EFL Learners' Well‐Being Affected by Their Self‐Efficacy and Resilience in AI‐Integrated Learning Environment

ABSTRACT With the advent of Artificial Intelligence (AI) in the academic context, the traditional learning settings have been changed to advanced instruments that boost the learners' achievement. A critical factor of learners' achievement in AI‐based settings is Psychological Well‐being (PWB). Nevertheless, detecting the effect of mental factors such as resilience and self‐efficacy on PWB in these settings is a recent issue in teaching because enhancing these constructs is critical for both educational success and PWB in digital EFL settings. Through Self‐determination theory (SDT), this study seeks to examine the interplay among PWB, self‐efficacy and resilience. Through purposive sampling, 317 EFL learners from Chinese universities who actively used AI tools in their language courses were selected, and after data screening, 292 valid responses were retained for analysis. Data were collected through three validated instruments: Ryff and Singer's Psychological Well‐Being Scale, Pintrich and De Groot's Self‐efficacy Scale and Cassidy's Academic Resilience Scale. The scales were distributed, and following data collection, structural equation modelling (SEM) was employed for model determination. Both self‐efficacy and resilience were identified as significant predictors of PWB. Self‐efficacy emerged as the stronger predictor, uniquely accounting for 21.81% of the variance in PWB scores, while resilience explained 14.52% of the variance independently. Based on the results, the two mental concepts have a crucial and statistically significant effect on how meaningfully and actively learners take part in AI‐based settings. The findings contribute to the broader view of SDT's utilization in AI‐based settings, paving the way for subsequent studies on motivational aspects in such settings.