Generative <scp>AI</scp> in work‐integrated learning: Supporting pre‐service teachers' emotional labour and self‐management in Australian initial teacher education

Abstract This study investigates how generative artificial intelligence (gen‐AI) interacts with pre‐service teachers' (PSTs) experiences of emotional labour, relational complexity and institutional navigation during work‐integrated learning (WIL) placements. Using the Emotional Capital in Education (ECE) framework, the study explores how emotional capital is preserved, converted and depleted across diverse affective economies, and how gen‐AI activities were taken up as culturally responsive scaffolds in this process. Drawing on survey and focus group data from 126 PSTs in an interventional initial teacher education (ITE) case study, the findings show that gen‐AI activities were associated with rehearsal of emotional self‐regulation, pedagogical preparation and professional confidence, particularly in emotionally ambiguous or culturally misaligned placements. These benefits, however, were uneven and shaped by institutional recognition, mentor attitudes and cultural legibility. The study contributes by showing how tailored, scaffolded use of gen‐AI can help PSTs build emotional capital for placement in ways that align with disciplinary expectations and diverse cultural‐emotional needs, pointing to future designs that foreground emotional as well as cognitive scaffolding in ITE. Practitioner notes What is already known about this topic Gen‐AI has demonstrated potential to enhance efficiency and productivity in education settings. PSTs experience high levels of emotional labour, identity stress and institutional pressure during WIL. Emotional misrecognition and affective mismatch are common among culturally diverse PSTs. What this paper adds Shows how gen‐AI can scaffold emotional regulation, relational confidence and cultural navigation for PSTs during WIL. Demonstrates how emotional capital is differentially recognised or blocked depending on the affective economy of the placement. Introduces gen‐AI as a culturally responsive scaffold that helps PSTs simulate feedback, rehearse responses and reduce affective isolation. Emphasises the role of AI literacy not just as a technical skill, but as an affective and ethical practice that shapes professional identity. Implications for practice and/or policy ITE programmes should embed emotionally intelligent, ethically attuned AI literacy training that addresses cultural norms of affect and professionalism. Mentor teacher development should include training on emotional capital and cultural‐affective misrecognition to better support diverse PSTs. Institutional policies should align university and school affective expectations, recognising gen‐AI not only as a tool but as a mediator of emotional legitimacy in WIL.