Digital Competence and Learner Engagement in AI ‐Mediated Higher Education: A Mixed‐Methods Study of the Moderating Role of Cognitive Load in EFL Contexts

With the growing integration of artificial intelligence into higher education, digital competence has become central to learners' meaningful engagement in AI‐mediated learning environments. However, its role may be constrained by the cognitive load generated when students navigate complex digital tools, resources, and learning tasks. This study, therefore, adopted an explanatory sequential mixed‐methods design to examine whether digital competence was associated with learners' engagement and whether cognitive load moderated this relationship. Quantitative data were collected from 339 Chinese university EFL learners through a questionnaire measuring digital competence, cognitive load and engagement. Regression analysis showed that cognitive load was negatively associated with engagement and significantly moderated the relationship between digital competence and engagement. In contrast, the direct effect of digital competence became non‐significant after these factors were considered. In the subsequent qualitative phase, interviews with 10 learners were used to explain and extend these findings, showing that digital competence supported engagement through richer resources, personalised learning, autonomy, and interaction, whereas information overload, distraction, AI dependence, unequal access, and privacy concerns constrained meaningful engagement. These findings suggest that digital competence should not be understood as a stand‐alone predictor of engagement, but as a conditional educational resource whose value depends on learners' ability to regulate cognitive load in AI‐mediated learning contexts. The study contributes to current discussions on digital transformation in higher education by highlighting the need for pedagogical support, critical AI literacy and equitable learning conditions that help students engage meaningfully with emerging technologies.