AI VERSUS HUMAN ENGLISH-VIETNAMESE TRANSLATION: A STUDY AT A PRIVATE UNIVERSITY

Authors

  • Bùi Kim Phượng Bình Dương University, Ho Chi Minh City, Vietnam
  • Nguyễn Minh Trang Bình Dương University, Ho Chi Minh City, Vietnam

DOI:

https://doi.org/10.46827/ejmts.v6i1.718

Keywords:

AI-assisted translation, human translation, neural machine translation, post-editing, novice translators

Abstract

The rapid progress in Artificial Intelligence (AI) technology, particularly in Neural Machine Translation (NMT) systems, has significantly impacted translation practices across the world. The use of AI systems like ChatGPT and DeepL is becoming more popular, and young translators in translation programs in Vietnam are more likely to use these systems in their academic work. However, there is little research on the pedagogical implications of these systems. This research focused at examining the effectiveness of AI-assisted translation and human translation in English-Vietnamese language pair for 67 English major students at Binh Duong University. A mixed-methods design was employed, in which two translation tasks were assigned: independent human translation and AI-assisted translation followed by post-editing to discover the two research questions. An analytic rubric was used in assessing translation quality, and descriptive statistics and thematic analysis were employed in data analysis. The results show that AI-assisted translation is superficial in nature, as only semantic accuracy is fulfilled while terminology, cultural sensitivity and pragmatic ability are lacking. The AI tool scored on average Fair to Good, while human translation scored Very Good on all nine criteria. Novice translators over-relied on the AI tool and lacked the critical thinking required in the post-editing phase, although they recognized its importance. More than half also expressed the need for pedagogical support. The study indicates that the AI tool should be used as a supplement, not a substitute, in the pedagogical process and recommends that teaching AI literacy, critical review, fast engineering, and post-editing should be done along with human translation.

Author Biographies

  • Bùi Kim Phượng, Bình Dương University, Ho Chi Minh City, Vietnam

    Bùi Kim Phượng is a lecturer at the Informatics & Foreign Language Center, Binh Duong University (IFLC-BDU), Vietnam. She has been teaching English as a Foreign Language (EFL) at the tertiary education level for two years. She can be reached at phuongbinhduongbui@gmail.com.

  • Nguyễn Minh Trang, Bình Dương University, Ho Chi Minh City, Vietnam

    Nguyễn Minh Trang is a lecturer at the Faculty of Foreign Languages, Binh Duong University, Ho Chi Minh City, Vietnam. He has been teaching English as a Foreign Language (EFL) at the tertiary level for over 25 years. He has several publications in international journals. His research interests include academic listening, ELT issues, translation and interpretation studies. He can be reached at nguyenminhtrang@hotmail.com.

References

Al-Afnan, M. A. (2024). Large Language Models as Computational Linguistics Tools: A Comparative Analysis of ChatGPT and Google Machine Translations. Journal of Artificial Intelligence Theory, 5, 1–15. https://doi.org/10.37965/jait.2024.0549

Arnold, D., Balkan, L., Humphreys, G., Meijer, S., & Robertson, S. (1994). Machine translation: An introductory guide. NCC Blackwell.

Bahdanau, D., Cho, K., & Bengio, Y. (2015). Neural machine translation by jointly learning to align and translate. arXiv. https://arxiv.org/abs/1409.0473

Baker, M., & Saldanha, G. (Eds.). (2020). Routledge encyclopedia of translation studies (3rd ed.). Routledge. https://doi.org/10.4324/9781315678626

Bell, R. T. (1991). Translation and translating: Theory and practice. Longman. Retrieved from https://books.google.ro/books/about/Translation_and_Translating.html?id=QLxrAAAAIAAJ&redir_esc=y

Bowker, L. (2020). Machine translation literacy instruction for international students. John Benjamins. https://doi.org/10.1080/08963568.2020.1794739

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates. https://doi.org/10.4324/9780203771587

Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE Publications. Retrieved from https://books.google.ro/books/about/Research_Design.html?hl=es&id=335ZDwAAQBAJ&redir_esc=y

Dörnyei, Z. (2007). Research methods in applied linguistics. Oxford University Press. Retrieved from https://books.google.ro/books/about/Research_Methods_in_Applied_Linguistics.html?id=gNh6QgAACAAJ&redir_esc=y

Field, A. (2013). Discovering statistics using IBM SPSS statistics (4th ed.). SAGE Publications. Retrieved from https://books.google.ro/books/about/Discovering_Statistics_Using_IBM_SPSS_St.html?hl=id&id=c0Wk9IuBmAoC&redir_esc=y

Guest, G., Bunce, A., & Johnson, L. (2006). How many interviews are enough? An experiment with data saturation and variability. Field Methods, 18(1), 59–82. https://doi.org/10.1177/1525822X05279903

House, J. (2015). Translation quality assessment: Past and present. Routledge. https://doi.org/10.4324/9780203072934

Hutchins, J. (1995). Machine translation: A concise history. Computing Research Quarterly. Retrieved from https://doi.org/10.65961/jts-2010-1_2-003

