Nursing and midwifery students’ ethical views on the acceptability of using AI machine translation software to write university assignments: A deficit-oriented or translanguaging perspective?

This paper focuses on tertiary English as an additional language (EAL) students' ethical choices, and the factors impacting on them, when deciding whether to engage with artificially-intelligent (AI) machine translation (MT) tools for the writing of university assignments. It also investigates how student responses align with either deficit-oriented or translanguaging theoretical perspectives. Via semi-structured interviews, the voices of 23 EAL nursing and midwifery students indicate an array of ethical positions which are based on three key areas of consideration: 1) ownership of language and ideas; 2) fairness and respect; and 3) personal growth. The study highlights the scalar, strategic and dynamic nature of students’ ethical decisions and shows that questions of ethicality tap into individual, social and institutional constructs of fairness and respect, skills recognition, lifelong learning and language dominion. The findings also indicate that discussions of fairness should focus not only on differences between non-EAL and EAL students, but also inequalities within EAL cohorts. Student responses provide evidence of both deficit-oriented and translanguaging perspectives. The researchers call for universities to create clear policies concerning use of MT that recognise the levels of reflection that students engage in when writing their assignments and value the full linguistic repertoires that students bring to global educational settings. •EAL students do not blindly use artificially-intelligent machine translation.•Discussions of ethical use of machine translation must be expanded beyond academic integrity.•Student perspectives on using machine translation reflect both deficit-oriented and translanguaging discourses.•University policies regarding machine translation should adopt a translanguaging approach to promote fairness.