AI‐assisted learning environments in China: Exploring the intersections of emotion regulation strategies, grit tendencies, self‐compassion, L2 learning experiences and academic demotivation

Abstract The increasing integration of artificial intelligence (AI) in education has led to a surge of interest in AI‐assisted learning environments. These environments offer various advantages, yet a deeper understanding of their effects on key student‐related constructs in the English as a foreign language (EFL) context is essential. This study aimed to fill this gap by investigating the relationships between emotion regulation strategies, grit, self‐compassion, L2 learning experiences and academic demotivation among Chinese EFL learners in AI‐supported settings. A quantitative research design was employed, with 219 EFL students participating through purposive sampling. Data were collected using validated questionnaires measuring the five target constructs and analysed using structural equation modelling. Results revealed that emotion regulation strategies were positively associated with L2 learning experiences and negatively associated with academic demotivation. Similarly, grit tendencies demonstrated positive correlations with L2 learning experiences and negative correlations with academic demotivation. Self‐compassion demonstrated similar patterns, with positive relationships to L2 learning experiences and negative associations with academic demotivation. These findings offer important pedagogical implications for EFL educators and developers of AI‐powered learning platforms in China. By understanding the influence of emotion regulation, grit and self‐compassion on learners' experiences and motivation, educators can implement strategies to foster these positive attributes.