Beyond the Machine: Rethinking AI Tools in Translator Training for Iraqi Students
DOI:
https://doi.org/10.25130/jfa.conf.10.3.22Keywords:
Artificial Intelligence, Translator Training, Iraq, Critical Thinking, Human-Machine BalancingAbstract
Given the limited and non-systematic use of Artificial Intelligence (AI) in translation education in Iraq, this research examines how such modern technologies may be useful and supportive tools within translation learning programs. This study is convinced that despite the partial effectiveness of these tools, they cannot replace the analytical and mental capacity of human translators. Throughout my academic experience in teaching translation, I have noticed that translation students sometimes depend on tools like ChatGPT with no realization of what should be limited. This is a serious problem that affects their skills and development. It also seeks to propose a deeper insight into merging artificial intelligence with translator training programs, on the condition that this is done under academic terms and guidelines that maintain the fundamental notion of translation as a perceptive and cultural act. The study gives attention to the analysis of AI tools such as ChatGPT, Google Translate and DeepL to estimate their benefits and drawbacks in promoting translation learning. Finally, it confirms that the use of AI should remain a support only, not a replacement for human conscious judgment and decision.
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References
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Bowker, L., & Buitrago Ciro, J. (2019). Machine translation and global research: Towards improved machine translation literacy in the scholarly community. Emerald Publishing.
Cambridge University Press. (2022). Artificial intelligence in language teaching and translation research. Cambridge University Press. https://www.cambridge.org
Castilho, S., Moorkens, J., Gaspari, F., Calixto, I., Tinsley, J., & Way, A. (2018). Evaluating MT for human translators in literary, technical, and academic settings. Machine Translation, 32(4), 1–20. https://doi.org/10.1007/s10590-018-9224-8
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Garcia, I. (2017). Translators and machine translation: Shifting paradigms in translator education. Translation & Interpreting, 9(2), 55–70.
Johnson, M., Schuster, M., Le, Q. V., Krikun, M., Wu, Y., Chen, Z., Dean, J. (2017). Google’s multilingual neural machine translation system: Enabling zero-shot translation. Transactions of the Association for Computational Linguistics, 5, 339–351. https://doi.org/10.1162/tacl_a_00065
Kenny, D. (2022). Translator training in the age of artificial intelligence. The Translator, 28(2), 123–139. https://doi.org/10.1080/13556509.2022.2048765
Koehn, P. (2020). Neural machine translation Cambridge University Press.
Massachusetts Institute of Technology (MIT). (2021). MIT launches research initiatives on AI in higher education. MIT News. https://news.mit.edu
Pym, A. (2021). Translation solutions for many languages: Histories of a flawed dream. Bloomsbury Academic.
Russell, S., & Norvig, P. (2020). Artificial intelligence: A modern approach (4th ed.). Pearson.
Toral, A. (2020). Post-editese: An exacerbated translationese. Machine Translation, 34(1), 41–67. https://doi.org/10.1007/s10590-019-09236-9
Toral, A., & Way, A. (2018). What level of quality can neural machine translation attain on literary text? In J. Moorkens, S. Castilho, F. Gaspari, & S. Doherty (Eds.), Translation quality assessment: From principles to practice (pp. 263–287). Springer. References
Bowker, L. (2020). Machine translation literacy: Helping students to make informed decisions about MT use in foreign language learning. Language Learning & Technology, 24(1), 1–15.
Bowker, L., & Buitrago Ciro, J. (2019). Machine translation and global research: Towards improved machine translation literacy in the scholarly community. Emerald Publishing.
Cambridge University Press. (2022). Artificial intelligence in language teaching and translation research. Cambridge University Press. https://www.cambridge.org
Castilho, S., Moorkens, J., Gaspari, F., Calixto, I., Tinsley, J., & Way, A. (2018). Evaluating MT for human translators in literary, technical, and academic settings. Machine Translation, 32(4), 1–20. https://doi.org/10.1007/s10590-018-9224-8
European Language Council. (2022). Artificial intelligence and language education. https://www.celelc.org
European Language Grid. (2020). ELG at a glance: Language technologies for Europe. https://www.european-language-grid.eu
Garcia, I. (2017). Translators and machine translation: Shifting paradigms in translator education. Translation & Interpreting, 9(2), 55–70.
Johnson, M., Schuster, M., Le, Q. V., Krikun, M., Wu, Y., Chen, Z., Dean, J. (2017). Google’s multilingual neural machine translation system: Enabling zero-shot translation. Transactions of the Association for Computational Linguistics, 5, 339–351. https://doi.org/10.1162/tacl_a_00065
Kenny, D. (2022). Translator training in the age of artificial intelligence. The Translator, 28(2), 123–139. https://doi.org/10.1080/13556509.2022.2048765
Koehn, P. (2020). Neural machine translation. Cambridge University Press.
Massachusetts Institute of Technology (MIT). (2021). MIT launches research initiatives on AI in higher education. MIT News. https://news.mit.edu
Pym, A. (2021). Translation solutions for many languages: Histories of a flawed dream. Bloomsbury Academic.
Russell, S., & Norvig, P. (2020). Artificial intelligence: A modern approach (4th ed.). Pearson.
Toral, A. (2020). Post-editese: An exacerbated translationese. Machine Translation, 34(1), 41–67. https://doi.org/10.1007/s10590-019-09236-9
Toral, A., & Way, A. (2018). What level of quality can neural machine translation attain on literary text? In J. Moorkens, S. Castilho, F. Gaspari, & S. Doherty (Eds.), Translation quality assessment: From principles to practice (pp. 263–287). Springer.
