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Abstract

The rapid development of generative artificial intelligence (GenAI) has introduced new opportunities for interpreter education, particularly in creating interactive, adaptive and personalized learning environments. This study investigated how undergraduate students taking a Chinese-to-English consecutive interpreting course (with English as their B language) engaged strategically with a customized GenAI assistant over eight weeks. Drawing on more than 600 recorded prompts and iterative class reflections collected across four exercises, we examined the way the students progressed from passive content requests to metacognitively informed dialogic interactions with GenAI. Guided by a dual framework aimed at integrating interpreter competence and metacognitive development models, the course embedded GenAI use within pedagogical scaffolding, including peer collaboration and instructor-led discussions. The students showed a clear progression in their prompting behavior, increasingly demonstrating strategic planning, critical feedback evaluation and reflective learning. Once the students had the skills to engage in meaningful goal-oriented prompting, they used GenAI not merely as a feedback tool, but also as a dialogic learning partner. These findings underscore GenAI’s potential not just as a technological aid, but also as a catalyst for developing autonomy, critical thinking and metacognitive awareness. This study highlights the importance of aligning GenAI integration with intentional pedagogy to support deep human-centered learning in interpreter training.

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2026-05-05
2026-05-11
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References

  1. Albl-Mikasa, M.
    (2012) The importance of being not too earnest: A process- and experience-based model of interpreter competence. InB. Ahrens, M. Albl-Mikasa & C. Sasse (Eds.), Dolmetschqualität in Praxis, Lehre und Forschung. Festschrift für Sylvia Kalina. Tübingen: Narr Francke Attempto, –.
    [Google Scholar]
  2. Bastani, H., Bastani, O., Sungu, A., Ge, H., Kabakcı, O. & Mariman, R.
    (2024) Generative AI can harm learning. The Wharton School Research Paper, Available atSSRN: https://ssrn.com/abstract=4895486or10.2139/ssrn.4895486
    https://doi.org/10.2139/ssrn.4895486 [Google Scholar]
  3. Bauer, E., Greiff, S., Graesser, C. A., Scheiter, K. & Sailer, M.
    (2025) Looking beyond the hype: Understanding the effects of AI on learning. Educational Psychology Review 37 (). 10.1007/s10648‑025‑10020‑8
    https://doi.org/10.1007/s10648-025-10020-8 [Google Scholar]
  4. Chang, C-C.
    (2018) English language needs of Chinese/English interpreting students: An error analysis of the Chinese-to-English Short Consecutive Interpreting Test. English Teaching and Learning (), –. 10.1007/s42321‑018‑0011‑7
    https://doi.org/10.1007/s42321-018-0011-7 [Google Scholar]
  5. Chang, C-C. & Wu, M-C.
    (2025) Structured peer feedback in simultaneous interpreting training. INContext: Studies in Translation and Interculturalism (), –. 10.54754/incontext.v5i2.124
    https://doi.org/10.54754/incontext.v5i2.124 [Google Scholar]
  6. Chapelle, A. C.
    (2025) Generative AI as game changer: Implications for language education. System, , . 10.1016/j.system.2025.103672
    https://doi.org/10.1016/j.system.2025.103672 [Google Scholar]
  7. Chen, J., Huang, K., Lai, C. & Jin, T.
    (2025) The impact of GenAI-based collaborative inquiry on critical thinking in argumentation: A case study of blended argumentative writing pedagogy. TESOL Quarterly, –. 10.1002/tesq.3407
    https://doi.org/10.1002/tesq.3407 [Google Scholar]
  8. Chen, P.
    (2024) The impact of generative AI on the role of translators and its implications for translation education. Education Insights, –. 10.70088/kc9vk395
    https://doi.org/10.70088/kc9vk395 [Google Scholar]
  9. Chen, S. & Kruger, J.-L.
    (2023) The effectiveness of computer-assisted interpreting: A preliminary study based on English–Chinese consecutive interpreting. Translation and Interpreting Studies (), –. 10.1075/tis.21036.che
    https://doi.org/10.1075/tis.21036.che [Google Scholar]
  10. (2024) A computer-assisted consecutive interpreting workflow: Training and evaluation. The Interpreter and Translator Trainer (), –. 10.1080/1750399X.2024.2373553
    https://doi.org/10.1080/1750399X.2024.2373553 [Google Scholar]
