Talking to algorithms, not students: Students' and lecturers' perceptions of machine translation in academic discussion

Abstract

Machine translation (MT) software has been present in English for Academic Purposes classrooms for several years, yet it remains a contentious issue. Traditionally, research in this field has focused on students’ use of MT for reading and writing rather than classroom listening and speaking. This study elicited the reasons why students utilise MT in academic discussions and their attitudes towards it. These were then contrasted with the attitudes and experiences of their lecturers. A mixed-methods design was employed, with qualitative data complementing quantitative data. Questionnaires were distributed to a cohort of master’s students (N = 88) and lecturers (N = 14). Following this, focus group discussions explored the role of MT regarding students’ understanding of lectures and assigned reading material. The findings revealed that MT use is widespread in university classrooms, but only a minority of students use it frequently – an unexpected finding in the twenty-first century. The latter group generally regarded it as a source of shame and expressed a desire to reduce their reliance on it. Because the students surveyed often reported dissatisfaction with using MT, alternative strategies are proposed based on the feedback collected. These include exploring the underlying reasons for using AI translation software beyond simple language difficulties and recognising the capabilities and limitations of MT. The article concludes with implications for institutional policy and EAP instruction.