Why Learners Drift In and Out: Examining Intermittent Discontinuance in AI-Mediated Informal Digital English Learning (AI-IDLE) Using SEM and fsQCA
For researchers and designers of AI-supported language learning, this work extends understanding from adoption and continuance to post-adoption discontinuance, highlighting the need for enjoyable, personalized, and controllable experiences.
This study examined why learners intermittently discontinue AI-mediated informal digital learning of English (AI-IDLE), finding that perceived intelligence, interactivity, and personalization reduce discontinuance through enjoyment, while perceived ineffectiveness, uncontrollability, and complexity increase it through boredom. Four configurational pathways leading to discontinuance were identified.
This study examined intermittent discontinuance in AI-mediated informal digital learning of English (AI-IDLE) through the cognition-affect-conation framework. Survey data were collected from 632 Chinese university EFL learners with prior AI-IDLE experience and analysed using structural equation modelling and fuzzy-set qualitative comparative analysis. The SEM results showed that perceived intelligence, perceived interactivity, and perceived personalisation reduced AI-IDLE intermittent discontinuance indirectly through enjoyment, whereas perceived ineffectiveness, perceived uncontrollability, and perceived complexity increased discontinuance indirectly through boredom. The fsQCA results further identified four configurational pathways leading to intermittent discontinuance, indicating that learners' temporary withdrawal from AI-IDLE can result from different combinations of cognitive barriers and affective disengagement. These findings extend AI-IDLE research from adoption and continuance to post-adoption discontinuance and highlight the need to design AI-supported English learning experiences that are enjoyable, personalised, controllable, and cognitively manageable.