Hanwei Wu

As generative artificial intelligence (GenAI) becomes increasingly integrated into second language (L2) learning, there is a growing need for reliable instruments to assess learners’ cognitive evaluation of GenAI-mediated interactive learning experiences. This study developed and validated the GenAI L2 Interactive Learning Experience Scale (AI-L2-ILES) among 1130 Chinese university students. Drawing on an interactive experience perspective and adapting it to L2 learning, the study conceptualized learners’ cognitive evaluation of GenAI interaction as comprising five dimensions: Effectiveness, Sociability, Novelty, Flow, and Seamlessness. Exploratory and confirmatory factor analyses supported this five-factor structure, with satisfactory evidence of reliability, validity, and measurement invariance across gender and academic disciplines. Network analysis further revealed that Flow was the most central dimension, Sociability showed the strongest association with Effectiveness, and Novelty occupied a relatively peripheral position. The network structure was stable across academic disciplines. Overall, the AI-L2-ILES provides a psychometrically sound measure of learners’ cognitive evaluation of GenAI-mediated interactive L2 learning experiences and captures five distinct but interconnected dimensions of this emerging construct. The scale offers a useful tool for examining how L2 learners cognitively evaluate their interactions with GenAI.

English proficiency is vital for non-native speakers’ career development, yet classroom instruction alone cannot meet practical demands, making informal digital learning of English (IDLE) increasingly important. Artificial intelligence (AI), with conversational and multimodal functions, offers new opportunities for IDLE. However, existing research on AI-mediated IDLE has predominantly focused on language majors and often relied on a single methodological lens, neglecting STEM undergraduates and the complex network dynamics among motivational factors. However, research has largely focused on language majors, leaving STEM majors underexplored. Guided by the Hedonic-Motivation System Adoption Model (HMSAM), this study analyzed data from 413 Chinese STEM majors using partial least squares structural equation modeling (PLS-SEM, SmartPLS 4.0) and psychological network analysis (PNA, R 4.5.3). PLS-SEM results showed that enjoyment was the strongest direct predictor of AI-IDLE, followed by focused immersion, perceived usefulness, and curiosity. Control contributed indirectly via focused immersion, while boredom was non-significant. Perceived ease of use influenced AI-IDLE only through cognitive and emotional pathways. The model explained 58.1% of the variance. PNA further identified enjoyment, focused immersion, and control as central nodes, while the link between perceived usefulness and AI-IDLE was non-significant. These findings suggest that Chinese STEM undergraduates’ AI-IDLE is primarily driven by intrinsic hedonic motivations rather than utilitarian evaluations. The study provides empirical support for designing AI tools that enhance enjoyment and control to foster STEM students’ extracurricular English engagement.

Willingness to communicate (WTC) with generative artificial intelligence (GenAI) voice chatbots is pivotal for enhancing second language (L2) speaking skills. Guided by MacIntyre et al.’s pyramid model, we investigated how dark personality traits (Machiavellianism, Narcissism, Psychopathy) and regulatory focus (promotion vs. prevention) shape L2 learners’ WTC with GenAI. Data were collected from 2,671 college students in China and analyzed using structural equation modeling (SEM) in AMOS 24 and fuzzy-set qualitative comparative analysis (fsQCA) in fsQCA 4.1. SEM revealed that Machiavellianism and Narcissism positively predicted WTC both directly and indirectly via promotion focus, with direct effects stronger than indirect ones, while Psychopathy exerted a direct negative effect. Prevention focus showed no significant independent influence. fsQCA identified five configurations linked to high WTC: Pragmatic Opportunist, Strategic Self-presenter, Cautious Achiever, Equipped Achievement Seeker, and Positive Performer. Across these, Machiavellianism and promotion focus consistently emerged as core drivers, with prevention focus interacting with promotion focus in certain contexts to strengthen WTC. Our findings elucidate the complex interplay between personality and motivation in GenAI-empowered settings. They underscore the need for personalized, motivation-sensitive instructional designs that leverage learners’ psychological profiles to foster effective L2 communication.

