The effect of high-immersion virtual reality (HiVR) on second language (L2) learners' socio-emotional responses has gained increasing attention in recent years, though findings remain mixed. This study meta-analyzed 18 primary studies (31 effect sizes, n = 996) published between 2019 and 2024 to examine (a) the overall effect of HiVR on L2 learners' socio-emotional variables as compared with low-immersion (2D) language instruction and (b) the extent to which this effect varies as a function of three groups of moderators, including specific socio-emotional variables as well as learner- and treatment-related characteristics. Regarding educational level, the included studies evaluated secondary and postsecondary L2 learners. Using a random-effects model, the resulting aggregate effect size across studies was g = 0.50. Subsequent moderator analyses showed that HiVR significantly increased L2 learners' sense of presence, self-efficacy, motivation, and engagement, while reducing anxiety as compared to less immersive experiences. Additionally, variability in the effect of HiVR was explained by learners' proficiency level and target language. No significant moderating effect was identified for educational level, HiVR content, HiVR learning activities, and head-mounted display type. These findings underscore HiVR's potential as a powerful tool for improving socio-emotional outcomes in L2 learning.
This quantitative study develops and validates a novel Culturally Responsive Blended Learning Success (CR-BLS) model for evaluating technology-enhanced EFL instruction effectiveness in collective educational contexts. Conducted at a Chinese university, the research examined an intelligent blended learning ecosystem integrating three widely adopted platforms (U-Campus, GradingNet, and Learning Hub) for content delivery, assessment, and collaboration in College English instruction. The CR-BLS model extends a widely recognized seven-factor quality framework by integrating three culturally grounded moderators: hierarchical teacher-student relationships, collective learning orientation, and examination-driven motivation. The proposed model was empirically validated through data collection from 1,816 non-English major undergraduates engaged with the blended learning ecosystem using partial least squares structural equation modeling (PLS-SEM). Results revealed that the proposed quality factors explained 71.4% of the variance in perceived satisfaction, 54.2% in perceived usefulness, and 34.1% in system use. Learner quality and instructor quality significantly influenced both perceived satisfaction and usefulness, while educational system quality primarily affected system use. Furthermore, perceived usefulness, satisfaction, and system use collectively accounted for 64.7% of the variance in perceived learning benefits. Cultural moderators demonstrated significant influence on the relationships between quality dimensions and learning outcomes, enhancing the proposed model's contextual validity for collective educational environments. This study contributes to both theoretical advancement and practical implementation of blended learning systems in collective educational settings.
This study investigates how language learners prioritize criteria for evaluating data-driven learning (DDL) tools and how these priorities relate to tool preference. While previous research has proposed multiple criteria for evaluating the perceived effectiveness of DDL tools in capturing the emic dimension of the learner experience, empirical work has scantily examined how learners differentially prioritize these criteria, or how such priority structures can be empirically incorporated into systematic comparisons of tools. Given the recent emergence of generative artificial intelligence (GenAI) in language learning, this study examines how GenAI-based tools may contribute to DDL when judged against learner-valued effectiveness criteria. Drawing on a literature review, six criteria were identified: sentence comprehensibility, relevance to semantic learning, relevance to syntactic learning, perceived pedagogical value, accessibility for independent use, and support for autonomous learning. Thirty-five Korean EFL university students completed an analytic hierarchy process (AHP) task. First, they made pairwise comparisons among the six criteria, yielding priority weights that indicated the relative importance learners attach to each aspect of DDL effectiveness. Second, the study compared the perceived effectiveness of a traditional online concordancer that accesses the British National Corpus with that of a custom-developed GenAI-based concordancer, prioritizing these weighted criteria. The results indicate that learners assign differentiated importance to the six criteria and that these priority patterns are associated with the concordance they regard as more effective. This study demonstrates the usefulness of AHP for modelling multi-criteria, perception-based evaluations of DDL tools and incorporating learners' priority judgements into the comparison of alternative tool designs.
This study investigates the effectiveness of data-driven learning (DDL) in promoting lexical complexity in Chinese English as a foreign language (EFL) learners' argumentative writing, tracks developmental trajectories, and examines learners' perceptions. Adopting a quasi-experimental design, one class (n = 26) received DDL instruction, and the other (n = 22) received non-DDL instruction. Data were collected using triangulation, including argumentative writing samples from five time points, pre- and post-instruction questionnaires and semi-structured interviews. Results showed that learners in the DDL class significantly improved their lexical complexity, while the non-DDL class experienced declines. Across the five time points, nonlinear trajectories were observed in lexical complexity at the individual learner level. Learners reported positive attitudes toward DDL, though some challenges in corpus use remained. These findings provide empirical support for the effectiveness of DDL in promoting lexical complexity development in Chinese EFL learners' argumentative writing and provide pedagogical implications for corpus-based writing instruction.
This study investigates whether and how interacting with ChatGPT may offer a context that supports perspective shifting and the development of cognitive flexibility, defined as the capacity to move between etic (outsider) and emic (insider) perspectives. Drawing on individual interviews with students enrolled in an advanced university-level Language for Specific Purposes (LSP) French course focused on marketing and advertising in France, this qualitative study examines students' perspectives on their experiences using ChatGPT to conduct market research on French consumer needs and preferences. The analysis reveals that while students expressed concerns about the legitimacy, authenticity, and cultural positioning of AI-generated content, the interactive and conversational nature of the tool enabled some students to experiment with culturally unfamiliar roles, adopt emerging emic stances, and reflect on the limits of their interpretive frameworks. However, co-creative engagement or shared agency with ChatGPT was not automatic and depended on prompt design, tolerance for ambiguity, and the negotiation of subjective positioning. Rather than facilitating perspective transformation, ChatGPT-supported interactions appeared to foster more modest but meaningful shifts in interpretive positioning and dialectical thinking. The study points to prompt literacy as crucial for fostering more dynamic partnerships with ChatGPT and enabling students to explore alternative perspectives and roles in ways that support the development of intercultural competence in the L2 classroom.
