An Analytical Study on Student Perceptions of Using CHATGPT in Language Translation
Main Article Content
Abstract
This study explores user perceptions and practical employment of ChatGPT Translation among students involved in English-Arabic translation tasks. For the quantitative part method, data were collected from a diverse assembly of 102 participants, comprising demographic queries and a constructed 20-item Likert scale questionnaire. The collected data were categorized and critically examined under five principal constructs: Efficiency, Accuracy, Ease of Use, Trustworthiness, and Overall Preference. The findings indicate a predominantly positive perception of ChatGPT among the participants. In addition, detailed analysis reveals novel insights, such as the considerable appreciation for ChatGPT's role in enhancing translation efficiency and the high level of trust expressed in its ability to maintain the confidentiality of translated work. Another significant finding is the tool's competitive edge, with participants favoring ChatGPT over other translation tools. Furthermore, the findings underscore the importance of extending the research landscape on AI-assisted translation tools. Thus, extending the research on A.I. Translation platforms fosters their effective integration into the industry and understanding of their potential impact on the future of translation pedagogy.
Downloads
Article Details
References
References
Almahasees, Zakaryia. (2023). Analysing English-Arabic Machine Translation: Google Translate, Microsoft Translator and Sakhr. Routledge.
Almahasees, Z., & Mahmoud, S. (2022). Evaluation of google image translate in rendering arabic signage into English. World Journal of English Language, 12(1).
Almahasees, Z. M. (2017). Assessing the translation of Google and Microsoft Bing in translating political texts from Arabic into English. International Journal of Languages, Literature and Linguistics, 3(1), 1-4.
Almahasees, Z. M. (2018). Assessment of Google and Microsoft Bing translation of journalistic texts. International Journal of Languages, Literature and Linguistics, 4(3), 231-235.
Archila, P. A., & de Mejía, A. M. T. (2020). Bilingual teaching practices in university science courses: How do biology and microbiology students perceive them?. Journal of Language, Identity & Education, 19(3), 163-178.
Bowker, L. (2020). Chinese speakers' use of machine translation as an aid for scholarly writing in English: a review of the literature and a report on a pilot workshop on machine translation literacy. Asia Pacific Translation and Intercultural Studies, 7(3), 288-298.
Cerdá Suárez, L. M., Núñez-Valdés, K., & Quirós y Alpera, S. (2021). A systemic perspective for understanding digital transformation in higher education: Overview and subregional context in Latin America as evidence. Sustainability, 13(23), 12956.
Deng, X., & Yu, Z. (2022). A systematic review of machine-translation-assisted language learning for sustainable education. Sustainability, 14(13), 7598.
Han, C., & Lu, X. (2023). Can automated machine translation evaluation metrics be used to assess students' interpretation in the language learning classroom?. Computer Assisted Language Learning, 36(5-6), 1064-1087.
Hasyim, M., Saleh, F., Yusuf, R., & Abbas, A. (2021). Artificial Intelligence: Machine Translation Accuracy in Translating French-Indonesian Culinary Texts. Available at SSRN 3816594.
Kirov, V., & Malamin, B. (2022). Are Translators Afraid of Artificial Intelligence?. Societies, 12(2), 70.
Klimova, B., Pikhart, M., Benites, A. D., Lehr, C., & Sanchez-Stockhammer, C. (2023). Neural machine translation in foreign language teaching and learning: a systematic review. Education and Information Technologies, 28(1), 663-682.
Lee, S. M. (2020). The impact of using machine translation on EFL students' writing. Computer assisted language learning, 33(3), 157-175.
Li, H., & Chen, H. (2019). Human vs. ai: An assessment of the translation quality between translators and machine translation. International Journal of Translation, Interpretation, and Applied Linguistics (IJTIAL), 1(1), 1-12.
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., ... & Lowe, R. (2022). Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35, 27730-27744.
Peng, K., Ding, L., Zhong, Q., Shen, L., Liu, X., Zhang, M., ... & Tao, D. (2023). Towards making the most of chatgpt for machine translation. arXiv preprint arXiv:2303.13780.
Pym, A. (2013). Translation skill-sets in a machine-translation age. Meta, 58(3), 487-503.
Sujarwo, S. (2020). Students' perceptions of using machine translation tools in the EFL classroom. Al-Lisan: Jurnal Bahasa (e-Journal), 5(2), 230-241.
Van Lieshout, C., & Cardoso, W. (2022). Google Translate as a tool for self-directed language learning.
Xu, J. (2020). Machine Translation for Editing Compositions in a Chinese Language Class: Task Design and Student Beliefs. Journal of Technology & Chinese Language Teaching, 11(1).