Profiling Chinese Student Interpreters’ Usage Intention of ChatGPT-assisted Translation by Q Methodology

by Cui CUI
College of Foreign Languages and Literature, Fudan University, Shanghai, People’s Republic of China; School of Foreign Languages, Wuhan City Polytechnic, Wuhan, People’s Republic of China.

10.46679/9788196780593ch06

Cui, C. (2024). Profiling Chinese Student Interpreters’ Usage Intention of ChatGPT-assisted Translation by Q Methodology. In T. Chuanmao & D. Juntao, Translating the Future: Exploring the Impact of Technology and AI on Modern Translation Studies (pp 137-176). CSMFL Publications. https://dx.doi.org/10.46679/9788196780593ch06

Abstract

This study investigates the Chinese student interpreters’ usage intention of ChatGPT-assisted translation with Q methodology. Guided by technology acceptance theory (TAM), the research aims to explore the usage intention profiles and consensus, as well as the differences between them. Q sort tasks were undertaken by 30 participants, and their commentary data was elicited to complement Q sort analysis. From the factor analysis, four factors were obtained, namely deeper-purpose seekers, big picture reflectors, deep understanders and coordinators. The distinguishing statements include the subject, technology, information and society elements. The article concludes with a discussion of the pedagogical implications of learning interpreting of university students.

Keywords: : Usage intention profiles; ChatGPT-assisted translation; Chinese student interpreters; Q methodology

This chapter is a part of: Translating the Future: Exploring the Impact of Technology and AI on Modern Translation Studies

