Qinqing Zhang

In the context of AI‐empowered language education, informal digital learning of English has emerged as a key site for fostering young learners ' positive psycho‐emotional development. However, limited attention has been paid to how family factors shape children ' s willingness to communicate with AI. This study investigated how parental investment influences upper‐primary students ' willingness to communicate with AI in an AI‐enhanced informal digital learning of English context, focusing on the mediating roles of AI learning self‐efficacy and learning autonomy. Grounded in Social Cognitive Theory and Self‐Determination Theory, the study surveyed 659 upper‐primary students from eight primary schools. Results revealed a serial mediation pathway: parental investment → self‐efficacy → autonomy → willingness to communicate with AI. These findings extend parental investment research into AI‐enhanced informal digital learning of English contexts and integrate two major motivational frameworks into a unified model of child L2 psycho‐emotional functioning.

Generative Artificial Intelligence (GenAI) offers robust information generation, swift responsiveness and high personalization for education. However, it remains unclear how these specific technological features translate into improved learning engagement through the mediation of learners' psychological states. Grounded in the Stimulus‐Organism‐Response (S‐O‐R) theory, this study investigates the mediating roles of AI trust and AI learning self‐efficacy in the relationships between GenAI features (responsiveness and personalization) and the learning engagement of English as a Foreign Language (EFL) learners. This study surveyed 697 undergraduate students from four universities in China, all of whom had used GenAI to assist their EFL learning. Using structural equation modelling, the results revealed that: (1) Both responsiveness and personalization significantly enhanced learners' AI trust and self‐efficacy; (2) These psychological states, in turn, positively influenced learning engagement; (3) A critical divergence was observed: responsiveness influenced engagement only indirectly through the full mediation of trust and self‐efficacy, whereas personalization exerted both a direct effect and indirect effects through the same mediators. This study elucidates the distinct psychological pathways through which different AI features affect engagement, providing a more nuanced theoretical account. The findings underscore that for AI tools to foster deep engagement, they must be designed not only to be responsive and personalised but also to proactively build learners' trust and confidence in their AI‐use capabilities.

The increasing incorporation of artificial intelligence (AI) in English as a foreign language (EFL) instruction has garnered much attention on the importance of technological elements in language instruction. However, while AI in education (AIED) is still in its early development, research on how learners’ AI literacy affects their language learning outcomes is insufficient. Furthermore, studies examining the impact of learners’ emotional states within the context of AIED are remarkably few. This study examines the interplay between AI literacy and EFL learners’ willingness to communicate (WTC), emphasizing the mediating roles of learners’ AI learning self-efficacy and foreign language classroom anxiety. This study utilizes structural equation modeling, analyzing data from 517 university students in China to construct a prediction model for WTC in AI-enhanced EFL contexts. The findings indicate that AI literacy improves self-efficacy in AI learning and diminishes classroom anxiety, both of which are significant mediators in the relationship between AI literacy and willingness to communicate. The study highlights the imperative of integrating AI literacy into EFL instruction to enhance learners’ expressive confidence and mitigate fear. The findings improve understanding of the interplay between AI literacy, psychological factors, and language learning outcomes, offering practical insights for the integration of AI in EFL education.

The academic study of work engagement among pre-service teachers during their practicum has been notably sparse, with even fewer investigations examining the influence of environmental stimuli on their engagement levels and the role of individual psychological and emotional factors throughout the practicum. To address this research gap, the present study, informed by the Stimulus–Organism–Response (SOR) theory, has developed a structural equation model. This model posits teacher support and peer support as stimuli, L2 grit and the foreign language teaching enjoyment as the organism, and the work engagement of pre-service teachers as the behavioral response. A quantitative survey was conducted among 516 preservice EFL teachers to examine the relationships among variables in the model. Findings showed direct predictions of work engagement by teacher support, L2 grit, and FLTE. L2 grit and FLTE sequentially mediated the relationship between teacher and peer support and work engagement. This study identified the factors influencing preservice EFL teachers’ work engagement, contributing to a deeper understanding of their psychological characteristics and emotional experiences during the teaching practicum. Additionally, the study offers practical implications for universities and internship schools to enhance preservice teachers’ work engagement during the practicum.