AI-mediated teaching of oral interpreting: professional judgment, teacher agency, and human oversight

Artificial intelligence (AI) is increasingly used in the teaching of oral interpreting, where university students practise spoken-language mediation across languages, such as listening to a source-language speech and orally rendering it into another language. This qualitative study examines how twelve higher education teachers engaged with large language models, automated speech recognition, and machine translation in oral interpreting courses. Semi-structured interviews were analysed using reflexive thematic analysis. Findings suggest that participants positioned AI as a pedagogical resource rather than a substitute for professional expertise. Teacher agency was enacted through boundary-setting, instructional redesign, and human oversight, while professional judgment centred on evaluating, contextualising, and regulating AI-generated outputs. The study argues that AI-mediated oral interpreting education reconfigures, rather than replaces, professional practice. It recommends teacher development and institutional guidance that support critical evaluation, calibrated reliance, ethical boundary-setting, and human oversight.