MAPPING THE RISE OF GENERATIVE AI IN L2 WRITING FEEDBACK: A BIBLIOMETRIC ANALYSIS
DOI:
https://doi.org/10.35631/IJEPC.1165004Keywords:
Artificial Intelligence, Generative AI, Writing FeedbackAbstract
Generative Artificial Intelligence (GenAI) is transforming Second Language (L2) writing instruction by enabling new forms of automated and interactive feedback that are personalised to individual learners. Academic interest has increased rapidly. However, the development, intellectual structure and emerging directions of GenAI mediated L2 writing feedback are insufficiently synthesised at the bibliometric level. The study maps the evolution and research landscape of GenAI in L2 writing feedback by examining publication trends, citation impact, geographical contributions, research themes and international collaboration. A bibliometric analysis was carried out on Scopus using an advanced search strategy that combined two main concepts: GenAI and writing feedback. The bibliographic records were then cleaned and standardised using OpenRefine, the publication and citation patterns were examined using Scopus Analyser, and the keyword co-occurrence and country level co-authorship networks were visualised with VOSviewer. The final dataset included 1,307 publications from 2023 to August 2026. The findings show a fast-growing area, with GenAI, Artificial Intelligence (AI), ChatGPT, large language models, higher education and feedback being the most frequent keywords. Specific notions such as academic writing, L2 writing, writing feedback, feedback literacy and English as a Foreign Language (EFL) writing and AI generated feedback seem to indicate an increasingly focused trajectory around language learning and writing development. The United States and China are the output giants. The analysis at country level shows an increasingly international landscape, with collaboration concentrated among major research hubs. Therefore, GenAI in L2 writing feedback has swiftly evolved from broad investigations of AI usage to more focused studies on feedback, writing development, learner engagement and pedagogical integration. This area is now an important and growing field of research in language education.
Downloads
References
Al-Khoury, A., Hussein, S. A., Abdulwhab, M., Aljuboori, Z. M., Haddad, H., Ali, M. A., Abed, I. A., & Flayyih, H. H. (2022). Intellectual Capital History and Trends: A Bibliometric Analysis Using Scopus Database. Sustainability (Switzerland), 14(18). https://doi.org/10.3390/su141811615
Alves, J. L., Borges, I. B., & De Nadae, J. (2021). Sustainability in complex projects of civil construction: Bibliometric and bibliographic review. Gestao e Producao, 28(4). https://doi.org/10.1590/1806-9649-2020v28e5389
Appio, F. P., Cesaroni, F., & Di Minin, A. (2014). Visualizing the structure and bridges of the intellectual property management and strategy literature: a document co-citation analysis. Scientometrics, 101(1), 623–661. https://doi.org/10.1007/s11192-014-1329-0
Assyakur, D. S., & Rosa, E. M. (2022). Spiritual Leadership in Healthcare: A Bibliometric Analysis. Jurnal Aisyah : Jurnal Ilmu Kesehatan, 7(2). https://doi.org/10.30604/jika.v7i2.914
Barrot, J., & Bui, H. (2026). Generative Artificial Intelligence for Automated Qualitative Feedback: A Cross-Comparison of Prompting Strategies. RELC Journal, 57, 107–132. https://doi.org/10.1177/00336882251412561
Crosthwaite, P., & Sun, S. (2025). Generative AI and L2 Written Feedback Studies: A Scoping Review. RELC Journal, 57, 207–219. https://doi.org/10.1177/00336882251386530
di Stefano, G., Peteraf, M., & Veronay, G. (2010). Dynamic capabilities deconstructed: A bibliographic investigation into the origins, development, and future directions of the research domain. Industrial and Corporate Change, 19(4), 1187–1204. https://doi.org/10.1093/icc/dtq027
Escalante, J., Pack, A., & Barrett, A. (2023). AI-generated feedback on writing: insights into efficacy and ENL student preference. International Journal of Educational Technology in Higher Education, 20(1). https://doi.org/10.1186/s41239-023-00425-2
