Configurating Learner Engagement in AI‐Mediated Informal Digital Learning of English: A Combined Serial Mediation and FsQCA Approach
TL;DR
The results demonstrated that AI‐IDLE exerts a significant direct predictive effect on learner engagement, and fsQCA identifies four equifinal configurations and reveals a vital compensatory mechanism, showing that AI support and high task valuation can effectively offset deficits in learner agency to sustain engagement.
Abstract
Despite the growing prevalence of AI‐mediated informal digital learning of English (AI‐IDLE), the complex configuration of cognitive‐affective factors underpinning learner engagement remains inadequately understood. Grounded in control‐value theory (CVT), this study employed a dual‐analytical approach—integrating structural equation modeling (SEM) and fuzzy‐set qualitative comparative analysis (fsQCA)—to examine learner engagement in AI‐IDLE among 796 Chinese EFL learners. The results demonstrated (1) AI‐IDLE exerts a significant direct predictive effect on learner engagement. (2) This impact is primarily orchestrated through a serial “cognition‐emotion‐behavior” chain, where value appraisals and L2 pride serve as pivotal mediating drivers; notably, the “causal decoupling” of anxiety positions AI‐IDLE as an affective sanctuary that attenuates traditional evaluative pressures. (3) fsQCA identifies four equifinal configurations and reveals a vital compensatory mechanism, showing that AI support and high task valuation can effectively offset deficits in learner agency (low control) to sustain engagement. Implications are discussed regarding how utilizing value‐centered support and mastery‐focused learning can sustain learner engagement in extramural activities.