ABSTRACT In the language learning domain, learner psychological well‐being has emerged as a critical outcome influenced by different psychological resources. Given that artificial intelligence (AI) has entered language learning at a fast pace, the psychological impact of AI on learners is now more pertinent. Both personality traits and psychological capital have been individually linked to learners' success and well‐being, yet their combined influence in technology‐based language learning contexts remains underexplored. This study investigates the predictive roles of personality traits and psychological capital in predicting the well‐being of English as a Foreign Language (EFL) learners in AI‐based environments. Adopting Seligman's PERMA theory, the study conceptualises personality traits (PTs) as stable internal resources and psychological capital (PsyCap) as a state element. A total of 373 EFL university students participated, and they completed the three questionnaires. The Structural equation modelling (SEM) demonstrated that PsyCap ( β = 0.713) and PTs ( β = 0.642) were both significant predictors of well‐being among learners, with PsyCap being the stronger predictor. The study contributes to Positive Psychology (PP) in language learning by establishing a theoretically integrated model of well‐being that bridges stable PTs and changeable psychological strengths.
Zhen Guo (Thu,) studied this question.