Speaker
Description
Abstract
This paper aims to expand previous investigations into the pedagogical application of artificial intelligence (AI) in translation studies by synthesizing empirical survey data on students’ use of AI in Theories and Methods of Transinterpretation (TMT) exercises with an instructional design framework for composing Vietnamese–English structural translation tasks, which are hopefully brought a new atmosphere to further motivate students. Drawing on classroom-based evidence collected from 68 undergraduate students and an applied methodological model developed at HUFLIT, the study proposes a comprehensive, AI-assisted pedagogy that balances automation with human editorial control. The findings demonstrate that AI-enhanced structural translation exercises can significantly improve student motivation, engagement, and perceived competence, provided that instructors actively curate linguistic structures, vocabulary domains, and contextual complexity. The paper contributes a scalable instructional model, a validated exercise-design workflow, and pedagogical implications for translation programs in comparable EFL contexts.
Keywords: structural translation, transinterpretation, learning motivation, Vietnamese–English translation, TMT
