Examining discontinuance of AI-mediated informal digital learning of English (AI-IDLE) among university students: Evidence from SEM and fsQCA

TL;DR

This study investigates the factors influencing university students' discontinuance of AI-mediated informal English learning, revealing that dissatisfaction and frustration, driven by perceived risks and complexity, lead to disengagement.

Contribution

It extends AI-IDLE research by analyzing post-adoption disengagement using SEM and fsQCA, highlighting causal complexity and multiple pathways to discontinuance.

Findings

Dissatisfaction and frustration positively predict discontinuance.

Frustration has a stronger effect than dissatisfaction.

Multiple configurations lead to high discontinuance, indicating causal complexity.

Abstract

This study examined university students' discontinuance intention towards AI-mediated informal digital learning of English (AI-IDLE). Drawing on the cognition-affect-conation framework, the study investigated how three cognitive factors, namely disconfirmation, perceived complexity, and perceived risk, influence two affective responses, namely dissatisfaction and frustration, and how these affective responses predict discontinuance intention. 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. Disconfirmation, perceived complexity, and perceived risk also positively influenced dissatisfaction and frustration. The fsQCA results further identified multiple sufficient configurations leading to high AI-IDLE discontinuance intention, indicating that discontinuance is shaped by causal complexity and equifinality rather than by a single necessary condition. These findings extend AI-IDLE research from adoption and engagement to post-adoption disengagement and provide implications for reducing learners' dissatisfaction, frustration, perceived complexity, and risk in AI-supported informal English learning.