The positive impact of Artificial Intelligence (AI) on second language (L2) learning is well-documented. An individual's attitude toward AI significantly influences its adoption. Despite this, no specific scale has been designed to measure this attitude, particularly in the Chinese context. To address this gap, our study aims to construct the AI-Assisted L2 Learning Attitude Scale for Chinese College Students (AL2AS-CCS) and evaluate its reliability, validity, and relationship with L2 proficiency. Our research comprises two phases, each involving separate samples. In Phase One (Sample 1: n = 379), we conducted exploratory factor analysis (EFA) to determine the factor structure of the AL2AS-CCS. The resulting two-factor structure consists of 12 items, categorized into cognitive and behavioral components. In Phase Two (Sample 2: n = 429), we performed confirmatory factor analysis (CFA) to validate the factor structure and assess model fit. CFA in Sample 2 confirmed the factor structure and demonstrated a good model fit. Additionally, the AL2AS-CCS exhibited high criterion validity, internal consistency, and cross-gender invariance. Our findings suggest that the AL2AS-CCS is a valid measurement tool for assessing Chinese college students' attitude toward AI-assisted L2 learning. Moreover, Chinese college students were discovered to maintain a moderately positive attitude toward AI-assisted L2 learning. Additionally, a positive correlation was identified between this attitude and their L2 proficiency.

Burnout impairs English as a foreign language (EFL) learning, while engagement enhances it. However, most relevant studies have focused on college students, neglecting senior high school students. To address this gap, this mixed-methods study used two scales to assess the levels of burnout and engagement among 1234 Chinese senior high school EFL students. We also applied a person-centered approach with a statistical software (Mplus 8.7) to identify their latent profiles. Then, we conducted semi-structured interviews with 30 students and analyzed the data using thematic analysis with a qualitative software (MAXQDA 2022) to investigate the antecedents of their burnout and engagement. The statistical analysis revealed three profiles: high burnout-low engagement (16.0 %), moderate burnout-moderate engagement (61.8 %), and low burnout-high engagement (22.2 %). The profiles were associated with demographic characteristics such as gender, age, and grade. The thematic analysis identified four major antecedents: high academic stress and low academic support for burnout, and high external support and high internal support for engagement. Each major antecedent consisted of several categories and subcategories. This study could inform the design of effective interventions to reduce Chinese EFL students' burnout and increase their engagement.

Second language (L2) teachers’ emotions can influence their well-being and students’ performance. However, most of the existing studies have focused on the role of individual factors in affecting L2 teachers’ emotions, while leaving environmental factors underexplored. To fill this gap, this study aimed to examine how the four dimensions of a supportive work environment (SWE) (perceived climate, PC; supervisory relationship, SR; peer group interaction, PGI; and perceived organization support, POS) relate to L2 teachers’ emotions (enjoyment, anxiety, pride, and anger). A sample of 406 Chinese L2 teachers completed two valid scales to measure their SWE and emotions. The data were analyzed by Partial Least Squares-Structural Equation Modeling (SEM) using Smart PLS 3 software. The results showed that (1) PC, PGI, and POS had a positive and significant effect on enjoyment, while SR had no significant effect; (2) PGI and POS had a negative and significant effect on anxiety, while PC and SR had no significant effect; (3) PGI had a positive and significant effect on pride, while the other three dimensions had no significant effect; and (4) POS had a negative and significant effect on anger, while the other three dimensions had no significant effect. The study concludes with some implications for L2 teachers’ education.

Generative artificial intelligence (GenAI) has been rapidly integrated into higher education, yet current studies largely rely on quantitative frameworks that capture initial adoption, leaving university EFL teachers’ motivational evolution for continued use of GenAI underexplored. To address this gap, this longitudinal qualitative study explores the motivational evolution and psychological need experience of 15 university EFL teachers’continued use of GenAI grounded in Self-Determination Theory (SDT). Data were collected through two rounds of semi-structured interviews over one year and analyzed in NVivo 11 PLUS. Our findings reveal a dynamic evolution of motivation from external regulation to value identification and professional identity integration. Crucially, need satisfaction and need frustration provide important psychological foundations for teachers’ motivation internalization. Notably, while need satisfaction directly facilitates university EFL teachers’ continued use of GenAI, need frustration can actually act as a potential catalyst that facilitates EFL teachers to form long-term continued use of GenAI through iterative reflection and instructional adjustment. This study extends the application of SDT within the GenAI education context, indicating that university EFL teachers’ continued use of GenAI is not merely a process of technology adoption, but also a process of professional growth and professional identity integration. The results provided preliminary implications for teacher professional development, GenAI design and education policy.