonstrating collaboration between authors Citation of authors 513 Vol. 5, No. 11 – Special Issue (EJAR) ISSN: 2181-2020 LPTIENP International Scientific-Practice Conference on “Linguistics and Pedagogical Technologies: International Experience and National Practice” Denau, November 20, 2025 in-academy.uz/index.php/ejar The citation network map related to the author citation analysis, conducted with a minimum of 5 publications and a minimum of 5 citation criterion to determine citation relationships and networks, is presented in Figure 2 below. In the analysis conducted on 73 units found to be interconnected, a total of 11 clusters, 229 connections, and a total connection strength of 585 were identified. Within these criteria, the authors receiving the most citations were Hwang Gwo-jen with 18 citations, Guo & Kai, with 14 citations, and Annamalai & Nagalatchime with 14 citations. However, these authors were not at the top in terms of total connection strength. The Authors' citation networks image for the specified analysis is presented below in Figure 2. Figure 2. Authors' citation networks Citation of countries A citation network map based on the countries of origin of publications in the WOS database has been created and is presented in the table below. In this context, an analysis was conducted on 10 countries with relationships between them, based on the criteria of at least 5 works published and 5 citations received by a country. The analysis revealed the existence of 2 clusters, 45 connections, and a total connection strength of 3548. The countries receiving the most citations are USA with 19391, China with 14720, the United Kingdom with 7951, Australia with 6130, and the Taiwan with 5178. China ranks first in the number of publications (1074) and the USA ranks first in number of citations (19391). The countries' citation networks image for the specified analysis is presented below in Figure 3. Figure 3. Countries' citation networks Co-occurunce of all keywords The concept of English teaching methods is used in relation to many keywords. This section presents a table of the most frequently used words and which words are used in connection with each other. In this context, when words repeated at least 10 times are included, 287 keywords reached a 514 Vol. 5, No. 11 – Special Issue (EJAR) ISSN: 2181-2020 LPTIENP International Scientific-Practice Conference on “Linguistics and Pedagogical Technologies: International Experience and National Practice” Denau, November 20, 2025 in-academy.uz/index.php/ejar total of 8 clusters, 4986 connections, and 12749 total connection strength. The most frequently preferred words in studies related to the concept of AI in English teaching were artificial intelligence with 1100 repetitions, chatbot with 912, chatgpt with 596, chatbots with 377, English Language Teaching with 277 repetitions, large language models with 200 repetitions. In terms of total connection strength, artificial intelligence had 3140, chatbot had 2500, and chatgpt had 1777. The Co-occurunce of all keywords image for the specified analysis is presented below in Figure 4. Figure 4. Co-occurunce of all keywords Citation of organizations To determine the network map of inter-institutional citations, an analysis was conducted on 125 observation units identified as having a relationship, based on the criterion that an institution must have at least 5 works and at least 5 citations. In the publication ranking, University of Hong Hong ranks first with 78 publications, followed by Education University of Hong Hong with 53 publications and Chinese University of Hong Kong with 51 publications. In the citation ranking, Johns Hopkins University ranked first with 1592 citations, University Hong Hong ranked second with 1351 citations, University Calif San Diago ranked third with 1341 citations. In terms of total link strength, Educ University Hong Hong ranked first with 216. The Institutional citation networks image for the specified analysis is presented below in Figure 5. Figure 5. Institutional citation networks DISCUSSION AND CONCLUSION In this study, academic publications related to the concept of AI in English teaching methods published in Web of Science were analyzed using bibliometric methods, and current trends, collaborations, most frequently used keywords, prominent authors, and thematic densities in this field were visualized using VOSviewer software. Our study examined publications from the last five years 515 Vol. 5, No. 11 – Special Issue (EJAR) ISSN: 2181-2020 LPTIENP International Scientific-Practice Conference on “Linguistics and Pedagogical Technologies: International Experience and National Practice” Denau, November 20, 2025 in-academy.uz/index.php/ejar (2012 – 2025) and determined that AI in English teaching is receiving increasing attention in the literature and that there has been a notable increase in the number of publications in this field, particularly in the last five years. In co-authorship analysis, it has been observed that authors tend to cluster to a large extent, with certain names achieving greater productivity through academic collaborations. It is noteworthy that the authors receiving the most citations are not necessarily the strongest names in terms of collaboration. This situation demonstrates that strategic collaborations, not just the number of publications, are a decisive factor in terms of academic impact. Based on cross-country citation analysis, the impact