Giannakos, M., Azevedo, R., Brusilovsky, P., Cukurova, M., Dimitriadis, Y., Hernandez-Leo, D., … Rienties, B. (2025). The promise and challenges of generative AI in education. Behaviour & Information Technology, 44(11), 2518–2544. https://doi.org/10.1080/0144929X.2024.2394886
Fahimnia, B., Sarkis, J., & Davarzani, H. (2015). Green supply chain management: A review and bibliometric analysis. In International Journal of Production Economics (Vol. 162, pp. 101–114). https://doi.org/10.1016/j.ijpe.2015.01.003
Fan, Y., Tang, L., Le, H., Shen, K., Tan, S., Zhao, Y., Shen, Y., Li, X., & Gašević, D. (2025). Beware of metacognitive laziness: Effects of generative artificial intelligence on learning motivation, processes, and performance. British Journal of Educational Technology, 56(2), 489–530. https://doi.org/10.1111/bjet.13544
Gu, D., Li, T., Wang, X., Yang, X., & Yu, Z. (2019). Visualizing the intellectual structure and evolution of electronic health and telemedicine research. International Journal of Medical Informatics, 130. https://doi.org/10.1016/j.ijmedinf.2019.08.007
Haristiani, N., Husaeni, D. N. Al, Herniwati, H., Sugihartono, S., & Abduh, A. (2025). Exploring trends, impact, and applications of artificial intelligence (AI) in language learning: From concept, definition, examples, to a comprehensive bibliometric analysis. SciEnggJ. https://doi.org/10.54645/202518supziv-58
Huiling, Ismail, L., & Han, W. (2024). A bibliometric analysis of artificial intelligence in language teaching and learning (1990–2023): evolution, trends and future directions. Education and Information Technologies, 29, 25211–25235. https://doi.org/10.1007/s10639-024-12848-z
Hyland, K. (2026). AI Feedback to L2 Writers. International Journal of TESOL Studies. https://doi.org/10.58304/ijts.260420
Jaleniauskiene, E., Lisaitė, D., & Daniusevičiūtė-Brazaitė, L. (2023). Artificial Intelligence in Language Education: A Bibliometric Analysis. Sustainable Multilingualism, 23, 159–194. https://doi.org/10.2478/sm-2023-0017
Kartal, G., & Yeşilyurt, Y. (2024). A bibliometric analysis of artificial intelligence in L2 teaching and applied linguistics between 1995 and 2022. ReCALL. https://doi.org/10.1017/s0958344024000077
Khiste, G. P., & Paithankar, R. R. (2017). Analysis of Bibliometric term in Scopus. International Research Journal, 01(32), 78–83.
Li, S. (2025). Generative AI and Second Language Writing. Digital Studies in Language and Literature, 2, 122–152. https://doi.org/10.1515/dsll-2025-0007
Liang, J., Hwang, G.-J., Chen, M.-R. A., & Darmawansah, D. (2021). Roles and research foci of artificial intelligence in language education: an integrated bibliographic analysis and systematic review approach. Interactive Learning Environments, 31, 4270–4296. https://doi.org/10.1080/10494820.2021.1958348
Lubis, A. H., Samsudin, D., Triarisanti, R., Jerusalem, M. I., & Hwang, Y. (2024). A bibliometric mapping analysis of publications on the utilization of artificial intelligence technology in language learning. Journal of Advanced Research in Applied Sciences and Engineering Technology. https://doi.org/10.37934/araset.38.1.156176
Mekheimer, M. (2025). Generative AI-assisted feedback and EFL writing: a study on proficiency, revision frequency and writing quality. Discover Education, 4. https://doi.org/10.1007/s44217-025-00602-7
Qadir, J. (2023). Engineering Education in the Era of ChatGPT: Promise and Pitfalls of Generative AI for Education. IEEE Global Engineering Education Conference, EDUCON, 2023-May. https://doi.org/10.1109/EDUCON54358.2023.10125121
Rahman, A., Raj, A., Tomy, P., & Hameed, M. (2024). A comprehensive bibliometric and content analysis of artificial intelligence in language learning: tracing between the years 2017 and 2023. Artificial Intelligence Review, 57. https://doi.org/10.1007/s10462-023-10643-9
Rasul, T., Nair, S., Kalendra, D., Robin, M., Santini, F. O., Ladeira, W. J., Sun, M., Day, I., Rather, R. A., & Heathcote, L. (2023). The role of ChatGPT in higher education: Benefits, challenges, and future research directions. Journal of Applied Learning and Teaching, 6(1), 41–56. https://doi.org/10.37074/jalt.2023.6.1.29
