Shaping the future of translation careers: Student interest and the need for curriculum reform in the AI era
DOI:
https://doi.org/10.12928/eltej.v7i3.12016Keywords:
Translation career, Students' interest, Artificial intelligence, Machine translation, CurriculumAbstract
This study explores students’ interest in pursuing a career in translation in the rapid advancement of artificial intelligence and the growing need for translation curriculum reform. A mixed method was employed, involving 45 students from the English Literature Department at the University of Technology Yogyakarta (UTY) during the 2023-2024 academic year. Participants were selected using a stratified random sampling technique and included second-, third-, and fourth-year students. Data collection was conducted through questionnaires and semi-structured interviews. The questionnaire assessed students’ interest in translation careers, while the interview provided deeper insight, involving six respondents from the three batches, representing both positive and negative responses to the questionnaire items. The findings indicate that 26,6% of respondents are interested in a translation career, 55,6% are neutral, and 15,53% are not interested. Neutral responses were most common among second-year students and least common among fourth-year students, likely because higher-year students tend to focus more on career planning. The study also highlights the need to reform translation curricula by integrating machine translation into classroom instruction, as a computer assisted as well as automatic. The findings of this study suggest the need for further research on developing an AI-based model for teaching translation.
References
Albir, A. Galan-Manas, A. Kuznik, A. Neunzig, W. Olalla-Soler, C. Rodriguez-Ines, P. & Romero, L. (2019). Evolution of the efficacy of the translation process in translation competence acquisition. Meta Translators’ Journal. 64(1), 219-265. Montreal: University of Montreal Press. https://doi.org/10.7202/1065336ar
Bakhov, I. Bilous, N. Saiko, M. Isaienko, S. Hurinchuk, S. & Nozhovnik, O (2024). Beyond the dictionary: Redefining translation education with artificial intelligence-assisted app design and training. International Journal of Learning, Teaching and Educational Research. 23(4) 118-140 . Mauritius: Society for Research and Knowledge Management https://doi.org/10.26803/ijlter.23.4.7
Bindels, J. & Pluymaekers, M (2022). The use of machine translation by undergraduate translation students for different learning tasks. Journal for Data Mining and Digital Humanities France: Nicolas Turenne https://doi.org/10.46298/jdmdh,9019
Byrne, D. (2022). A worked example of Braun and Clarke’s approach to reflexive thematic analysis. Quality & quantity, 56(3), 1391-1412. New York: Springer
https://doi.org/10.1007/s11135-021-01182-y
Çakir, I. & Bahyan, S (2021). The effect of machine translation on translation classes at the tertiary level. Journal of Narrative and Language Studies 9(16) 122-134. Turkey: Karadenis Technical University
Caukin, S. Trail, L. Vinson, L. & Wright, C (2024). Tech talk entering a new frontier: AI in education. International Journal of the Whole Child. 8(2) 47-55. New Jersey: Wiley-Blackwell https://www.researchgate.net/publication/377556990
Chen, M. (2023). Trust, understanding, and machine translation: the task of translation and the responsibility of the translator. AI & SOCIETY. Vol. 39 pp 2307-2319. New York: Springer Science+Business Media. https://doi.org/10.1007/s00147-023-01681-6
Cunha, S. (2023). MT and legal translation: application in training. Proceedings of Machine Translation Summit XIX, Vol. 2: Users Track, 11–23, Macau SAR, China. Asia-Pacific Association for Machine Translation. https://aclanthology.org/2023.mtsummit-users.2
Datta, G. Joshi, & N, Gupta, K (2023). Performance comparison of statistical vs. neural-based translation system on low-resource language. International Journal on Smart Sensing and Intelligent System 1(16). 1–13. New Zealand: Sciendo. https://doi.org/10.2478/ijssis-2023-0007
Cuenca, E. Estrella, P. Bruno, L. Mutal, J. Girletti, S. Volkart, L & Bouillon. P. (2020). Re-design of machine translation training tool (MT3). Proceedings of the 22nd Annual Conference of the European Association for Machine Translation, (pp. 375–382), Lisboa: European Association for Machine Translation. https://aclanthology.org/2020.eamt-1.40
Field, A. (2024). Discovering Statistics Using IBM SPSS Statistics (6th ed.). London: SAGE Publications.
García-Escribano, A. & Díaz-Cintas, J (2023). Integrating post-editing into the subtitling classroom: what do subtitlers-to-be think? Linguistica Antverpiensia, New Series: Themes in Translation Studies, 22. pp. 115-137. Belgium: University of Antwerp
George, D., & Mallery, P. (2021). IBM SPSS Statistics 27 step by step: A simple guide and reference (17th ed.). New York: Routledge.
Hao, Y. & Pym, A. (2022). Where do translation students go? A study of the employment and mobility of Master graduates. The interpreter and translator trainer. 17(9). 1-19. United Kingdom: Taylor & Francis https://doi.org/10.1080/1750399X.2022.2084595
Hellmich, E. & Vinall, K. (2023). Student use and instructor beliefs: Machine translation in language education. Language Learning & Technology, 27(1), 1–27. Manoa: ScholarSpace
https://hdl.handle.net/10125/73525
Herbig, N. Pal, S. Van Genabith, J & Krṻger, A (2019). Integrating artificial and human intelligence for efficient translation. arxiv.org/abs/1903.02978v1. New York: Cornell University https://doi.org/10.48550/arXiv.1903.02978
Hiebl, B. & Gromann, D, (2023). Quality in human and machine translation: Interdisciplinary survei. Proceeding of the 24th Annual Conferencesof the European Association for Machine Translation, pages 375–384, Tampere, Finland. European Association for Machine Translation. https://aclanthology.org/2023.eamt-1.37.pdf
Husein, D. Bahar (2020). English major students’ self-concept perspective on viewing translator as a profession. New Language Dimension I(2) 49-54. Surabaya: Universitas Negeri Surabaya.