  11. Chi, H. T. M. & Wylie, R.
    (2014) The ICAP Framework: Linking cognitive engagement to active learning outcomes. Educational Psychologist (), –. 10.1080/00461520.2014.965823
    https://doi.org/10.1080/00461520.2014.965823 [Google Scholar]
  12. Corpas Pastor, G. & Defrancq, B.
    (Eds.) (2023) Interpreting technologies: Current and future trends. Amsterdam: John Benjamins. 10.1075/ivitra.37
    https://doi.org/10.1075/ivitra.37 [Google Scholar]
  13. Crompton, H., Edmett, A. & Ichaporia, N.
    (2023) Artificial intelligence and English language teaching: A systematic literature review. London: British Council.
    [Google Scholar]
  14. Earl, L. M.
    (2013) Assessment as learning: Using classroom assessment to maximize student learning. Thousand Oaks, CA: Corwin Press.
    [Google Scholar]
  15. Edmett, A., Ichaporia, N., Crompton, H. & Crichton, R.
    (2024) Artificial intelligence and English language teaching: Preparing for the future. London: British Council. 10.57884/78EA‑3C69
    https://doi.org/10.57884/78EA-3C69 [Google Scholar]
  16. Ehrensberger-Dow, M., Benites, D. A. & Lehr, C.
    (2023) A new role for translators and trainers: MT literacy consultants. The Interpreter and Translator Trainer (), –. 10.1080/1750399X.2023.2237328
    https://doi.org/10.1080/1750399X.2023.2237328 [Google Scholar]
  17. 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 (), –. 10.1111/bjet.13544
    https://doi.org/10.1111/bjet.13544 [Google Scholar]
  18. Gile, D.
    (2009) Basic concepts and models for interpreter and translator training (Rev. ed.). Amsterdam: John Benjamins. 10.1075/btl.8
    https://doi.org/10.1075/btl.8 [Google Scholar]
  19. Goh, C. C. & Vandergrift, L.
    (2021) Teaching and learning second language listening: Metacognition in action. London: Routledge. 10.4324/9780429287749
    https://doi.org/10.4324/9780429287749 [Google Scholar]
  20. Guan, L., Zhang, Y. E. & Gu, M. M.
    (2025) Examining generative AI–mediated informal digital learning of English practices with social cognitive theory: A mixed-methods study. ReCALL (), –. 10.1017/S0958344024000259
    https://doi.org/10.1017/S0958344024000259 [Google Scholar]
  21. Han, C. & Fan, Q.
    (2019) Using self-assessment as a formative assessment tool in an English–Chinese interpreting course: Student views and perceptions of its utility. Perspectives (), –. 10.1080/0907676X.2019.1615516
    https://doi.org/10.1080/0907676X.2019.1615516 [Google Scholar]
  22. Jiang, R., Yang, G. & Shen, Q.
    (2025) The motivational impact of GenAI tools in language learning: A quasi-experiment study. International Journal of Applied Linguistics (), –. 10.1111/ijal.12701
    https://doi.org/10.1111/ijal.12701 [Google Scholar]
  23. Kim, J., Yu, S., Lee, S-S. & Detrick, R.
    (2025) Students’ prompt patterns and its effects in AI-assisted academic writing: Focusing on students’ level of AI literacy. Journal of Research on Technology in Education, –. 10.1080/15391523.2025.2456043
    https://doi.org/10.1080/15391523.2025.2456043 [Google Scholar]
  24. Kornacki, M. & Pietrzak, P.
    (2024) Hybrid workflows in translation: Integrating genAI into translator training. London: Routledge.
