Affective and cognitive resources for engagement in AI-assisted language learning: A self-determination theory perspective integrating SEM and random forest

Artificial intelligence (AI) is increasingly reshaping language learning in higher education, yet students’ engagement in AI-assisted language learning depends on more than access to technological tools. Drawing on Self-Determination Theory (SDT) as an overarching theoretical perspective, this study examined how emotional intelligence and metacognition were associated with engagement directly and indirectly through enjoyment and self-efficacy, while also evaluating their predictive value. Survey data from 847 university students in China were analyzed using structural equation modeling and random forest analysis with repeated nested cross-validation. The SEM results showed that emotional intelligence and metacognition were positively associated with engagement, enjoyment, and self-efficacy, while enjoyment and self-efficacy were also positively related to engagement. All four indirect effects were significant. The extended random forest model achieved an out-of-sample R² of .472, improving R² by .081 over the baseline random forest and by .069 over the extended linear model. Emotional intelligence showed the highest permutation importance and revealed potentially nonlinear predictive patterns across individual learners. These findings highlight the complementary value of explanatory and predictive approaches for understanding engagement in AI-assisted language learning.