Article published In: Human-centeredness in Translation: Advancing Translation Studies in a human-centered AI era
Guest-edited by Miguel A. Jiménez-Crespo
[InContext 5:1] 2025
► pp. 167–193
Human-centered pedagogies in the age of generative AI
Examining student perceptions of augmentation, progress, and agency
Available under the Creative Commons Attribution-NonCommercial-NoDerivatives (CC BY-NC-ND) 4.0 license.
For any use beyond this license, please contact the publisher at rights@benjamins.nl.
Published online: 31 May 2025
https://doi.org/10.54754/incontext.v5i1.114
https://doi.org/10.54754/incontext.v5i1.114
Abstract
Since its release in November 2022, ChatGPT has been used by students alongside other AI-powered tools like machine translation for various language-related tasks. Despite its growing use, educators and researchers have not yet fully monitored or understood its impact within academic settings. This study investigates the perceptions of undergraduate students enrolled in a translation course in an English Degree program, focusing on how they view, experience, and use generative AI, with particular emphasis on ChatGPT as the most widely used tool. Conducted as part of the teaching innovation project Multilingual Competence: Implementing AI (ChatGPT) for Multilingual Classroom Success at Universitat Rovira i Virgili, Spain, this study spanned five 90-minute sessions, during which students engaged with ChatGPT through three types of exercises that involved translation, post-editing, and comprehension of texts generated by this tool. Pre- and post-experiment questionnaires were administered to examine the impact of ChatGPT on students’ perceptions of progress and sense of agency. The findings indicate that students generally hold neutral-to-positive views regarding the effectiveness of ChatGPT in translating, writing texts, and language learning. However, ChatGPT received particular criticisms as a post-editing tool, as evidenced by quantitative as well as qualitative data. The students emphasized the need for additional training, particularly in prompt generation. On the other hand, some students expressed their concerns regarding data privacy or various ethical issues such as the environmental impact of ChatGPT. In terms of agency, quantitative and qualitative data shows that most students believe that they retain significant control in their interactions with ChatGPT. These results suggest that while students recognize the potential of ChatGPT, they are also aware of its limitations. These findings align with Human-Centered Artificial Intelligence (HCAI) approaches, which emphasize the importance of human control and critical thinking as fundamental principles in fostering effective and responsible human-machine interactions.
Keywords: Generative AI, ChatGPT, translation, editing, questionnaire
Аннотация
С момента выхода ChatGPT в ноябре 2022 года, студенты стали пользоваться им наряду с другими системами на базе искусственного интеллекта (ИИ), такими как машинный перевод, для выполнения различных языковых заданий. Несмотря на растущее использование ChatGPT, преподаватели и исследователи до сих пор не полностью оценили или осознали его влияние в академической среде. В статье анализируется восприятие и использование генеративного ИИ студентами-бакалаврами, обучающимися на курсах перевода в рамках программы по английскому языку, с акцентом на ChatGPT как на наиболее популярную из существующих систем. Исследование проводилось в рамках учебно-инновационного проекта «Многоязычная компетенция: Применение ИИ (ChatGPT) для обеспечения успеха в многоязычных классах». Это исследование длилось на протяжении пяти занятий продолжительностью 90 минут, в ходе которых студенты выполнили три упражнения помощью ChatGPT, включая перевод, постредактирование и понимание текстов, созданных ChatGPT. Для анализа воздействия ChatGPT на восприятие студентами собственных достижений и уровня самостоятельности был проведён опрос. Результаты показали, что студенты в целом придерживаются нейтрально- положительного мнения относительно эффективности ChatGPT в переводе, составлении текстов и изучении языка. Однако ChatGPT подвергся особой критике как инструмент постредактирования, о чем свидетельствуют как статистические, так и качественные данные. Студенты подчеркнули необходимость дополнительного обучения, особенно в области создания промптов. Кроме того, некоторые студенты выразили свою обеспокоенность по поводу конфиденциальности данных или различных этических вопросов, таких как воздействие ChatGPT на окружающую среду. С точки зрения автономности, количественные и качественные данные показали, что большинство студентов считают, что они сохраняют значительный контроль при взаимодействии с ChatGPT. Эти замечания свидетельствуют о том, что, хотя студенты признают потенциал ChatGPT, они также осознают его недостатки. Эти результаты согласуются с человеко-ориентированным подходом к искусственному интеллекту (Human-Centered AI или HCAI), который подчёркивает важность человеческого контроля и критического мышления как основополагающих принципов в содействии эффективному и ответственному взаимодействию человека и машины.
Ключевые слов а: Генеративный ИИ, ChatGPT, пер ев од, редактирование, опрос
References (31)
Altan, Mustapha X. (2006). Beliefs about language learning of foreign language-major university students. Australian Journal of Teacher Education, 31(2), 45–52.
Ashouri, Amir Farzad and Zahra Fotovatnia. (2010). The effect of individual differences on learners’ translation belief in EFL learning. English Language Teaching, 3(4), 228–236.
Ayvazyan, Nune. (2017). Communicative translation in foreign-language teaching and learning [PhD dissertation]. Universitat Rovira i Virgili. [URL]
Ayvazyan, Nune, Ester Torres-Simón and Anthony Pym. (2024). Translation students’ trust in machine translation: Too good to be true? In Cem Odacioglu (Ed.), Revolutionizing Translation Studies: Synthesizing Translation with AI and IT innovation (pp. 9–30). Aktif.