    [Google Scholar]
  25. Krüger, R.
    (2024) Outline of an artificial intelligence literacy framework for translation, interpreting and specialised communication. Lublin Studies in Modern Languages and Literature (), –. 10.17951/lsmll.2024.48.3.11‑23
    https://doi.org/10.17951/lsmll.2024.48.3.11-23 [Google Scholar]
  26. Lee, J.
    (2018) Feedback on feedback: Guiding student interpreter performance. Translation and Interpreting: The International Journal of Translation and Interpreting Research (), –. 10.12807/ti.110201.2018.a09
    https://doi.org/10.12807/ti.110201.2018.a09 [Google Scholar]
  27. Liu, C., Hou, J., Tu, Y.-F., Wang, Y. & Hwang, G.-J.
    (2021) Incorporating a reflective thinking promoting mechanism into artificial intelligence-supported English writing environments. Interactive Learning Environments (), –. 10.1080/10494820.2021.2012812
    https://doi.org/10.1080/10494820.2021.2012812 [Google Scholar]
  28. Lu, X.
    (2025) The effectiveness of ChatGPT-assisted lexical-syntactic flexibility practice for interpreting competence and quality: The case of Chinese-to-English consecutive interpreting. The Interpreter and Translator Trainer (), –. 10.1080/1750399X.2025.2533014
    https://doi.org/10.1080/1750399X.2025.2533014 [Google Scholar]
  29. Massey, G., Piotrowska, M. & Marczak, M.
    (2023) Meeting evolution with innovation: An introduction to (re-)profiling T&I education. The Interpreter and Translator Trainer (), –. 10.1080/1750399X.2023.2237321
    https://doi.org/10.1080/1750399X.2023.2237321 [Google Scholar]
  30. Miao, F. & Holmes, W.
    (2023) Guidance for generative AI in education and research. Paris: UNESCO Publishing. 10.54675/EWZM9535
    https://doi.org/10.54675/EWZM9535 [Google Scholar]
  31. Mohebbi, A.
    (2025) Enabling learner independence and self-regulation in language education using AI tools: A systematic review. Cogent Education (). 10.1080/2331186X.2024.2433814
    https://doi.org/10.1080/2331186X.2024.2433814 [Google Scholar]
  32. Olalla-Soler, C.
    (2024) A bibliometric investigation on didactic proposals in interpreter training (2001–2020). The Interpreter and Translator Trainer (), –. 10.1080/1750399X.2024.2333707
    https://doi.org/10.1080/1750399X.2024.2333707 [Google Scholar]
  33. Pöchhacker, F. & Liu, M.
    (2024) Interpreting technologized. Interpreting (), –. 10.1075/intp.00112.poc
    https://doi.org/10.1075/intp.00112.poc [Google Scholar]
  34. Setton, R. & Dawrant, A.
    (2016) Conference interpreting: A complete course. Amsterdam: John Benjamins. 10.1075/btl.120
    https://doi.org/10.1075/btl.120 [Google Scholar]
  35. Teng, F. M.
    (2025) Understanding EFL student writers’ metacognitive awareness in utilizing ChatGPT. System. 10.1016/j.system.2025.103848
    https://doi.org/10.1016/j.system.2025.103848 [Google Scholar]
  36. Tian, S. & Yang, W.
    (2024) Modeling the use behavior of interpreting technology for student interpreters: An extension of UTAUT model. Education and Information Technologies (), –. 10.1007/s10639‑023‑12225‑2
    https://doi.org/10.1007/s10639-023-12225-2 [Google Scholar]
  37. Van Horn, K.
    (2024) ChatGPT in English language learning: Exploring perceptions and promoting autonomy in a University EFL context. Teaching English as a Second or Foreign Language — TESL-EJ (). 10.55593/ej.28109a8
    https://doi.org/10.55593/ej.28109a8 [Google Scholar]
  38. Wang, X., Wang, B. & Yuan, L.
    (2025) The function of ASR-generated live transcription in simultaneous interpreting: Trainee interpreters’ perceptions from post-task interviews. Humanities and Social Sciences Communications (). 10.1057/s41599‑025‑04492‑w
    https://doi.org/10.1057/s41599-025-04492-w [Google Scholar]
  39. Xu, X., Qiao, L., Cheng, N., Liu, H. & Zhao, W.
    (2025) Enhancing self-regulated learning and learning experience in generative AI environments: The critical role of metacognitive support. British Journal of Educational Technology (), –. 10.1111/bjet.13599
    https://doi.org/10.1111/bjet.13599 [Google Scholar]
  40. Yamada, M.
    (2025) ChatGPT eigo gakushujutsu: AI jidai no cho-dokugaku skill book [ChatGPT English learning strategy: Skill book for autonomous learning in the age of AI]. Tokyo: ALC.
    [Google Scholar]
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