Applying neural machine translation and ChatGPT in the teaching of business English writing

Abstract As language teaching becomes more complex and diverse, there has been a rapid increase in the demand for advanced technology, driving the widespread adoption of neural machine translation (NMT) and ChatGPT in the field. This study contributes to the literature on the use of technology in language teaching by evaluating the application of NMT technology and ChatGPT in teaching English as a foreign language (EFL) writing in three business fields: finance, economics, and business administration. By building six comparable corpora consisting of students’ direct-writing and post-edited writing based on machine-translated texts, we examined whether NMT can help improve students’ performance in business English writing classes, and whether ChatGPT can complement NMT. Our statistical analyses show that in general, NMT can enhance the proficiency of students’ academic writing, but its improvement effect works on different dimensions for those students studying in different majors. Specifically, for finance students, NMT can improve their academic writing at the word and syntax levels and mechanics, while it harms the organizational dimension. For students in economics, the improvement effect of NMT mainly focuses on enhancing the dimensions of syntax, cohesion, and mechanics, whereas for students in business administration, NMT works primarily on the dimensions of content, cohesion, and mechanics. As for the dimensions where NMT performs poorly, our analysis of students’ essay writing shows that ChatGPT can complement NMT by making improvements and providing feedback to students. Our paper adds value to existing research on the use of technology in language teaching by investigating the application of NMT and ChatGPT in teaching EFL writing, and by proposing potential directions for their use in the teaching of writing business English.