of this field is largely concentrated in a relatively small group of countries and is organized into two main collaboration/citation clusters (45 links; total link strength = 3548). With the highest number of citations (19,391), the US is the most influential country in terms of impact, while China leads in terms of publication count (1,074) but ranks second in terms of citation impact (14,720 citations). Following these two leaders, the United Kingdom (7,951), Australia (6,130), and Taiwan (5,178) form the second group of countries with high citations. The keyword analysis showed that research on artificial intelligence in English language teaching has centered around a series of prominent concepts and related networks. When keywords appearing at least 10 times were included, 287 keywords formed 8 clusters and produced a total of 12,749 link strengths with 4,986 links. The most frequently used keywords were “artificial intelligence” (1,100 times), “chatbot” (912), “ChatGPT” (596), ‘chatbots’ (377), “English language teaching” (277), and “large language models” (200). In terms of network centrality (total link strength), “artificial intelligence” (3,140), “chatbot” (2,500), and “ChatGPT” (1,777) were the strongest connection nodes, indicating that these terms not only appeared most frequently but also linked multiple subtopics within the literature. In organisational analyses, 125 networked institutions (≥5 publications and ≥5 citations) were identified, forming the institutional citation network. Hong Kong University (78) ranked first in terms of publication output, followed by Hong Kong Education University (53) and Hong Kong Chinese University (51). Johns Hopkins University (1,592) ranked first in terms of citation impact, followed by the University of Hong Kong (1,351) and the University of California, San Diego (1,341). In terms of collaboration strength, the Hong Kong University of Education had the highest total connection strength (216). References: 1. Ahmed, T., & Rashid, D. M. (2024). Science Mapping of English Language Teaching Methods Published in Scopus from 2000 to 2022. International Journal of Information Management Sciences, 8(1), 197-212. 2. Golonka, E. M., Bowles, A. R., Frank, V. M., Richardson, D. L., & Freynik, S. (2014). Technologies for foreign language learning: A review of technology types and their effectiveness. Computer Assisted Language Learning, 27(1), 70-105. https://doi.org/10.1080/09588221.2012.700315 3. Guo, S., Zheng, Y., & Zhai, X. (2024). Artificial intelligence in education research during 2013–2023: A review based on bibliometric analysis. Education and Information Technologies, 29(13), 16387-16409. https://doi.org/10.1007/s10639-024-12491-8 4. Hidayat, D. N., Ramadhan, S., Mahlil, M., Mason, J., Hartono, R., & Muslimin, A. I. (2025). Research trends in artificial intelligence in English language teaching (ELT): A bibliometric analysis. 516 Vol. 5, No. 11 – Special Issue (EJAR) ISSN: 2181-2020 LPTIENP International Scientific-Practice Conference on “Linguistics and Pedagogical Technologies: International Experience and National Practice” Denau, November 20, 2025 in-academy.uz/index.php/ejar Studies in English Language and Education, 12(2), 560-578. https://doi.org/10.24815/siele.v12i2.39339 5. Huang, X., Zou, D., Cheng, G., Chen, X., & Xie, H. (2023). Trends, Research Issues and Applications of Artificial Intelligence in Language Education. Educational Technology & Society, 26(1), 112-131. 6. Hwang, G.-J., Xie, H., Wah, B. W., & Gašević, D. (2020). Vision, challenges, roles and research issues of Artificial Intelligence in Education. Computers and Education: Artificial Intelligence, 1, 100001. https://doi.org/10.1016/j.caeai.2020.100001 7. Kismoyo, C. P., Kartowagiran, B., Suyanto, S., & Ramadhan, S. (2023). Analysis of Continuity of Care Research Developments Based on the Scopus Database 20 20-2023: A Bibliometric Study. International Journal of Membrane Science and Technology, 10(3), 164-173. https://doi.org/10.15379/ijmst.v10i3.1499 8. Liang, J.-C., Hwang, G.-J., Chen, M.-R. A., & Darmawansah, D. (2023). Roles and research foci of artificial intelligence in language education: An integrated bibliographic analysis and systematic review approach. Interactive Learning Environments, 31(7), 4270-4296. https://doi.org/10.1080/10494820.2021.1958348 9. Liu, Q., Ali, N. L., & Lee, H. Y. (2025). Applying VOSviewer in a bibliometric review on English language teacher education research: An analysis of narratives, networks and numbers. Cogent Education, 12(1), 1-18. https://doi.org/10.1080/2331186X.2025.2449728 10. Liu, W., Lin, Y., & Yu, Z. (2024). A Bibliometric Analysis of Artificial Intelligence Chatbots in Language Education. Social Education Research, 59-78. https://doi.org/10.37256/ser.5120243372 11. Paek, S., & Kim, N. (2021). Analysis of Worldwide Research Trends on the Impact of Artificial Intelligence in Education. Sustainability, 13(14), 7941. https://doi.org/10.3390/su13147941 12. Yu, J. (2025). A Review on the Foreign Language Teaching Empowered by Artificial Intelligence in Recent Ten Years: A Scientometric Analysis Based on CiteSpace. International Journal of Social Science, Education and Humanities, 1(1), 115-124. https://doi.org/10.63313/IJSSEH.9012 13. Zou, D., Xie, H., & Wang, F. L. (2018). Future Trends and Research Issues of TechnologyEnhanced Language Learning: A Technological Perspective. Knowledge Management & E-Learning, 10(4), 426-440.