Kanglang, L. & Fazaal, M (2021). Artificial intelligence and translation teaching: A critical perspective on the transformation of education. International Journal of Education Science 33(1-3), 64-73. United Kingdom: Taylor & Francis.
Kirov, V. & Malamin, B.(2022). Are translators afraid of artificial intelligence? Societies 12(2) 1-14. Switzerland: MDPI. https://doi.org/10.3390/soc12020070
Lange, A. Monticelli, D. & Rundle, C. (Eds.). (2024). The Routledge Handbook of the History of Translation Studies. London & New York: Routledge. http://dx.doi.org/10.4324/9781032690056
Lee, S. (2023). The effectiveness of machine translation in foreign language education: A systematic review and meta-analysis. Computer Assisted Language Learning, 36(1-2), 103-125. United Kingdom: Taylor & Francis. https://doi.org/10.1080/09588221.2021.1901745
Loock, R. & Léchauguette, S (2021). Machine translation literacy and undergraduate students in applied languages report on an exploratory study. Revista Tradumάtica No.19 pp. 205-225. Bacelona: Universitat Autònoma de Barcelona. https://doi.org/10.5565/rev/tradumatica.281
Miyata, R & Fujita, A. (2021). Understanding pre-editing for black-box neural machine. Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics. Main Volume, (pp. 1539–1550), Online. Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.eacl-main.132
Mohamed, Y. Khanan, A. Bashir, M. Mohamed, A. Adiel, M. & Elsadig, M. (2024). The impact of artificial intelligence on language translation: A review. IEEE Acces Journals Vol 12 pp: 25553 – 25579. New York: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ACCESS.2024.3366802
O’Keeffe, B. (2023). The translation of stone. American Book Review. 44(4). 105-110. Nebraska: University of Nebraska Press. https://doi.org/10.1353/abr.2023.a921791
Oner Bulut, S (2020). Integrating machine translation into translation training; Toward ‘Human Translator Competence’, transLogos, 2(2). 1-26. Turkey: Diye Global Communications http://dx.doi.org/10.29228/transLogos.11
Pastor, D. (2021). Introducing machine translation in the translation classroom: a survey on students’ attitude and perception. Revista Tradumάtica No.19 pp. 48-65. Bacelona: Universitat Autònoma de Barcelona. http://dx.doi.org/10.5565/rev/tradumatica.273
Rokan, K (2021). Rennaisance and the development of translation in the Arab world. Journal of Humanities and Education Development. 3(4) 10-13.Jaipur: Shillonga Publications Group http://dx.doi.org/10.22161/jhed.3.4.2
Shin, D. & Chon, Y. (2023). Second language learner’s post-editing strategies for machine translation errors. Language Learning and Technology. 27(1) 1–25. Manoa: National Foreign Language Resource Center. https://hdl.handle.net/10125/73523
Steigerwald, E. Ramires-Castaneda, V. Brandt, D. Baldi, A. Saphiro, J. Bowker, L & Tarvin, R. (2022). Overcoming language barrier in academia: Machine translation tools and a vision for a multilingual future. BioScience Journal, 72(10) 989-998. Oxford: Oxford University Press. https://doi.org/10.1093/biosci/biac062
Tian, S. Jia, L. & Zhang, Z. (2023). Investigating students’ attitudes towards translation technology: The status quo and structural relations with translation mindsets and future work self. Frontiers in Psychology. pp 1–16. Switzerland: Frontiers Media SA https://doi.org/10.3389/fpsyg.2023.1122612
White, J. Fu, Q. Hays, A, Sandborn, M. Olea, C. Gilbert, H. Elnashar, A. Spencer-Smith, J & Schmidt, D.C. (2023). A prompt pattern catalog to enhance prompt engineering with ChatGPT. cs>arXiv:2302.11382. New York: Cornell University
https://doi.org/10.48550/arXiv.2302.11382
Yamada, M (2019). The impact of Google neural machine translation on post-editing by student translators. The Journal of Specialized Translation. Issue 32. 87–105. Switzerland; ZHAW School of Applied Linguistics.
Yuxiu, Y. (2024). Application of translation technology based on AI in translation teaching. System and Soft Computing. Vol. 6 pp.1–8. Netherland: Elsevier. https://doi.org/10.1016/j.sasc.2024.200072
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Eko Setyo Humaniika, Yohanes Radjaban
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish in ELTEJ agree to the following terms: Authors retain copyright and grant the ELTEJ right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY-SA 4.0) that allows others to share (copy and redistribute the material in any medium or format) and adapt (remix, transform, and build upon the material) the work for any purpose, even commercially with an acknowledgement of the work's authorship and initial publication in ELTEJ. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in ELTEJ. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
