ARTIFICIAL INTELLIGENCE TOOLS IN TRANSLATOR EDUCATION: OPPORTUNITIES AND CHALLENGES
DOI:
https://doi.org/10.66345/stj.6564Keywords:
artificial intelligence, translator education, machine translation, post-editing, translation competence, CAT tools, large language models, translation pedagogy.Abstract
This article investigates the integration of artificial intelligence tools into translator education, examining both the opportunities these technologies present and the pedagogical challenge. As neural machine translation systems, AI-assisted computer-aided translation platforms, and large language models become increasingly prevalent in professional translation practice. Drawing on a review of recent empirical studies and theoretical frameworks in translation pedagogy, this article analyzes the impact of AI tools on key aspects of translator education, including competence development, post-editing skill acquisition, and critical evaluation. The findings suggest that AI tools, when integrated thoughtfully and critically, can enhance the learning process by exposing trainees to authentic professional workflows
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References
1. K. Moorkens, S. Castilho, F. Gaspari, and S. Doherty, Translation Quality Assessment: From Principles to Practice. Cham: Springer, 2018.
2. C. Nord, Text Analysis in Translation: Theory, Methodology, and Didactic Application, 2nd ed. Amsterdam: Rodopi, 2005.
3. A. Pym, Exploring Translation Theories, 2nd ed. London: Routledge, 2014.
4. PACTE Group, “Building a translation competence model,” in Triangulating Translation, F. Alves, Ed. Amsterdam: John Benjamins, 2003, pp. 43–66.
5. European Master's in Translation, EMT Competence Framework 2022. Brussels: European Commission, 2022.
6. J. Thomas and A. Harden, “Methods for the thematic synthesis of qualitative research in systematic reviews,” BMC Medical Research Methodology, vol. 8, no. 1, p. 45, 2008. https://doi.org/10.1186/1471-2288-8-45
7. A. Way, “Quality expectations of machine translation,” in Translation Quality Assessment, J. Moorkens et al., Eds. Cham: Springer, 2018, pp. 159–178.
8. G. Massey and A. Ehrensberger-Dow, “Technical and workplace literacies,” The Interpreter and Translator Trainer, vol. 11, no. 4, pp. 350–362, 2017. https://doi.org/10.1080/1750399X.2017.1359847
9. R. Bizzoni, C. Bross, and M. Popovic, “Large language models and the future of translation,” Translation Spaces, vol. 12, no. 2, pp. 211–238, 2023. https://doi.org/10.1075/ts.22020.biz
10. M. Fomicheva and L. Specia, “Reference-free machine translation evaluation using LASER embeddings,” in Proc. EMNLP 2019, Hong Kong, pp. 5174–5179.
11. D. Ruzmatova, “Translation studies in post-Soviet Central Asia: Emerging frameworks and challenges,” Translation and Interpreting Studies, vol. 18, no. 1, pp. 45–67, 2023. https://doi.org/10.1075/tis.21008.ruz
12. TAUS, Machine Translation Post-Editing Guidelines. Amsterdam: TAUS, 2016. [Online]. Available: https://www.taus.net
13. M. Koponen, “Is machine translation post-editing worth the effort? A survey of research into post-editing and effort,” The Journal of Specialised Translation, no. 25, pp. 131–148, 2016.
14. C. G. Mellinger, “Translator competencies and the use of machine translation output,” in New Directions in Translation Studies, C. Way, Ed. New York: Routledge, 2020, pp. 89–107.
15. D. Kiraly, A Social Constructivist Approach to Translator Education. Manchester: St. Jerome, 2000.
16. S. O'Brien, “Machine translation and cognition,” in The Handbook of Translation and Cognition, J. W. Schwieter and A. Ferreira, Eds. Hoboken: Wiley-Blackwell, 2017, pp. 497–516.
17. F. Teixeira and D. O'Brien, “Eye tracking and the processing of machine-translated text,” Translation Spaces, vol. 6, no. 1, pp. 73–95, 2017. https://doi.org/10.1075/ts.6.1.04tei
18. M. Forcada, "Making sense of neural machine translation," Translation Spaces, vol. 6, no. 2, pp. 291–309, 2017. https://doi.org/10.1075/ts.6.2.06for
19. E. Pym and A. Torres-Simón, “Translator ethics and AI: New challenges for the profession,” Meta, vol. 68, no. 1, pp. 1–18, 2023. https://doi.org/10.7202/1099654ar
20. J. Drugan, Quality in Professional Translation. London: Bloomsbury, 2013.
