AI-Assisted Request Pragmatics of Korean Pre-Service Elementary English Teachers

This study examines the pragmatic competence of Korean pre-service elementary English teachers when composing requests with AI translation support. Focusing on formal written and spoken contexts, it examines the types of request head acts (RHAs), internal mitigators, and request supportive moves (RSMs) these future teachers employ. Twenty-two freshmen enrolled in a teacher education program completed a discourse-completion task (DCT) including three scenarios: writing a hotel room-upgrade email, making a room-change request at a hotel front desk, and sending a peer-to-peer activity-change message. Responses were coded (CCSARP-based) for RHA type, syntactic and lexical/phrasal downtoners, and RSMs, including their position relative to the RHA. Results show entitlement-sensitive patterning. In high-entitlement situations (room change), participants favored direct query-preparatory RHAs with minimal support. In lower-entitlement peer interactions, they used off-record strategies and heavier mitigation. Upgrade emails. Despite fluent AI-generated English, the pre-service teachers often exhibited mismatches between context and mitigation: over-verbosity in simple requests and under-mitigation in delicate. The study recommends teacher education on entitlement calibration, proportional mitigation, and pre/post-RHA sequencing to support effective co-teaching and technology-mediated collaboration.