Teaching Ethical Reasoning with AI-Generated Dilemmas: A Module Design for Interpreter Education

While artificial intelligence (AI) has been increasingly integrated into interpreting workflows, pedagogical resources that systematically develop ethical reasoning competence in this context remain scarce. This article addresses preparing interpreter students for ethical reasoning in technology-mediated contexts where AI systems pose novel dilemmas concerning data privacy, transparency, responsibility, and equity, and presents a four-week module design using AI-generated scenarios to develop such competence. Drawing on situated learning and reflective practice, the module combines scenario-based learning, dialogic pedagogy, and critical AI literacy. The design progresses from instructor-led analysis to student-led facilitation, with each two-hour session combining small-group scenario analysis, facilitated discussion and reflection. Large language models (LLMs) generate ethical dilemmas tailored to institutional contexts, while instructors critically curate this content and model systematic evaluation of AI outputs. Assessment comprises participation, applied framework analysis, and reflective essays examining reasoning processes. The article provides implementation guidance including session structures, facilitator strategies, scenario generation principles, and assessment frameworks adaptable to diverse contexts. The design demonstrates how AI, when critically framed, can scaffold ethics education by developing dual competencies increasingly essential for interpreters: technical facility with AI tools alongside critical literacy about their limitations.