Reynolds, Barry; Gao, Yang;  Du, Yunfei; Wang, Xiaochen

Faculty of Education, University of Macau, China ;  School of Foreign Studies, Xi'an Jiaotong University, China

Abstract: While artificial intelligence (AI) has expanded the opportunities for informal digital learning of English (IDLE), our understanding of how learners’ cognitive load, well-being, flow, and engagement interact in IDLE contexts remains limited. The present study explored how cognitive load influenced learner engagement in IDLE through the mediating roles of flow and well-being among Chinese university students. An explanatory sequential mixed-methods design was employed. In the quantitative phase, survey data were collected from 620 participants using validated scales measuring cognitive load, flow, well-being, and engagement in IDLE, and analyzed using structural equation modeling. In the qualitative phase, semi-structured interviews were conducted with 10 students selected through purposeful sampling. The interview data were analyzed thematically using deductive coding to further explain and support the quantitative findings. Results showed that cognitive load negatively predicted flow and well-being, which in turn positively predicted IDLE engagement; both flow and well-being significantly mediated the relationship between cognitive load and engagement. Qualitative findings illustrated these mechanisms in learners’ experiences, highlighting perceptions of cognitive challenges and emotional responses in AI-supported IDLE contexts. The study extends Cognitive Load Theory to AI-mediated IDLE and offers pedagogical implications for enhancing engagement in IDLE.

Key words: AI-mediated informal digital learning (AI-IDLE),  Cognitive Load Theorym, flow, well-being

Note: This is a pre-proof version and is subject to change during the editing process.