Steiss, J., Tate, T., Graham, S., Cruz, J., Hebert, M., Wang, J., Moon, Y., Tseng, W., Warschauer, M., & Olson, C. B. (2024). Comparing the quality of human and ChatGPT feedback of students’ writing. Learning and Instruction, 91. https://doi.org/10.1016/j.learninstruc.2024.101894
Su, J., & Yang, W. (2023). Unlocking the Power of ChatGPT: A Framework for Applying Generative AI in Education. ECNU Review of Education, 6(3), 355–366. https://doi.org/10.1177/20965311231168423
Tabari, M. A., Kushki, A., & Wang, Y. (2025). Comparing the effects of teacher- and AI-mediated corrective feedback on accuracy, complexity, and quality in L2 written narratives. Computer Assisted Language Learning. https://doi.org/10.1080/09588221.2025.2561608
Van Eck, N. J., & Waltman, L. (2007). Bibliometric mapping of the computational intelligence field. International Journal of Uncertainty, Fuzziness and Knowldege-Based Systems, 15(5), 625–645. https://doi.org/10.1142/S0218488507004911
Van Eck, N. J., & Waltman, L. (2010a). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. https://doi.org/10.1007/s11192-009-0146-3
Van Eck, N. J., & Waltman, L. (2010b). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. https://doi.org/10.1007/s11192-009-0146-3
Van Eck, N. J., & Waltman, L. (2017). Citation-based clustering of publications using CitNetExplorer and VOSviewer. Scientometrics, 111(2), 1053–1070. https://doi.org/10.1007/s11192-017-2300-7
Verbeek, A., Debackere, K., Luwel, M., & Zimmermann, E. (2002). Measuring progress and evolution in science and technology - I: The multiple uses of bibliometric indicators. International Journal of Management Reviews, 4(2), 179–211. https://doi.org/10.1111/1468-2370.00083
Wu, T., & Yu, Z. (2024). Bibliometric and Systematic Analysis of Artificial Intelligence Chatbots’ Use for Language Education. Journal of University Teaching and Learning Practice. https://doi.org/10.53761/6jmnst54
Wu, Y. C. J., & Wu, T. (2017). A decade of entrepreneurship education in the Asia Pacific for future directions in theory and practice. In Management Decision (Vol. 55, Number 7, pp. 1333–1350). https://doi.org/10.1108/MD-05-2017-0518
Yan, D., & Zhang, S. (2024). L2 writer engagement with automated written corrective feedback provided by ChatGPT: A mixed-method multiple case study. Humanities and Social Sciences Communications, 11. https://doi.org/10.1057/s41599-024-03543-y
Yan, L., Sha, L., Zhao, L., Li, Y., Martinez-Maldonado, R., Chen, G., Li, X., Jin, Y., & Gašević, D. (2024). Practical and ethical challenges of large language models in education: A systematic scoping review. British Journal of Educational Technology, 55(1), 90–112. https://doi.org/10.1111/bjet.13370
Yang, C., & Chen, H. H.-J. (2025). ChatGPT and L2 Chinese writing: evaluating the impact of model version and prompt language on automated corrective feedback. Computer Assisted Language Learning, 39, 1119–1147. https://doi.org/10.1080/09588221.2025.2453205
Yang, C., Chen, J., Hou, S., Wider, W., & Yi, W. L. (2025). Charting the developmental landscape of artificial intelligence in language education using bibliometric methods. Discover Artificial Intelligence, 5. https://doi.org/10.1007/s44163-025-00732-9
Yang, C.-W., Li, R., & Yang, L. (2025). Revisiting Trends in GenAI-Assisted Second Language Writing: Retrospect and Prospect. Journal of Educational Computing Research, 63, 1819–1863. https://doi.org/10.1177/07356331251367309
Zhang, X., & Umeanowai, K. O. (2024). Exploring the transformative influence of artificial intelligence in EFL context: A comprehensive bibliometric analysis. Education and Information Technologies, 30, 3183–3198. https://doi.org/10.1007/s10639-024-12937-z
Zhang, Z., Aubrey, S., Huang, X., & Chiu, T. (2025). The role of generative AI and hybrid feedback in improving L2 writing skills: a comparative study. Innovation in Language Learning and Teaching. https://doi.org/10.1080/17501229.2025.2503890
