Neither Luddite nor enthusiast: interpreting teachers’ AI use in teaching

Abstract

This study investigates how Chinese English interpreting teachers in China position themselves in relation to the growing presence of artificial intelligence (AI) in interpreter training. While existing research has largely focused on student attitudes, tools, and curriculum-level discussions, the perspectives of interpreting educators remain less visible, despite their role in shaping pedagogical practice and professional norms. Drawing on a survey of 156 interpreting teachers in Master of Translation and Interpreting (MTI) programs, the study examines multiple dimensions of AI orientation, including readiness (enabling conditions and AI evaluative literacy), reported use of AI-enabled tools, critical evaluation practice, perceived usefulness and future intention, profession-related concerns (threat to professional autonomy and perceived labor devaluation), and restrictive responses (restriction behavior frequency, human judgment/skill protection, and restrictive orientation toward AI integration). Descriptive results indicate above-midpoint levels of readiness and perceived usefulness/future intention, alongside moderate reported use with substantial variability. Correlational analyses show that enabling conditions are positively associated with tool use and with perceived usefulness/future intention, whereas AI evaluative literacy shows a small negative association with tool use and perceived usefulness, and no association with future intention. Reported use is strongly associated with perceived usefulness and positively associated with future intention. Profession-related concerns are also evident: restrictive orientation is strongly associated with autonomy threat and positively associated with perceived labor devaluation, and autonomy threat and labor devaluation are positively related. In a regression model including role and teaching experience, autonomy threat and labor devaluation do not show unique effects on restrictive orientation, while teaching experience is strongly associated with restrictiveness. The findings suggest that interpreting teachers’ engagement with AI is not captured by adoption-related constructs alone. Alongside perceived pedagogical value, profession-related concerns and experience-linked boundary-setting shape how AI is positioned in interpreter training. The study extends technology acceptance approaches by incorporating constructs related to autonomy and labor value, and it highlights the need for AI integration strategies that address pedagogical use as well as professional implications.