© CSMFL Publications & its authors.
Published: November 12, 2024

References

  1. Ajzen, I. (1980). Understanding attitudes and predicting social behavior. Englewood cliffs.
  2. Braun, S (2013) Keep your distance? Remote interpreting in legal proceedings: A critical assessment of a growing practice. Interpreting 15 (2), 200-28.
  3. Brown, S. R. (1980). Political subjectivity. New Haven, CT: Yale University Press.
  4. Caldarini, G., Jaf, S., & McGarry, K. (2022). A literature survey of recent advances in chatbots. Information, 13(1), 41. https://doi.org/10.3390/INFO13010041
  5. Carey, J. M., & Kacmar, C. J. (2010). Cultural and language effects on technology acceptance and attitude: Chinese perspectives. International Journal of Information Technology, 16(1), 1-19.
  6. Carl, M. & Braun, S. (2018). Translation, interpreting and new technologies. In K. Malmkjaer (ed), The Routledge Handbook of Translation Studies and Linguistics. London: Routledge, 374-390.
  7. Chatterjee, J., & Dethlefs, N. (2023). This new conversational AI model can be your friend, philosopher, and guide, and even your worst enemy. Patterns, 4(1), 100676. https://doi.org/10.1016/j.patter.2022.100676
  8. Chen, Y. & Qiu M. M. Application of TAM in distance education based on Internet. The Chinese Journal of ICT in Education. 2005 (2), 72-73.
  9. Christie, B. (1981). Face to file communication: A psychological approach to information systems. John Wiley & Sons, Inc.
  10. Cotton, D. R., Cotton, P. A., & Shipway, J. R. (2023). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT, Innovations in Education and Teaching International. https://dx.doi.org/10.1080/14703297.2023.2190148
  11. Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems: Theory and results (Doctoral dissertation, Massachusetts Institute of Technology).
  12. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management science, 35(8), 982-1003.
  13. Dehouche, N. (2021). Plagiarism in the age of massive Generative Pre-trained Transformers (GPT-3). Ethics in Science and Environmental Politics, 21, 17-23. https://doi.org/10.3354/esep00195
  14. Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., & Wright, R. (2023). “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642
  15. Farrokhnia, M., Banihashem, S. K., Noroozi, O., & Wals, A. (2023). A SWOT analysis of ChatGPT: Implications for educational practice and research. Innovations in Education and Teaching International, 1-15.
  16. Gao, Y., Wang, R., & Hou, F. (2023). How to Design Translation Prompts for ChatGPT: An Empirical Study. arXiv e-prints, arXiv-2304. https://doi.org/10.48550/arXiv.2304.02182
  17. Gil, José Ramón Biau, & Anthony Pym. “Technology and translation (a pedagogical overview).” Translation Technology and its Teaching, Intercultural Studies Group, Universitat Rovira i Virgili, Tarragona (2006).
  18. Gile, Daniel (1995). Basic Concepts and Models for Interpreter and Translator Training. Amsterdam and Philadelphia: John Benjamins.
  19. Gordijn, B., Have, H.T. (2023). ChatGPT: evolution or revolution?. Medicine, Health Care and Philosophy. 26: 1–2. https://doi.org/10.1007/s11019-023-10136-0
  20. Hoffman, R. R. (1997). The cognitive psychology of expertise and the domain of interpreting. Interpreting, 2(1-2), 189-230.
  21. Ire, K. (2014). Q methodology for post-social-turn research in SLA. Studies in Second Language Learning and Teaching, 4(1), 13-32.
  22. Irie, K., & Ryan, S. (2015). Study abroad and the dynamics of change in learner L2 self-concept. Motivational dynamics in language learning, 343-366.
  23. Jiao, W., Wang, W., Huang, J. T., Wang, X., & Tu, Z. P. (2023). Is ChatGPT a good translator? Yes, with GPT-4 as the engine. arXiv preprint, arXiv:2301.08745. https://arxiv.org/abs/2301.08745
  24. Kalina, S.; Ziegler, K. (2015). Technology, in: F. Pöchhacker (ed). Routledge Encyclopedia of Interpreting Studies. New York: Routledge, pp. 410–412.
  25. Ling, X.X., Wang D. M. & Yuan J. (2023). Reflection on Technology Ethics and Academic Ethics in the Context of ChatGPT Craze. Journal of Xinjiang University (Philosophy and Social Sciences). 44(04), 123-136.
  26. Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., & Ge, B. (2023). Summary of ChatGPT/ GPT-4 research and perspective towards the future of large language models. arXiv preprint arXiv:2304.01852. https://doi.org/10.48550/arXiv.2304.01852
  27. Lo, C. K. (2023). What is the impact of ChatGPT on education? A rapid review of the literature. Education Sciences, 13(4), 410.
  28. Lundberg, A. (2019). Teachers’ beliefs about multilingualism: Findings from Q method research. Current Issues in Language Planning, 20(3), 266-283.
  29. Lundberg, A., de Leeuw, R., & Aliani, R. (2020). Using Q methodology: Sorting out subjectivity in educational research. Educational Research Review, 31, 100361.
  30. MacIntyre, P. D., MacKay, E., Ross, J., Abel, E., Ortega, L., & Han, Z. (2017). The emerging need for methods appropriate to study dynamic systems. Complexity theory and language development: In celebration of Diane Larsen-Freeman, 97-122.
  31. Man D. L. & Mo J. (2023) An empirical study of MTI students’ intention to use translation technology. Foreign Language and Translation (01), 86-91. https://dx.doi.org/10.19502/j.cnki.2095-9648.2023.01.011
  32. Mellinger, C. D. (2019). Computer-assisted interpreting technologies and interpreter cognition: A product and process-oriented perspective. Tradumàtica, (17), 0033-44.
  33. Millar, J. D., Mason, H., & Kidd, L. (2022). What is Q methodology?. Evidence-Based Nursing, 25(3), 77-78.
  34. Mu L. (2022). Translation teaching research in China. Shanghai Foreign Language Education Press.
  35. Ren, X. H. & Zhai, N. (2012). An empirical research of information technology acceptance behaviors of teachers in college classroom teaching. Journal of Distance Education, 2012, 30(02): 84-90.
  36. Rieber, L. P. (2020). Q methodology in learning, design, and technology: An introduction. Educational Technology Research and Development, 68(5), 2529-2549.
  37. Roziner, I. & Shlesinger, M. (2010) Much ado about something remote: Stress and performance in remote interpreting. Interpreting 12(2), 214-47.
  38. Sawyer, D. B. (2004). Fundamental aspects of interpreter education. Fundamental Aspects of Interpreter Education, 1-328.
  39. Schäler, R., Way, A., & Carl, M. (2003). EBMT in a Controlled Environment. Recent advances in example-based machine translation, 83-114.
  40. Swanson, E. B. (1974). Management information systems: appreciation and involvement. Management science, 21(2), 178-188.
  41. Tao, Y. L. (2023) Translation technology teaching in China (1990-2020): Past, Present and Prospect. Shanghai Journal of Translation, 169(2), 49.
  42. Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments, 10(1), 15.
  43. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425-478.
  44. Vieira, L. N. (2020). Automation anxiety and translators. Translation Studies, 13(1), 1-21.
  45. Wang H. S. & Liu S. J. (2023). Smart Translation Education: Concept, Pathways and Prospects. Shanghai Journal of Translators, (03):47-51+95.
  46. Watts, S. & Stenner, P. (2012). Doing Q methodological research: Theory, method & interpretation. Doing Q Methodological Research, 1-248.
  47. Watts, S. (2015). Develop a Q methodological study. Education for Primary Care, 26(6), 435-437.
  48. Yang, Y., & Montgomery, D. (2013). Gaps or bridges in multicultural teacher education: A Q study of attitudes toward student diversity. Teaching and Teacher Education, 30, 27-37.
  49. Zhan C. (2010). Thirty years of interpreting teaching in China: development and current situation. Journal of Guangdong University of Foreign Studies, 21(6), 89-92.
  50. Zhang H., Liu C, Wang D. B. & Zhao Z. X. (2023) Research on the influencing factors of ChatGPT users’ intention. Information studies: Theory & Application, (04),15-22. https://dx.doi.org/10.16353/j.cnki.1000-7490.2023.04.003
  51. Zhao, B. & Feng, Q. H. (2019). On translation technology in the teaching guideline for undergraduate translation majors: connotations, development and footing. Foreign Language World (5), 14-20.
  52. Zheng, Y. H., Zhang M. C., Wang X. N., Zhang B. J. Cheng L. L. & Rao G. Q. (2023). Multi-domain Changes and Challenges Caused by ChatGPT. Journal of Tianjin Normal University (Social Sciences), (3): 49-63.
  53. Zheng, Y., Lu, X., & Ren, W. (2019). Profiling Chinese university students’ motivation to learn multiple languages. Journal of Multilingual and Multicultural Development, 40(7), 590-604.
  54. Zheng, Y., Lu, X., & Ren, W. (2020). Tracking the evolution of Chinese learners’ multilingual motivation through a longitudinal Q methodology. The Modern Language Journal, 104(4), 781-803.

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