The Future of Human Translators in the Era of Artificial Intelligence Translation Technologies

Authors

  • Arwaa Al Mudarra Shaqra University

DOI:

https://doi.org/10.17507/jltr.1604.16

Keywords:

artificial intelligence, human translators, machine translation, translation studies, translation technologies

Abstract

This study explored the future of human translators in the era of Artificial Intelligence (AI) translation technologies. Data were collected through semi-structured interviews, enabling an in-depth examination of translators’ insights regarding their evolving roles and the challenges they face in adapting their skills to remain competitive with AI. The results showed that 93% of participants envisioned their future flourishing through collaboration with AI. However, the themes emerging from the data analysis revealed that 97% of participants expressed fears of losing autonomy, job security, professional identity, and creative control unless human translators develop their skills. Additionally, 93% were concerned about diminishing human skills and values, while 90% expressed concerns about translation quality assurance. The findings revealed that 87% of participants faced challenges related to technical complexity and usability, while 83% reported difficulties in accessing technology training resources. To overcome these challenges, the study recommends developing proficiency in AI-powered translation applications and enhancing collaboration with AI. The study contributes to translation studies and technology by emphasizing the importance of adopting collaborative approaches to translation teaching and practice to remain competitive with AI. The study concludes that human translators are indispensable, particularly for enhancing cultural relevance and maintaining ethical standards when using AI translation tools.

Author Biography

Arwaa Al Mudarra, Shaqra University

Department of English Language, Faculty of Science and Humanities, Dhurma

References

Algouzi, S., & Alzubi, F. (2023). The study of AI-mediated communication and socio-cultural language-related variables. Applied Artificial Intelligence, 37(1), 1-21.

Awadh, M. (2024). Challenges and strategies of translating scientific texts: A comparative study of human translation and artificial intelligence. Educational Administration: Theory and Practice, 30(4), 9898-9909.

Bo, L. (2023). Ethical issues for literary translation in the era of artificial intelligence. Bable, 69(4), 529–545.

Bowker, L. (2020). The Routledge handbook of translation and technology (pp. 498-515). Routledge.

Cadwell, P., Federici, F., & O'Brien, S. (2022). Communities of practice and translation: An introduction. The Journal of Specialized Translation, 37, 1-15.

Cadwell, P., & O'Brien, S. (2021). Language, technology, and translation: An ecosystem approach. Machine Translation, 35(1), 1-19.

Carvalho, I., Ramires, A., & Iglesias, M. (2023). Attitudes towards machine translation and languages among travelers. Information Technology Tourism, 25, 175–204.

Catford, C. (1965). A linguistic theory of translation: An essay in applied linguistics. Oxford University Press.

Chomsky, N. (1965). Aspects of the theory of syntax. MIT Press.

Cordingley, A. (2024). Theoretical challenges for genetics of translation. Translation Studies, 17(1), 1–19.

Guerberof-Arenas, A., & Toral, A. (2022). Creativity in translation: Machine translation as a constraint for literary texts. Translation Spaces, 11, 184–212.

Halliday, K. (1985). An introduction to functional grammar. Arnold.

Hancock, T., Naaman, M., & Levy, K. (2020). AI-mediated communication: Definition, research agenda, and ethical considerations. Journal of Computer-Mediated Communication, 25, 89–100.

Hassan, S., Firat, O., & Cho, K. (2018). Achieving human-level performance in neural machine translation. In Proceedings of ACL (pp. 3799-3808).

Hewson, L., & Martin, J. (1991). Redefining translation: The variational approach. Routledge.

Huang, J., Lee, K., & Kim, Y. (2020). Hybrid translation with classification: Revisiting rule-based and neural machine translation. Electronics, 9(2), 201-218.

Hutchins, W., & Somers, H. (1992). An introduction to machine translation. Academic Press.

Ibrahim, H, & Alkhawaja, L. (2023). Comparative evaluation of neural machine translation of fiction literature: A case study. Journal of Namibian Studies, 34, 2806–2822.

Jakobson, R. (2000). On linguistic aspects of translation. Routledge.

Kamaluddin, M., Rasyid, M., Abqoriyyah, F., & Saehu, A. (2024). Accuracy analysis of DeepL: Breakthroughs in machine translation technology. Journal of English Education Forum, 4(2), 122-126.

Kenechi, S. (2024). Artificial intelligence in translation studies: Benefits and challenges. Cascades, Journal of the Department of French & International Studies, 2(1), 5–15.

Khare, K., Blanes-Vidal, B., Nadimi, S., & Acharya, R. (2024). Emotion recognition and artificial intelligence: A systematic review (2014–2023) and research recommendations. Information Fusion, 102, 1-36.

Kirov, V., & Malamin, B. (2022). Are translators afraid of artificial intelligence? Societies, 12(2), 70-84.

Koponen, M., Salmi, L., & Nikulin, M. (2019). A product and process analysis of post-editor corrections on neural, statistical and rule-based machine translation output. Machine Translation, 33, 61–90.

Läubli, S., & Orrego-Carmona, D. (2020). When Google Translate is not enough: Contextual and cultural understanding in translation. Journal of Translation Studies, 14(2), 235-252.

Lin, Y. (2023). The relationship between machine translation and human translation in the era of artificial intelligence machine translation. Proceedings of the 3rd International Conference on Signal Processing and Machine Learning (pp. 133-139).

