GigaChat rhetorical potential for transforming metadiscoursive patterns in Russian academic writing

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Abstract

The study is relevant due to the need to improve students’ academic writing skills; the relevance is also substantiated by the ongoing transformation of scientific communication under the influence of artificial intelligence (AI). The study presents the results of a comparative analysis of metadiscoursive components in Russian-language abstracts written by undergraduates of engineering faculties before and after AI editing. The research focuses on the effectiveness of GigaChat neural network model in editing scientific texts in terms of the specific scientific and technical communication. The author used methods of quantitative and interpretative analysis. The research material consisted of 40 Russian-language abstracts written by the 2nd year undergraduates in engineering. The study revealed the frequency dynamics of metadiscoursive markers before and after AI text processing. The results showed that the number of boosters, attitude markers, and self-reference markers in the texts created with the developed prompts and edited by GigaChat reached the reference level, i.e. the normalized frequency of the metadiscoursive tools in the abstracts written by the leading researchers in technical sciences. The increase in hedging markers above the reference level softened the categorical statements. Despite GigaChat failed to achieve the reference frequency for all metadiscoursive markers after AI text editing the academic style significantly improved and came closer to the metadiscoursive canons of scientific and technical communication. The research studies transformations in scientific communication under the influence of AI and reveals the potential for optimizing the written scientific speech of novice researchers.

About the authors

Olga A. Boginskaya

Irkutsk National Research Technical University

Author for correspondence.
Email: olgaa_boginskaya@mail.ru
ORCID iD: 0000-0002-9738-8122
SPIN-code: 1370-7025

Doctor of Philology, Professor at the Department of Foreign Languages

83 Lermontov St., Irkutsk, 664074, Russian Federation

References

  1. Becher, T., & Trowler, P. (2001). Academic tribes and territories. Buckingham: SRHE and Open University Press.
  2. Boginskaya, O. A. (2022). Cross-disciplinary variation in metadiscourse: A corpus-based analysis of Russian-authored research article abstracts. Training, Language and Culture, 6(3), 55–66. https://doi.org/10.22363/2521-442X-2022-6-3-55-66 EDN: PWPCDQ
  3. Boginskaya, O. A. (2023). Russian lexical and syntactic hedges in dissertation reviews. Russian Language Studies, 21(1), 18–32. (In Russ.). https://doi.org/10.22363/2618-8163-2023-21-1-18-32. EDN: YZIACM
  4. Boginskaya, O. A. (2024). “It is evident that …”: Boosting as a strategy employed to express author’s commitment to the truth of a proposition (a corpus-based analysis of research article abstracts). Speech Genres, 19(1), 56–65. (In Russ.). https://doi.org/10.18500/2311-0740-2024-19-1-41-56-65 EDN: FHAZFS
  5. Dontcheva-Navratilova, O. (2021). Engaging with the reader in research articles in English: Variation across disciplines and linguacultural backgrounds. English for Specific Purposes, 63, 18–32. https://doi.org/10.1016/j.esp.2021.02.003 EDN: AYEKNK
  6. Hyland, K. (2005). Metadiscourse: exploring interaction in writing. UK, London: A&C Black.
  7. Ivakhnenko, E. N., & Nikolskiy, V. S. (2023). ChatGPT in higher education and science: A threat or a valuable resource? Vysshee obrazovanie v Rossii = Higher Education in Russia, 32(4), 9–22. (In Russ.). https://doi.org/10.31992/0869-3617-2023-32-4-9-22 EDN: TZHIHU
  8. Ivanova, L. A. (2024). Artificial intelligence in writing scientific articles — a positive or a harmful factor? Crede Experto: Transport, Society, Education, Language, (4). (In Russ.). https://doi.org/10.51955/2312-1327_2024_4_6 EDN: YRKWJQ
  9. Jiang, F., & Hyland, K. (2024). Does ChatGPT write like a student? Engagement markers in argumentative essays. Written Communication, 42(3), 463–492. https://doi.org/10.1177/07410883251328311 EDN: XTUWMT
  10. Khedri, M., Heng, C. S., & Ebrahimi, S. F. (2013). An exploration of interactive metadiscourse markers in academic research article abstracts in two disciplines. Discourse Studies, 15(3), 319–331. https://doi.org/10.1177/1461445613480588
  11. Korotkina, I. B. (2018). Academic writing in Russia: The urge for interdisciplinary studies. Vysshee obrazovanie v Rossii = Higher Education in Russia, 27(10), 64–74. (In Russ.). https://doi.org/10.31992/0869-3617-2018-27-10-64-74 EDN: YLVIOT
  12. Mo, Z., & Crosthwaite, P. (2025). Exploring the affordances of generative AI large language models for stance and engagement in academic writing. Journal of English for Academic Purposes, 75, Article 101499. https://doi.org/10.1016/j.jeap.2025.101499 EDN: HZABZD
  13. Ou, B. K., Khuder, B., Franzetti, S., & Negretti, R. (2024). Conceptualising and cultivating critical GAI literacy in doctoral academic writing. Journal of Second Language Writing, 66, Article 101156. https://doi.org/10.1016/j.jslw.2024.101156 EDN: QWTNNW
  14. Pecorari, D. (2023). Generative AI: same but different? Journal of Second Language Writing, 62, Article 101067. https://doi.org/10.1016/J.JSLW.2023.101067
  15. Rymanova, I. E. (2023). The role of artificial intelligence in developing academic writing skills. Language. Society. Education: collection of scientific works of the 4th International Scientific Conference “Linguistic and Cultural Aspects of Modern Engineering Education” (pp. 183–188). Tomsk: Tomsk Polytechnic University. EDN: BZSNRB
  16. Tikhonova, E. V., Kosycheva, M. A., & Golechkova, T. Yu. (2023). Establishing rapport with the reader: Engagement markers in the discussion section of a research article. Integration of Education, 27(3), 354–372. https://doi.org/10.15507/1991-9468.112.027.202303.354-372 EDN: JXLYEP
  17. Utkina, T. I., & Kostareva, E. V. (2021). Features of metadiscourse use (sphere of economics, students’ written texts in English). Tomsk State University Journal of Philology, (74), 188–207. (In Russ.). https://doi.org/10.17223/19986645/74/11 EDN: XRSBSF
  18. Warschauer, M., Tseng, W., Yim, S., Webster, T., Jacob, S., Du, Q., & Tate, T. (2023). The affordances and contradictions of AI-generated text for writers of English as a second or foreign language. Journal of Second Language Writing, 62, Article 101071. https://doi.org/10.1016/j.jslw.2023.101071. EDN: WENSXW
  19. Xu, C. (2025). Intradisciplinary heterogeneity of academic writing: A diachronic probe into interactional metadiscourse in medical research articles. Journal of English for Academic Purposes, 76, Article 101541. https://doi.org/10.1016/j.jeap.2025.101541. EDN: EKWXQV
  20. Zashikhina, I. M. (2021). Academic writing: A discipline or disciplines? Vysshee obrazovanie v Rossii = Higher Education in Russia, 30(2), 134–143. (In Russ.). https://doi.org/10.31992/0869-3617-2021-30-2-134-143 EDN: YVQHMI
  21. Zhang, M., & Zhang, J. (2025). Disciplinary variation of metadiscourse: A comparison of human-written and ChatGPT-generated English research article abstracts. Journal of English for Academic Purposes, 76, Article 101540. https://doi.org/10.1016/j.jeap.2025.101540 EDN: CRAVOB

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