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Despite the growing attention to non-cognitive factors and their effects explored in studies inspired by Positive Psychology, there remains a scarcity of research examining how these factors impact young heritage learners’ language development in Chinese as a Heritage Language (CHL) learning, weakening the generalization of prior findings. Given the importance of speaking ability in developing communication skills and fostering language proficiency, this study investigates the interplay effects of L2 grit, motivational intensity, and willingness to communicate (WTC) on the L2 speaking performance among young CHL learners (N = 383), employing structural equation modeling (SEM). The findings indicate that 1) L2 grit, which encompasses perseverance of effort (PE) and consistency of interest (CI), significantly and positively predicts motivational intensity; 2) PE and WTC are direct predictors of speaking performance, whereas CI is not a direct predictor; 3) CI indirectly predicts speaking performance through the mediation of WTC; and 4) both PE and CI can positively predict speaking performance through the serial mediation of motivational intensity and WTC. The research concludes by presenting its implications for CHL educators and instructors in designing tailored approaches to enhancing young learners’ speaking skills within the framework of heritage language and beyond.

Despite the prevalence of English private tutoring (EPT) in EFL contexts, scant literature has focused on its tutors’ career experiences. This narrative study, underpinned by social cognitive career theory (SCCT), inquiries into the career trajectories of three EFL tutors in Mainland China. Data were mainly collected through three rounds of narrative interviews. Tutors’ photos representing significant career moments supplemented the interview data. Based on their narratives, these participants experienced ‘zigzag’ career trajectories that featured frequent turnovers, lengthy work gaps, and identity shifts from institutional tutors to start-up owners. Informed by SCCT, this study unveils that tutors’ career trajectories were shaped by their self-efficacy beliefs associated with personal attributes and prior experiences. Tutors’ trajectories were also shaped by their outcome expectations from material and social perspectives, and contextual influences, including personal opportunities and barriers, institutional management, and socio-political factors like the ongoing ‘Double Reduction’ policy. These findings are discussed in order to inform other tutors to visualize and organize their career development within the context of policy constraints and, furthermore, offer implications for tutorial institutions to retain tutors for instructional consistency. This inquiry also demonstrates the potential to apply the SCCT model to understanding teachers’ career development in the ‘shadow education’ context.

This study employed eye-tracking and mouse click frequency analysis to investigate the predictive power of gaze behaviors, mouse-clicking, and their interactive effects with linguistic backgrounds on the IELTS (International English Language Testing System) listening test scores. A total of 77 test takers (45 with English as their first language (E-L1) and 32 with English as their second language (E-L2)) participated in this study. Their eye movements and mouse click frequencies were recorded as they took a computer-based IELTS listening test. The subsequent data analysis, utilizing linear mixed models, showed that gaze patterns, mouse actions, and language background significantly predicted listening test outcomes across four listening test sections and between E-L1 and E-L2 candidates, accounting for 33.2% of the variance observed in test scores. These results indicate the effect of potential sources of construct-irrelevant variance on test scores, which are not predicted in the available construct definitions of the test used in the study. Implications for the listening construct and test validity are discussed.

Since self-regulated learning (SRL) has been integrated into educational objectives, it is crucial for EFL teachers to incorporate SRL elements, such as feedback, into their instructional practices. Given the inherent complexity of the feedback process and the challenges associated with changing in-service teachers' feedback focus, developing preservice teachers’ literacy at the beginning of their professional careers is essential. To investigate the extent to which preservice teachers can generate multifocus feedback incorporating an SRL element, this study analyses the feedback categories generated by 286 preservice teachers and examines the underlying factors that influence their feedback choices using a mixed-methods approach. The feedback pattern analysis revealed that, despite recognizing the importance of SRL, preservice teachers struggled with creating multiple feedback entries supporting SRL. An investigation into SRL-focused feedback patterns and their influencing factors suggested several ways to embed SRL into the core of teacher feedback. These include building teacher beliefs to cultivate collegial resources, constructing a databank of feedback comments on SRL activities to facilitate feedback provision, and examining learner differences to craft individualized feedback. The conclusion summarizes the implications for future feedback studies, outlining an agenda for further research in this area.

AI technology is reshaping language classrooms, prompting students to adopt flexible roles exhibiting linguistic competence and self-regulated learning (SRL) skills. Considerable studies explore the necessary integrated learning perspectives, emphasizing AI's adaptive role as a mind tool. In AI-mediated language learning, the technology's metacognitive importance enables students to learn with AI as a partner, encouraging independent critical thinking. Within Zimmerman's SRL model, AI as a mind tool is integrated for improving language students' strategic employment in a cyclical process. A systematic review, following PRISMA protocols, examines the intersection of AI and self-regulated language learning (SRLL) over 2000–2022. Findings highlight AI's evolving role, predominantly through algorithms and systems, aiming for micro and macro integration. Interactive AI has not fully engaged in two-way directions, despite a familiar process approach in reviewed studies. In the favored ESL/EFL research context, task-specific AI is utilized to encourage cyclical improvement with learner autonomy enhancement mainly among higher education students at intermediate level or above. Pedagogical values are possible when major SRL phases are fully practiced, even without highly autonomous AI. Future research is directed toward adaptive personalized technology by exploring the dynamic interplay between AI technologies and SRLL as educational practices under Education 4.0 principles.