Jakobson, R. (2004). On linguistic aspects of translation. In L. Venuti (Ed.), The translation studies reader (2nd ed., pp. 138–143). Routledge. (Original work published 1959). Retrieved from https://web.stanford.edu/~eckert/PDF/jakobson.pdf

Jiao, W., Wang, W., Huang, J. T., Wang, X., & Tu, Z. (2023). Towards making the most of ChatGPT for machine translation. In Findings of the Association for Computational Linguistics: EMNLP 2023 (pp. 6262–6278). Association for Computational Linguistics. https://aclanthology.org/2023.findings-emnlp.373

Koehn, P. (2020). Neural machine translation. Cambridge University Press. https://doi.org/10.1017/9781108802302

Koponen, A. (2016). Is machine translation post-editing worth the effort? A usability study with student translators. JoSTrans: The Journal of Specialised Translation, 25.

Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159–174. https://doi.org/10.2307/2529310

Lörscher, W. (1991). Translation performance, translation process and translation strategies. Retrieved from https://books.google.ro/books/about/Translation_Performance_Translation_Proc.html?id=aanlAAAAMAAJ&redir_esc=y

Mammadova, I. (2024). Machine translation vs. human translation: A comparative analysis. Journal of Language and Translation Studies, 12(2), 45–67. Retrieved from https://egarp.lt/index.php/JPURM/article/view/174

Martindale, M. J., & Carpuat, M. (2018). Fluency over adequacy: A pilot study in measuring user trust in imperfect MT. Proceedings of the 13th Conference of the Association for Machine Translation in the Americas, 13–25. https://aclanthology.org/W18-6302/

Nababan, M. R., Nuraeni, A., & Sumardiono. (2012). Pengembangan model penilaian kualitas terjemahan. Kajian Linguistik và Sastra, 17(1), 39–57.

Nguyen, K. M., Nguyen, T. H., Luu, K. Q., & Nguyen, L. M. (2022). A Vietnamese-English neural machine translation system. In Interspeech 2022 (pp. 1234–1238). ISCA. https://www.isca-speech.org/archive/interspeech_2022/

Nida, E. A. (1964). Toward a science of translating. Brill. Retrieved from https://books.google.ro/books/about/Toward_a_Science_of_Translating.html?id=T9A7EAAAQBAJ&redir_esc=y

Nunnally, J. C. (1978). Psychometric theory (2nd ed.). McGraw-Hill. Retrieved from https://books.google.ro/books/about/Psychometric_Theory.html?id=WE59AAAAMAAJ&redir_esc=y

O'Brien, S. (2012). Translation as human-computer interaction. Translation Spaces, 1(1), 101–122. https://doi.org/10.1075/ts.1.06obr

OECD. (2022). AI and the future of skills, Volume 1: Capabilities and assessments. OECD Publishing. https://doi.org/10.1787/5b324866-en

PACTE Group. (2011). Strategy and translation competence. In K. Windle & A. Pym (Eds.), The Oxford handbook of translation studies (pp. 391–402). Oxford University Press.

Pym, A. (2013). Translation skill-sets in a machine-translation age. Meta, 58(3), 487–503. https://doi.org/10.7202/1025047ar

Reiss, K. (1971). Möglichkeiten und Grenzen der Übersetzungskritik. Hueber. Retrieved from https://books.google.ro/books/about/M%C3%B6glichkeiten_und_Grenzen_der_%C3%9Cbersetz.html?id=M31JAQAAIAAJ&redir_esc=y

Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6th ed.). Pearson. Retrieved from https://books.google.ro/books/about/Using_Multivariate_Statistics.html?id=ucj1ygAACAAJ&redir_esc=y

UNESCO. (2021). Recommendation on the ethics of artificial intelligence. https://unesdoc.unesco.org/ark:/48223/pf0000381115

Vaswani, A., et al. (2017). Attention is all you need. In Advances in Neural Information Processing Systems, 30. https://arxiv.org/abs/1706.03762

Vermeer, H. J. (1989). Skopos and commission in translational action. In A. Chesterman (Ed.), Readings in translation theory (pp. 173–187). Finnlectura. Retrieved from https://www.taylorfrancis.com/chapters/edit/10.4324/9780429280641-26/skopos-commission-translational-action-hans-vermeer-andrew-chesterman

Wei, J., et al. (2025). Representation drift in long-context machine translation. arXiv preprint arXiv:2505.16894.

Williams, J. (2013). Theories of translation. Palgrave Macmillan. https://books.google.ro/books/about/Theories_of_Translation.html?id=-agox8ZrSg8C&redir_esc=y

Wolff, J. (2024). Zero-shot learning and its implications for neural machine translation. AI & Society, 39(1), 112–125.

Zouhar, V., Tamchyna, A., Popel, M., & Bojar, O. (2021). Neural Machine Translation Quality and Post-Editing Performance. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 10204–10214). https://doi.org/10.18653/v1/2021.emnlp-main.801

Downloads

Published

2026-06-26

Issue

Section

Articles