Disentangling Emotional Intelligence in AI‑Mediated Informal Digital Learning of English Among Under‑Resourced EFL Learners: A Latent Profile Approach

With the deep integration of artificial intelligence (AI) and second language (L2) education, learners’ psychological states are increasingly influenced by a combination of internal and external factors, such as emotional intelligence (EI) and AI-mediated informal digital learning of English (AI-IDLE). However, existing research on EI has largely been confined to formal classroom settings. Moreover, studies on AI-IDLE have predominantly focused on resource-rich regions, with little attention paid to extracurricular English learning in under-resourced educational contexts. To address the aforementioned research gaps, this study employed Latent Profile Analysis (LPA) guided by Control-Value Theory (CVT) to identify heterogeneity in EFL learners’ EI. It also examined how different EI profiles relate to AI-IDLE. The data were collected from 565 EFL learners in under-resourced regions. A three-profile solution was supported by model-fit indices, and the profiles were labeled Balanced Regulator (43%), Restricted Regulator (36%), and Optimal Regulator (21%). Significant differences in AI-IDLE were observed across the three profiles in subsequent ANOVA (p < .001). Three latent categories of EI were identified, and their differentiated patterns in AI-IDLE were documented, highlighting the critical role of EI in AI-IDLE. This study is innovative in that it situates the research in resource-scarce regions and integrates EI and AI-IDLE into a single framework, thereby uncovering the mechanisms through which learners’ emotional abilities shape their engagement with AI in this specific context. This study provides empirical evidence for emotional interventions in L2 education, highlighting that enhancing learners’ AI literacy can promote their psychological well-being and academic development.