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A Cross Sectional Study of ChatGPT in Translation: Magnitude of Use, Attitudes, and Uncertainties

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Abstract

This preliminary cross-sectional study, focusing on Artificial Intelligence (AI), aimed to assess the impact of ChatGPT on translation within an Arab context. It primarily explored the attitudes of a sample of translation teachers and students through semi-structured interviews and projective techniques. Data collection included gathering information about the advantages and challenges that ChatGPT, in comparison to Google Translate, had introduced to the field of translation and translation teaching. The results indicated that nearly all the participants were satisfied with ChatGPT. The results also revealed that most students preferred ChatGPT over Google Translate, while most teachers favored Google Translate. The study also found that the participants recognized both positive and negative aspects of using ChatGPT in translation. Findings also indicated that ChatGPT, as a recent AI-based translation-related technology, is more valuable for mechanical processes of writing and editing translated texts than for tasks requiring judgment, such as fine-tuning and double-checking. While it offers various advantages, AI also presents new challenges that educators and stakeholders need to address accordingly.

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Data Availability

Data supporting the finding of this study are not openly available because participants of this study did not agree for their data to be shared publicly. Derived data supporting the findings of this study are available from the corresponding author on reasonable request.

References

  • Ali, J. (2023). Benefits and challenges of using ChatGPT: An exploratory study on English language program. University of Bisha Journal for Humanities, 2(2), 629–641.

    Google Scholar 

  • Aljanabi, M., Ghazi, M., Ali, A. H., & Abed, S. A. (2023). ChatGPT: Open possibilities. Iraqi Journal for Computer Science and Mathematics, 4(1), 62–64.

  • Almahasees, Z. (2021). Analysing English-Arabic Machine translation: Google Translate, Microsoft translator and Sakhr. Routledge.

  • Amin, R., & Mandapuram, M. (2021). CMS-Intelligent machine translation with adaptation and AI. ABC Journal of Advanced Research, 10(2), 199–206.

    Article  Google Scholar 

  • Bašić, Ž., Banovac, A., Kružić, I., & Jerković, I. (2023). Better by you, better than me? ChatGPT-3 as writing assistance in students’ essays. arXiv. arXiv:2302.04536.

  • Bassnett, S. (2003). The translation turn in cultural studies. In Translation translation (pp. 433–449). Brill. https://doi.org/10.1163/9789004490093028.

  • Bin-Hady, W. R. A. (2023 October). The impact of machine translation postediting on translation industry: A qualitative research synthesis. The Fifth Scientific Conference (1–2), 2023 Hadhramout University, (pp. 1305–1316).

  • Bin-Hady, W. R. A., Al-Kadi, A., Hazaea, A., & Ali, J. K. M. (2023). Exploring the dimensions of ChatGPT in English language learning: A global perspective. Library Hi Tech. https://doi.org/10.1108/LHT-05-2023-0200.

    Article  Google Scholar 

  • Bowker, L., & Fisher, D. (2010). Computer-aided translation. In Y. Gambier & L. van Doorslaer (Eds.), Handbook of translation studies (Vol. 1, pp. 60–65). John Benjamins Publishing.

  • Catterall, M., & Ibbotson, P. (2000). Using projective techniques in education research. British Educational Research Journal, 26(2), 245–256.

  • Chomsky, N. (2023). Advances in AI: ChatGPT & human brain. Available on YouTube. https://www.youtube.com/Youtube.com/watch?v=-NMR5JXp37k.

  • Dörnyei, Z. (2007). Research methods in applied linguistics: Quantitative, qualitative and mixed methodologies. Oxford University Press.

  • Gao, Y., Wang, R., & Hou, F. (2023). Unleashing the Power of ChatGPT for Translation: An Empirical Study. arXiv preprint arXiv:2304.02182.

  • Haleem, A., Javaid, M., & Singh, R. P. (2022). An era of ChatGPT as a significant futuristic support tool: A study on features, abilities, and challenges. BenchCouncil Transactions on Benchmarks Standards and Evaluations, 2(4), 100089. https://doi.org/10.1016/j.tbench.2023.100089.

