Navigating ethical collaboration with machine translation: An exploratory study on the role of L2 development in GenAI writing

We explore how language development, and training informed by development, impact L2 students’ navigation of ethical collaboration with machine translation (MT). Our interdisciplinary approach integrated the Writing Studies’ concept of ‘ethical collaboration’ – an ethically guided interface between students and GenAI – with Applied Linguistics, which highlights the need to address GenAI/MT ethics and L2 language. We created a model synthesizing the “Student Guide to AI Literacy” (MLA, 2024) with a theory of L2 development. Data were collected by 1) an established protocol – direct writing in English, self-translation from the L1 and machine translation from the L1, 2) developmentally focused and acknowledgement training, and 3) post-editing of self-translation. An emergence (onset) criterion and frequencies measured written development and evaluation of MT output, frequencies measured monitoring after training and a statistical analysis measured impact of the training types. The analysis of development and evaluation demonstrated that development impacted un/ethical evaluation of MT output. The post-edits indicated that developmentally focused training encouraged ethical monitoring when development permitted and had more impact than acknowledgement training. We conclude that guidelines and training on ethical collaboration with GenAI should be informed by L2 development, not only GenAI literacy, and that research on this topic should continue.