Artificial intelligence (AI) offers new possibilities for English as a foreign language (EFL) learners to enhance their learning outcomes, provided that they have access to AI applications. However, little is written about the factors that influence their intention to use AI in distributed EFL learning contexts. This mixed-methods study, based on the technology acceptance model (TAM), examined the determinants of behavioral intention to use AI among 464 Chinese EFL college learners. As to quantitative data, a structural equation modelling (SEM) approach using IBM SPSS Amos (Version 24) produced some important findings. First, it was revealed that perceived ease of use significantly and positively predicts perceived usefulness and attitude toward AI. Second, attitude toward AI significantly and positively predicts behavioral intention to use AI. However, contrary to the TAM assumptions, perceived usefulness does not significantly predict either attitude toward AI or behavioral intention to use AI. Third, mediation analyses suggest that perceived ease of use has a significant and positive impact on students’ behavioral intention to use AI through their attitude toward AI, rather than through perceived usefulness. As to qualitative data, semi-structured interviews with 15 learners, analyzed by the software MAXQDA 2022, provide a nuanced understanding of the statistical patterns. This study also discusses the theoretical and pedagogical implications and suggests directions for future research.

English-as-a-foreign-language (EFL) learning is essential for Chinese senior high school students, but burnout can impair their engagement and performance. Resilience, however, can enhance their engagement and shield them from burnout. This mixed-methods study aimed to investigate the interplay between burnout, resilience, and engagement among 413 Chinese senior high school EFL learners. Using AMOS 24, quantitative analyses with structural equation modeling (SEM) revealed the reciprocity between these three constructs. Specifically, burnout was negatively associated with both resilience and engagement, explaining 28.6% and 33.3% of their shared variance respectively. Besides, resilience and engagement were positively correlated, sharing 70.6% of their variance. Qualitative semi-structured interviews with 15 students, analyzed by MAXQDA 2022, supported this reciprocity by specifying the role of each component of these constructs. This study also discusses the theoretical and practical implications of findings and suggests directions for future research.

Artificial intelligence and positive psychology play crucial roles in education, yet there is limited research on how these psychological factors influence learners’ use of AI, particularly in language education. Grounded in self-determination theory, this study investigates the factors influencing Chinese English learners’ intention to use AI for language learning. Utilizing structural equation modeling, this research examines the mediating roles of grit, flow, and resilience in the relationship between basic psychological needs and the intention to use AI. Data were analyzed using AMOS 26 and SPSS 26. The findings reveal that flow, grit, and resilience mediate the relationship between basic psychological needs and the intention to adopt AI tools for language learning. This study provides valuable insights into how educational environments can be designed to fulfill psychological needs, thereby fostering greater engagement and acceptance of AI in language education.