    Article  Google Scholar 

  • Holly, M. L., Arhar, J., & Kasten, W. (2005). Action research for teachers: Travelling the yellow brick road. Pearson.

  • James, G. (2020). Introduction to Google Translate. Gilad James Mystery School.

  • Javaid, M., Haleem, A., Singh, R. P., Khan, S., & Khan, I. H. (2023). Unlocking the opportunities through ChatGPT Tool towards ameliorating the education system. Bench Council Transactions on Benchmarks Standards and Evaluations, 3(2), 100115. https://doi.org/10.1016/j.tbench.2023.100115.

    Article  Google Scholar 

  • Jiao, W., Wang, W., Huang, J. T., Wang, X., & Tu, Z. (2023). Is ChatGPT a good translator? A preliminary study. arXiv Preprint arXiv:230108745.

  • Khoshafah, F. (2023). ChatGPT for Arabic-English translation: Evaluating the accuracy. Research Square. https://doi.org/10.21203/rs-2814154/v1.

  • Kim, S. G. (2023). Using ChatGPT for language editing in scientific articles. Maxillofac Plast Reconstr Surg, 45, 13. https://doi.org/10.1186/s40902-023-00381-x.

    Article  PubMed  PubMed Central  Google Scholar 

  • Škobo, M., & Petričević, V. (2023). Navigating the challenges and opportunities of literary translation in the age of AI: Striking a balance between human expertise and machine power. Društvene i humanističke Studije, 8(2), 317–336.

    Article  Google Scholar 

  • Kornacki, M. (2018). Computer-assisted translation (CAT) tools in the translator training process. Peter Lang.

  • Lazaraton, A. (2005). Quantitative research methods. In E. Hinkel (Ed.), Handbook of research in second language teaching and learning (pp. 209–224). Routledge.

  • Lund, B. D., Wang, T., Mannuru, N. R., Nie, B., Shimray, S., & Wang, Z. (2023). ChatGPT and a new academic reality: Artificial intelligence-written research papers and the ethics of the large language models in scholarly publishing. Journal of the Association for Information Science & Technology, 74(5), 570–581.

    Article  Google Scholar 

  • Mahyoob, M., Algaraady, J., & Alblwi, A. (2023). A proposed framework for human-like language processing of ChatGPT in academic writing. International Journal of Emerging Technologies in Learning (iJET), 18(14), 282–293. https://doi.org/10.3991/ijet.v18i14.41725.

    Article  Google Scholar 

  • Maier-Hein, L., Eisenmann, M., Sarikaya, D., März, K., Collins, T., Malpani, A., & Speidel, S. (2022). Surgical data science–from concepts toward clinical translation. Medical Image Analysis, 76, 102306. https://doi.org/10.1016/j.media.2021.102306.

    Article  PubMed  Google Scholar 

  • Malaeb, D., Hallit, R., & Hallit, S. (2023). Assessing Health students’ attitudes and usage of ChatGPT in Jordan: Validation Study. JMIR, 9, e48254. https://mededu.jmir.org/2023/1/e48254/PDF.

  • Mayne, A. (2023). Collaborative creative writing with OpenAI’s ChatGPT. https://andrewmayneblog.wordpress.com/2022/11/30/collaborative-creative-writing-with-openais-chatgpt/.

  • Mohammed, A. A. Q., Al-ghazali, A., & Alqohfa, K. (2023). Exploring ChatGPT uses in higher studies: A case study of arab postgraduates in India. Journal of English Studies in Arabia Felix, 2(2), 9–17. https://doi.org/10.56540/jesaf.v2i2.55.

    Article  Google Scholar 

  • Moqbel, M. S. S., & Al-Kadi, A. M. T. (2023). Foreign language learning assessment in the age of ChatGPT: A theoretical account. Journal of English Studies in Arabia Felix, 2(1), 71–84. https://doi.org/10.56540/jesaf.v2i1.62.

    Article  Google Scholar 

  • Paterson, K. (2023). Machine translation in higher education: Perceptions, policy, and pedagogy. TESOL Journal, 14(2), e690. https://doi.org/10.1002/tesj.690.