Boakye, Naomi. (2007). Investigating students’ beliefs about language learning. Per Linguam, 23(2), 1–14.
Briggs, Neil. (2018). Neural machine translation tools in the language learning classroom: Students’ use, perceptions, and analyses. The Jalt Call Journal, 14(1), 3–24. [URL]
Capel, Tara and Margot Brereton. (2023, April 23–28). What is human-centered about human-centered AI? A map of the research landscape. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, Hamburg, Germany.
Carreres, Angeles. (2006, December 5–6). Strange bedfellows: Translation and language teaching. The Sixth Symposium on Translation, Terminology and Interpretation in Cuba and Canada, Havana, Cuba.
Chen, Lijia, Pingping Chen and Zhijian Lin. (2020). Artificial intelligence in education: A review. Ieee Access, 81, 75264–75278.
Devasena, R. (2024). Artificial intelligence in education: An alternative to traditional learning. Journal of English Language Teaching, 66(1), 13–21.
ELIS. (2024). European Language Industry Survey: Trends, Expectations and Concerns of the European Language Industry. European Union of Associations of Translation Companies. [URL]
Freitag, Markus, George Foster, David Grangier, Viresh Ratnakar, Qijun Tan and Wolfgang Macherey. (2021). Experts, errors, and context: A large-scale study of human evaluation for machine translation. Transactions of the Association for Computational Linguistics, 91, 1460–1474.
Horwitz, Elaine K. (1985). Using student beliefs about language learning and teaching in the foreign language methods course. Foreign Language Annals, 18(4), 333–340.
Janiesch, Christian, Patrick Zschech and Kai Heinrich. (2021). Machine learning and deep learning. Electronic Markets, 31(3), 685–695.
Jiménez-Crespo, Miguel Ángel. (2024). Exploring professional translators’ attitudes towards control and autonomy in the human-centred AI era: Quantitative results from a survey study. Tradumàtica, 221, 276–301.
Kasneci, Enkelejda, Kathrin Seßler, Stefan Küchemann, Maria Bannert, Daryna Dementieva, Frank Fischer, Urs Gasser, Georg Groh, Stephan Günnemann and Eyke Hüllermeier. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and individual differences, 1031, 102274.
Kelly, Niamh and Jennifer Bruen. (2015). Translation as a pedagogical tool in the foreign language classroom: A qualitative study of attitudes and behaviours. Language Teaching Research, 19(2), 150–168.
Kuntz, Patricia S. (1996). Beliefs about language learning: The Horwitz model (No. ED397649). ERIC Document Reproduction Service. [URL]
Lee, Min, Der-Thanq Chen, Roy Jun Yi Tan and Wei Loong David Hung. (2024). Re-conceptualising learner feedback agency: a situational, deliberative and entangled perspective. Assessment & Evaluation in Higher Education, 49(5), 680–692.
Liao, Posen. (2006). EFL learners’ beliefs about and strategy use of translation in English learning. RELC Journal, 37(2), 191–215.
Moorkens, Joss. (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.
O’Brien, Sharon. (2024). Human-centered augmented translation: Against antagonistic dualisms. Perspectives, 32(3), 391–406.
Pym, Anthony and Yu Hao. (2025). How to Augment Language Skills: Generative AI and Machine Translation in Language Learning and Translator Training. Routledge.
Shayan, Shakila, Marlies van Steenbergen, Koen van Turnhout, Zelda Zeegers, Somaya Ben Allouch, Maaike Harbers, Guido Evertzen, Janna Bruijning, Wieke van Boxtel and Katarina Jerkovich. (2023, August 28–September 1). AI in the human loop: The impact of differences in digital assistant roles on the personal values of users. Human-Computer Interaction — INTERACT 2023. INTERACT 2023. 19th IFIP TC13 International Conference, York, UK.
Shneiderman, Ben. (2020). Human-centered artificial intelligence: Three fresh ideas. AIS Transactions on Human-Computer Interaction, 12(3), 109–124.
Sison, Alejo Jose G., Marco Tulio Daza, Roberto Gozalo-Brizuela and Eduardo C. Garrido-Merchán. (2024). ChatGPT: More than a “weapon of mass deception” ethical challenges and responses from the human-centered artificial intelligence (HCAI) perspective. International Journal of Human–Computer Interaction, 40(17), 4853–4872.
Tsai, Shu-Chiao. (2022). Chinese students’ perceptions of using Google Translate as a translingual CALL tool in EFL writing. Computer Assisted Language Learning, 35(5–6), 1250–1272.
Vallor, Shannon. (2024). Defining human-centered AI: An interview with Shannon Vallor. In Catherine Régis, Jean-Louis Denis, Maria Luciana Axente & Atsuo Kishimot (Eds.), Human-Centered AI. A Multidisciplinary Perspective for Policy-Makers, Auditors, and Users (pp. 13–20). Chapman and Hall/CRC.
Vitaz, Milica and Sandra Vasković. (2024). Insight into the latest innovations: University students’ opinions and attitudes on using ChatGPT for educational purposes. Facta Universitatis, Series: Linguistics and Literature, 22(1), 17–25.
Wang, Lulu, Simin Xu and Kanglong Liu. (2024). Understanding students’ acceptance of ChatGPT as a translation tool: A UTAUT model analysis. arXiv preprint, arXiv:2406.06254.
Ziani, Safae. (2024). Is ChatGPT a friend or foe for translation students? [Unpublished PhD dissertation]. Universitat Rovira i Virgili.