Examining discontinuance of AI-mediated informal digital learning of English (AI-IDLE) among university students

This study examined university students’ discontinuance intention towards AI-mediated informal digital learning of English (AI-IDLE). Grounded in the cognition–affect–conation (CAC) framework and informed by appraisal-based accounts of emotion, the study modelled disconfirmation, perceived complexity, and perceived risk as post-adoption cognitive appraisals; dissatisfaction and frustration as distinct affective responses; and discontinuance intention as the conative outcome. A cross-sectional survey was conducted with 746 Chinese university students who had experience using AI tools for informal English learning. Data were analysed using structural equation modelling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA). The SEM results showed that dissatisfaction and frustration positively predicted discontinuance intention, with frustration showing the stronger effect. All three cognitive appraisals positively influenced both affective responses. The fsQCA results identified multiple sufficient configurations leading to high discontinuance intention, demonstrating causal complexity and equifinality. The findings specify an appraisal–emotion–withdrawal mechanism for AI-IDLE while indicating that negative affect is not inherently discontinuance-producing: it is more likely to become inhibitory when learners experience persistent goal obstruction and limited perceived control. The study extends AI-IDLE research from adoption and engagement to post-adoption disengagement and offers implications for designing more manageable, trustworthy, and pedagogically useful AI-supported learning experiences.