    Article  Google Scholar 

  • Paulsen Christensen, T., Bundgaard, K., Schjoldager, A., & Dam Jensen, H. (2022). What motor vehicles and translation machines have in common-a first step towards a translation automation taxonomy. Perspectives, 30(1), 19–38. https://doi.org/10.1080/0907676X.2021.1900307.

    Article  Google Scholar 

  • Peng, K., Ding, L., Zhong, Q., Shen, L., Liu, X., Zhang, M., Ouyang, Y., & Tao, D. (2023). Towards making the most of ChatGPT for machine translation. Available at https://arxiv.org/abs/2303.13780.

  • Petrova, V. (2019). Translation quality assessment tools and processes in relation to CAT tools. In Proceedings of the Human-Informed Translation and Interpreting Technology Workshop (HiT-IT 2019) (pp. 89–97).

  • Pym, A. (2023). Exploring translation theories. Taylor & Francis.

  • Ragni, V., & Nunes Vieira, L. (2022). What has changed with neural machine translation? A critical review of human factors. Perspectives, 30(1), 137–158.

    Article  Google Scholar 

  • Rose, H., McKinley, J., & Baffoe-Djan, B. J. (2020). Data collection research methods in applied linguistics. Bloomsbury Academics.

  • Rothwell, A., Moorkens, J., Fernández-Parra, M., Drugan, J., & Austermueh, F. (2023). Translation tools and technologies. Routledge.

  • Sadasivan, V. S., Kumar, A., Balasubramanian, S., Wang, W., & Feizi, S. (2023). Can AI-generated text be reliably detected?https://arxiv.org/abs/2303.11156?ref=emergentmind.

  • Sanz-Valdivieso, L., & López-Arroyo, B. (2023, June). Google Translate vs. ChatGPT: Can non-language professionals trust them for specialized translation? International Conference Human-informed Translation and Interpreting Technology (HiT-IT 2023), (pp. 97–107). Naples, Italy.

  • Stap, D., & Araabi, A. (2023, July). ChatGPT is not a good indigenous translator. In Proceedings of the Workshop on Natural Language Processing for Indigenous Languages of the Americas (AmericasNLP) (pp. 163–167).

  • Su, J., & Yang, W. (2023). Unlocking the power of ChatGPT: A framework for applying generative AI in education. ECNU Review of Education, 6(3), 355–366. https://doi.org/10.1177/20965311231168423.

    Article  Google Scholar 

  • Wang, H., Wu, H., He, Z., Huang, L., & Church, K. W. (2021). Progress in machine translation. Engineering, 18, 143–153. https://doi.org/10.1016/j.eng.2021.03.023.

    Article  Google Scholar 

  • Way, A. (2018). Quality expectations of machine translation. In J. Moorkens, S. Castilho, F. Gaspari, & S. Doherty (Eds.), Translation quality assessment: From principles to practice (pp. 159–178). Springer International Publishing. Machine Translation Series.

  • Weltman, J. S. (2015). Language processing and the artificial mind: Teaching code literacy in the humanities. Louisiana State University and Agricultural & Mechanical College.

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Acknowledgements

The authors are thankful to the Deanship of Scientific Research at University of Bisha for supporting this work through the Fast-Track Research Support Program.

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  1. Jamal Kaid Mohammed Ali

    Present address: University of Bisha, Bisha, Saudi Arabia

Authors and Affiliations

  1. University of Bisha, Bisha, Saudi Arabia

    Yousef Sahari

  2. Ibb University, Ibb, Yemen

    Abdu M. Talib Al-Kadi

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  1. Yousef Sahari
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Correspondence to Jamal Kaid Mohammed Ali.

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All procedures performed in the present study were reviewed and approved by the institutional review board of University of Bisha (No. 58/44/11700).

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Sahari, Y., Al-Kadi, A.M.T. & Ali, J.K.M. A Cross Sectional Study of ChatGPT in Translation: Magnitude of Use, Attitudes, and Uncertainties. J Psycholinguist Res 52, 2937–2954 (2023). https://doi.org/10.1007/s10936-023-10031-y

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