Artificial Intelligence And The Transformation Of English Language Studies: A Bibliometric Perspective (2018–2025)

Thanh Thuy Tong

doi:10.18535/ijsrm/v13i10.el05

Abstract

Artificial intelligence (AI) is reshaping English language studies at an unprecedented pace, particularly since the emergence of generative models such as ChatGPT in 2022. It should be emphasized that although the number of academic publications in this domain has risen rapidly, large-scale bibliometric syntheses remain scarce, leaving research trends, thematic clusters, and knowledge gaps insufficiently systematized. This study draws on Scopus data from 2018 to 2025, applying Bibliometrix and VOSviewer to analyze 850 cleaned records. Findings confirm that publication output has grown exponentially, with a dramatic surge in 2024–2025; journals such as Education and Information Technologies and Computer Assisted Language Learning stand out as leading outlets; and the most influential scholars are concentrated in China, South Korea, and the United States, while country collaboration networks reveal a distinctly multipolar structure. Notably, keyword mapping and thematic evolution indicate a shift from traditional tools such as machine translation and intelligent tutoring systems toward emerging topics including ChatGPT, automated feedback, and online learning. In other words, the study not only consolidates evidence of the field’s rapid transformation but also suggests future research directions, including extending inquiry to multiple language skills, developing integrative theoretical frameworks, and advancing cross-cultural empirical studies. Taken together, these contributions provide value for both academics and policymakers seeking to integrate AI into foreign language education strategies in an effective and sustainable manner.

Keywords

mapping and thematic evolution indicate a shift from traditional tools such as machine translation and intelligent tutoring systems toward emerging topics including ChatGPTautomated feedbackand online learning

References

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[refR-1] Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007DOI ↗Google Scholar ↗

[refR-2] Choi, J., Hickman, L., Monaghan, J., & Le, Q. T. (2023). Generative AI in education: Opportunities, challenges, and future research. Computers & Education: Artificial Intelligence, 4, 100128. https://doi.org/10.1016/j.caeai.2023.100128DOI ↗Google Scholar ↗

[refR-3] Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070DOI ↗Google Scholar ↗

[refR-4] Dwivedi, Y. K., Hughes, L., Baabdullah, A. M., Ribeiro-Navarrete, S., Giannakis, M., Al-Debei, M. M., Dennehy, D., Metri, B., Buhalis, D., & Papagiannidis, S. (2023). Metaverse beyond the hype: Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642DOI ↗Google Scholar ↗

[refR-5] Kasneci, E., Seegerer, S., Budde, M., Häusler, J., Fischer, F., & Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274DOI ↗Google Scholar ↗

[refR-6] Kohnke, L., & Moorhouse, B. L. (2022). Adopting artificial intelligence applications in EFL classrooms: Teachers’ perceptions and practices. Computer Assisted Language Learning, 35(7), 1345–1365. https://doi.org/10.1080/09588221.2021.1955119DOI ↗Google Scholar ↗

[refR-7] Li, V., & Hafner, C. A. (2023). Exploring the pedagogical affordances of ChatGPT in higher education: Implications for English language teaching. Education and Information Technologies, 28(10), 10819–10838. https://doi.org/10.1007/s10639-023-11958-2DOI ↗Google Scholar ↗

[refR-8] Lin, T. B., & Lan, Y. J. (2022). Artificial intelligence and language learning: Research trends and future directions. Journal of Computer Assisted Learning, 38(6), 1547–1563. https://doi.org/10.1111/jcal.12695DOI ↗Google Scholar ↗

[refR-9] Marginson, S. (2022). Global science and national comparison: International collaboration in English-medium research. Studies in Higher Education, 47(3), 441–456. https://doi.org/10.1080/03075079.2020.1750581DOI ↗Google Scholar ↗

[refR-10] Nguyen, T. M., & Tran, T. (2023). AI-powered feedback for academic English writing: A case study with EFL learners. Asia Pacific Education Review, 24(3), 357–372. https://doi.org/10.1007/s12564-023-09786-5DOI ↗Google Scholar ↗

[refR-11] Sailer, M., Stadler, M., Schultz-Pernice, F., Franke, U., Schöffmann, C., Paniotova, V., & Fischer, F. (2021). Technology-related teaching skills of university instructors—A systematic review. Computers & Education, 173, 104271. https://doi.org/10.1016/j.compedu.2021.104271DOI ↗Google Scholar ↗

[refR-12] Sun, P. P., & Chen, J. (2022). Intelligent tutoring systems for English language learning: A systematic review. ReCALL, 34(2), 178–196. https://doi.org/10.1017/S0958344022000095DOI ↗Google Scholar ↗

[refR-13] Viberg, O., Khalil, M., & Baars, M. (2022). Self-regulated learning and learning analytics in online learning environments: A review of empirical research. Computers in Human Behavior, 136, 107382. https://doi.org/10.1016/j.chb.2022.107382DOI ↗Google Scholar ↗

[refR-14] Zhang, W., & Zou, D. (2023). Generative AI for EFL writing: Potentials and challenges of ChatGPT. System, 114, 102991. https://doi.org/10.1016/j.system.2023.102991DOI ↗Google Scholar ↗

[refR-15] Zhao, Y., & Wibowo, S. (2021). Artificial intelligence in education: A bibliometric analysis. Education and Information Technologies, 26, 4503–4525. https://doi.org/10.1007/s10639-021-10494-xDOI ↗Google Scholar ↗

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