Although generative artificial intelligence (GenAI) literacy models have recently emerged in the field of second language (L2) teaching, most of them adopt a top‐down approach by modifying existing frameworks, such as digital literacy models. As a result, these models may not fully capture the contextually grounded competencies needed for effective L2 teaching. Furthermore, many of these models are designed for in‐service L2 teachers, leaving pre‐service teachers largely unaddressed. To fill this gap, this study utilised the Behavioural Event Interview (BEI) to analyse the GenAI literacy of 20 pre‐service L2 teachers from universities in China, who were divided into two groups: 10 high‐performing and 10 average‐performing participants. Using MAXQDA 2022 for data analysis, 18 key GenAI literacy attributes were identified. An independent t ‐test conducted with SPSS 26 revealed that nine of these attributes were discriminative (i.e., Agency , Developmental Motivation , Resilience , Prompt Design , Multiple‐tool Integration , Classroom Interaction , Personalised Teaching , Reflective Practice , and Collaborative Innovation ), distinguishing high performers from average ones. The remaining attributes were categorised as benchmark competencies (i.e., Value Cognition , Characteristic Cognition , Basic Operations , Resource Preparation , Interdisciplinary Goal Integration , Content Evaluation , Homework Grading , Ethics , and Emotional Care ), which are essential for all pre‐service L2 teachers. Based on McClelland's Iceberg Model, these attributes were further organised into four dimensions: Knowledge , Skills , Traits , and Motives . Finally, we discuss the implications of our model and provides directions for future research in GenAI literacy for pre‐service L2 teachers.

GenAI could either exacerbate or alleviate inequalities in under‐resourced K12 EFL education, depending largely on teachers’ ability to utilize it. However, these teachers remain understudied. To address this digital divide, this mixed‐method study, grounded in Bandura's Social Cognitive Theory, aims to provide a comprehensive understanding of these teachers’ GenAI integration by exploring their GenAI literacy, perspectives, and barriers. Latent profile analysis of quantitative data ( n = 514) in Mplus 8.7 identified three distinct profiles: lagging (14.1%), moderate (70.6%), and advanced (16.3%). Nominal logistic regression showed no significant influence of gender, age, teaching experience, or educational background on profile membership. However, teachers from primary schools, townships, and those with senior titles were more likely to belong to the lagging group. Thematic analysis of qualitative data from open‐ended questionnaires ( n = 313) in MAXQDA 2022 revealed several key insights. 21.4% of teachers had never used GenAI. The remaining teachers identified eight key benefits (e.g., creating immersive situations) and risks (e.g., ethical concerns). Four major barriers were also highlighted (e.g., insufficient training and guidelines, lack of resources). Based on these findings, we call for immediate support to ensure educational equity for these teachers.

Objective: International students frequently face psychological adaptation difficulties while studying and living abroad. As an effective psychological resource, positive solitude has been identified as a potential factor for improving psychological well-being, but the underlying mechanism linking the two has not been fully explored. The current study aims to explore the relationship between positive solitude and psychological adaptation of international students, with particular emphasis on the intermediary roles of authenticity and loneliness. Methods: A total of 529 international tertiary students (Mage = 23.76, SD = 5.08; 60.68% male) were surveyed using the Positive Solitude Scale (PSS), Authenticity Scale (AS), 6-item UCLA Loneliness Scale (ULS-6), and Brief Psychological Adaptation Scale (BPAS). SPSS27.0 was used for descriptive statistical analysis and Pearson correlation analysis. PROCESS macro (Model 6) was employed to test a serial mediation model, in which authenticity and loneliness function as intermediary variables between positive solitude and psychological adaptation. Results: The correlation analysis indicated significant associations among positive solitude, authenticity, loneliness, and psychological adaptation (r = −0.544~0.511). Positive solitude was directly and positively related to psychological adaptation (β = 0.132, t = 3.609, p < 0.001) and indirectly related to psychological adaptation through two pathways: a single mediation via authenticity (indirect effect = 0.089) and a serial mediation through authenticity and loneliness (indirect effect = 0.062). Loneliness did not serve as a significant mediator (indirect effect = –0.015, 95% CI [–0.049, 0.019]). The total indirect effect was 0.136. Conclusions: Interventions targeting international students’ capacity for experiencing positive solitude and authenticity can help to reduce loneliness and increase psychological adaptation. The findings derived from this study are conducive to understanding the relationship between positive solitude and psychological adaptation, as well as its underlying mechanisms. In addition, the study offers a new perspective for educational management and psychological counseling services for international students.