The Inspiration of Translation Technology Education in The Era of Artificial Intelligence to the Cultivation of Translators' Ability
DOI:
https://doi.org/10.54097/kkd8dt84Keywords:
Artificial Intelligence, Translation Technology Education, Translator Ability, Talent CultivationAbstract
Artificial intelligence technology continues to integrate into the translation industry. Machine translation, generative artificial intelligence and computer-aided translation tools continue to change the traditional translation production mode, and also put forward new requirements for the knowledge structure, professional role and professional ability of translators. Traditional translation education attaches more importance to language knowledge and translation skills. The cultivation of technical awareness, human-computer collaboration, translation quality assessment and other abilities is relatively insufficient. There is a certain adaptation problem between talent cultivation and the needs of the intelligent translation industry. Based on this, the study analyses the actual requirements of translation technology education in the era of artificial intelligence and the cultivation of translators' ability, and puts forward a specific cultivation path. Meanwhile, by strengthening the construction of teachers, improving the intelligent teaching platform, and establishing a school-enterprise coordination mechanism, we will provide a guarantee for the reform of translation technology education. The author takes the view that translation education in the era of artificial intelligence should adhere to the subjectivity of translators, organically combine language ability, technical literacy and comprehensive judgement ability, promote students to form technical application, translation evaluation and human-computer collaboration ability, and cultivate composite talents who can adapt to the intelligent translation environment.
Downloads
References
[1] Doherty, S., & Kenny, D. (2014). The design and evaluation of a statistical machine translation syllabus for translation students. The Interpreter and Translator Trainer, 8(2), 295–315. https://doi.org/10.1080/1750399X.2014.937571. DOI: https://doi.org/10.1080/1750399X.2014.937571
[2] Gaspari, F., Almaghout, H., & Doherty, S. (2015). A survey of machine translation competences: Insights for translation technology educators and practitioners. Perspectives, 23(3), 333–358. https://doi.org/10.1080/0907676X.2014.979842. DOI: https://doi.org/10.1080/0907676X.2014.979842
[3] Kenny, D., & Doherty, S. (2014). Statistical machine translation in the translation curriculum: Overcoming obstacles and empowering translators. The Interpreter and Translator Trainer, 8(2), 276–294.
https://doi.org/10.1080/1750399X.2014.936112 DOI: https://doi.org/10.1080/1750399X.2014.936112
[4] Krüger, R., & Hackenbuchner, J. (2024). A competence matrix for machine translation oriented data literacy teaching. Target, 36(2), 245–275. https://doi.org/10.1075/target.22127.kru. DOI: https://doi.org/10.1075/target.22127.kru
[5] Li, X., Gao, Z., & Liao, H. (2024). An empirical investigation of college students’ acceptance of translation technologies. PLOS ONE, 19(2), e0297297.
https://doi.org/10.1371/journal.pone.0297297 DOI: https://doi.org/10.1371/journal.pone.0297297
[6] Man, D., Mo, A., Chau, M. H., O’Toole, J. M., & Lee, C. (2020). Translation technology adoption: Evidence from a postgraduate programme for student translators in China. Perspectives, 28(2), 253–270.
https://doi.org/10.1080/0907676X.2019.1677730 DOI: https://doi.org/10.1080/0907676X.2019.1677730
[7] Mellinger, C. D. (2017). Translators and machine translation: Knowledge and skills gaps in translator pedagogy. The Interpreter and Translator Trainer, 11(4), 280–293.
https://doi.org/10.1080/1750399X.2017.1359760 DOI: https://doi.org/10.1080/1750399X.2017.1359760
[8] Pym, A. (2013). Translation skill sets in a machine translation age. Meta, 58(3), 487–503. https://doi.org/10.7202/1025047ar. DOI: https://doi.org/10.7202/1025047ar
[9] Venkatesan, H. (2023). Technology preparedness and translator training: Implications for curricula. Babel, 69(5), 666–703. https://doi.org/10.1075/babel.00335.ven. DOI: https://doi.org/10.1075/babel.00335.ven
[10] Zhang, J., & Doherty, S. (2025). Investigating novice translation students’ AI literacy in translation education. The Interpreter and Translator Trainer, 19(3 4), 234–253. https://doi.org/10.1080/1750399X.2025.2541478. DOI: https://doi.org/10.1080/1750399X.2025.2541478
[11] Zhang, W., Li, A. W., & Wu, C. (2025). University students’ perceptions of using generative AI in translation practices. Instructional Science, 53(4), 633–655. DOI: https://doi.org/10.1007/s11251-025-09705-y
https://doi.org/10.1007/s11251 025 09705 y
[12] Prapunta, S. (2025). “We don’t just translate texts”: Students’ perceptions towards the effectiveness of project based learning in a translation course. MEXTESOL Journal, 49(1), 1–15. DOI: https://doi.org/10.61871/mj.v49n1-10
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Journal of Education and Educational Research

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.