Liu Q. (2024). A study on Chinese Korean literary translation using GPT: Focused on the translation of idioms in the novel to live. Scholar, 46(1), 273–287.

Madkour, M. & Al Askar, H. (2024). Exploring cognitive teaching approaches for inclusive translation online classes: A case study. Theory and Practice in Language Studies, 14(4), 1219-1230.

Mohammed, S., Khalaf, A., Aljanabi, M., & Mijwil, M. (2024). Challenges and opportunities in translation studies: The evolving role of generative AI in translation development. In N. Mansour & L. M. Bujosa Vadell (Eds.), Sustainability and Financial Services in the Digital Age (pp. 107-117). Springer International Publishing.

Moneus, A. M., & Sahari, Y. (2024). Artificial intelligence and human translation: A contrastive study based on legal texts. Heliyon, 10(6), 1-14.

Moorkens, J. (2022). Ethics and machine translation. In Dorothy Kenny (Ed.) Machine translation for everyone: Empowering users in the age of artificial intelligence (pp. 121- 140). Language Science Press.

Naveen, P. & Trojovsky, P. (2024). Overview and challenges of machine translation for contextually appropriate translations. iScience, 27(10), 1-25.

Newmark, P. (1981). Approaches to translation. Pergamon Press.

Nida, E., & Taber, C. (1974). The theory and practice of translating. Brill, Leiden.

Nimbalkar, P. (2021). Resolution in ambiguity in machine translation using natural language processing techniques. International Journal of Scientific Research and Engineering Development, 4(3), 1346-1350.

O’Hagan, M. (2020). (ed.). The Routledge handbook of translation and technology (pp. 453-468). Routledge.

Öner Bulut, S., & Alimen, N. (2023). Translator education as a collaborative quest for insights into the re-positioning of the human translator (educator) in the age of machine translation. The Interpreter and Translator Trainer, 17(3), 375–392.

Pastor, G., & Noriega-Santiáñez, L. (2024). Human versus neural machine translation creativity: A study on manipulated MWEs in literature. Information, 15(9), 530-548.

Rahul, C., Arathi, T., Panicker, S., & Gopikakumari, R. (2023). Morphology and word sense disambiguation embedded multimodal neural machine translation system between Sanskrit and Malayalam. Biomedical Signal Processing and Control, 85, 791-807.

Ranathunga, S., Lee, A., Prifti Skenduli, M., Shekhar, R., Alam, M., & Kaur, R. (2023). Neural machine translation for low resource languages: A survey. ACM Computer Survey, 55, 1–37.

Reijers, W, & Dupont, Q. (2023). Prolegomenon to contemporary ethics of machine translation. In H. Moniz, C. Parra Escartín (eds.), Towards responsible machine translation, Machine Translation: Technologies and Applications, 4, 11-27.

Reiss, K., & Vermeer, H. (1984). Groundwork for a general theory of translation. Tubingen: Niemeyer.

Sahari,Y., Al-Kadi, T. & Ali, A. (2023). Cross sectional study of ChatGPT in translation: Magnitude of use, attitudes, and uncertainties. Journal of Psycholinguist Research, 52, 2937–2954.

Sheng, A., & Kong, Y. (2023). Neural machine translation and human translation. Babel. International Journal of Translation, 69(4), 483–498.

Snell-Hornby, M. (1988). Translation studies: An integrated approach. John Benjamins.

Soysal, F. (2023). Enhancing translation studies with AI: Challenges, opportunities, and proposals. International Journal of Philology and Translation Studies, 5(2), 12-24.

Trzaskawka, P. (2020). Selected clauses of a copyright contract in Polish and English in translation by Google Translate: A tentative assessment of quality. International Journal of Semiotic Law, 33, 689–705.

Tuma, M., & Hamood, F. (2023). The impact of technology on translation. Journal of Language Studies, 7(2), 377-395.

Vermeer, H. (1996). A Skopos theory of translation: Some arguments for and against. TextconText-Verlag.

Wang, L. (2023). The impacts and challenges of artificial intelligence translation tool on translation professionals. In SHS Web of Conferences, 163, 1-6.

Wang, X., Chen, C., & Xing, Z. (2019). Domain-specific machine translation with recurrent neural network for software localization. Empirical Software Engineering, 24(6), 3514-3545.

Yang, C. (2022). The application of artificial intelligence in translation teaching. In Proceedings of the 4th International Conference on Intelligent Science and Technology, 56-60.

Yousif, S., & Khalaf, A. (2024). Revolutionizing translation with AI: Unravelling neural machine translation and generative pre-trained large language models. Springer Proceedings in Business and Economics (pp. 107-117).

Yu, L. (2024). A Comparative analysis of human translation and machine translation’s impact on pedagogy from a linguistic perspective. Lecture Notes in Education Psychology and Public Media, 34(1), 313–318.

Zayed, M., & Nuirat, W. (2024). Human touch in AI translation: Strategies employed by professional translators in post editing Arabic-English AI generated translations of media texts. Educational Administration: Theory and Practice, 30(4), 2055-2062.

Zhang, J., & Zong, C. (2020). Neural machine translation: Challenges, progress and future. China Technology Science, 63, 2028–2050.

Downloads

Published

2025-07-01

Issue

Section

Articles