Investigating student engagement with AI-driven feedback in translation revision: A mixed-methods study
Simin Xu, Yanfang Su, Kanglong Liu
Abstract
Abstract Despite the well-established importance of feedback in education, the application of Artificial Intelligence (AI)-generated feedback, particularly from language models like ChatGPT, remains understudied in translation education. This study investigates the engagement of Master’s students in translation with ChatGPT-generated feedback during their revision process. A mixed-methods approach, combining a translation-and-revision experiment with quantitative and qualitative analyses, was employed to examine the feedback, translations before and after revision, the revision process, and student reflections. The results reveal complex interrelations among cognitive, affective, and behavioural dimensions influencing students’ engagement with AI feedback and their subsequent revisions. Specifically, the findings indicate that students invested considerable cognitive effort in the revision process, despite finding the feedback comprehensible. Moreover, they exhibited moderate affective satisfaction with the feedback model. Behaviourally, their actions were largely influenced by cognitive and affective factors, although some inconsistencies were observed. This research provides novel insights into the potential applications of AI-generated feedback in translation teaching and opens avenues for further investigation into the integration of AI tools in language teaching settings.