Why Learners Drift In and Out: Examining Intermittent Discontinuance in AI-Mediated Informal Digital English Learning (AI-IDLE) Using SEM and fsQCA
Yiran Du, Huimin He

TL;DR
This study investigates why learners intermittently discontinue AI-mediated informal digital English learning, identifying cognitive and affective factors influencing withdrawal through SEM and fsQCA analysis.
Contribution
It extends AI-IDLE research to include post-adoption discontinuance, revealing multiple pathways involving cognitive and emotional factors affecting learner retention.
Findings
Perceived intelligence, interactivity, and personalisation reduce discontinuance via enjoyment.
Perceived ineffectiveness, uncontrollability, and complexity increase discontinuance via boredom.
Four configurational pathways lead to intermittent discontinuance based on cognitive and affective factors.
Abstract
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…
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