A substantial body of research has demonstrated the benefits of written corrective feedback (WCF) for L2 written accuracy (see meta-analyses such as Kang & Han); however, comparatively few studies have experimentally examined the effectiveness of feedback in different timing conditions or how learners engage with feedback provided at different times. Yet, understanding when feedback is most beneficial and how feedback timing shapes learners’ engagement with corrections is pedagogically essential, as it can help learners maximize their opportunities to notice and integrate corrections into their evolving L2 linguistic system. The present study employs a mixed-methods design to compare learners’ engagement with WCF and accuracy improvement under three timing conditions: (i) immediate synchronous WCF (during writing), (ii) delayed synchronous WCF, and (iii) asynchronous WCF. Twenty advanced EFL university students completed two writing tasks, processed unfocused indirect WCF, completed audio-recorded questionnaires on their engagement with WCF, and revised their texts using screencast technology to capture real-time engagement and changes in accuracy. All groups significantly improved accuracy, with no advantage for synchronous or delayed synchronous over asynchronous feedback. All participants were similarly engaged, but different patterns of engagement were found because of different feedback timing conditions. These findings suggest that advanced learners can benefit equally from different feedback timing conditions using unfocused indirect feedback. Methodological and pedagogical implications are drawn.

Generative artificial intelligence (GAI) has gained increasing attention in English as a foreign language (EFL) education, with growing evidence supporting its efficacy in enhancing oral performance. Nonetheless, limited research has examined how GAI’s multimodal capabilities shape learner behavior, and how different learning behavior clusters relate to motivation and oral performance dynamics. To address these gaps, this study explored (1) the learning behavior clusters emerging from learners’ interactions with the Multimodal GAI (MGAI), and (2) the relationship between these clusters, and associated changes in motivation and oral performance. From a three-week intervention with 60 EFL learners, data were collected on behavior, motivation and performance. K-means clustering identified four distinct learners’ behavior clusters: interaction-based taskers, comfort-oriented balancers, output-focused monitors, and resource-driven strategists. We found significant differences across clusters. Resource-driven strategists and output-focused monitors showed greater positive changes in intrinsic motivation, while interaction-based taskers exhibited greater positive changes in extrinsic motivation. In oral performance, resource-driven strategists and output-focused monitors exhibited greater positive changes than both comfort-oriented balancers and interaction-based taskers in terms of fluency and coherence. Qualitative insights (i.e., interview and dialogue data) in each cluster provided illustrative information for these quantitative results. These findings provide pedagogical insights for the integration of MGAI into language learning contexts, highlighting that multimodal support alone does not automatically lead to positive learning changes. Thus, pedagogically guided use of multimodal support is needed to help learners move beyond surface-level MGAI-human interaction and more effectively appropriate MGAI tools to support scaffolded oral learning.

While both the importance of learner engagement and the role of design features have been acknowledged in the literature as key for realizing the potential of e-portfolios in language education, the relationship between learner engagement and teachers’ instructional design awaits further examination. To address this research gap, we conducted a qualitative case study at an international school in Hong Kong within the context of Chinese language education. Two groups of secondary students participated in this study, each utilizing e-portfolios as a learning tool under the guidance of two teachers. Major data sources included focus-group interviews with 8 students from one class and 12 from another. Other supplementary data sources included an online survey filled out by 19 students, collected excerpts from learners’ e-portfolios, and individual interviews with the two teachers. The analysis first involved identifying and categorizing learner engagement with e-portfolios, followed by thematic analysis of the relationship between teachers’ design choices and different patterns of learner engagement. The study found that while all four dimensions of learner engagement were identified among the learners, the specific types of engagement under each dimension differed significantly between the two groups. These patterns of learner engagement were shaped by teachers’ instructional design, especially how e-portfolios were integrated into teaching and learning, the emphasis placed on reflection and revision, and the purposes for using technology. This study contributes valuable insights for language educators aiming to implement e-portfolios effectively in their teaching.

Generative artificial intelligence (GenAI) is rapidly expanding within L2 education and is increasingly positioned as a core resource for lesson planning. This study explores how 22 pre-service teachers mobilized multimodal GenAI tools, including DALL-E for image generation and ChatGPT for instructional support, to plan Grades 3–4 Chinese-as-a-second-language (L2) vocabulary lessons in Hong Kong primary schools. Using the TPACK framework, we examined how their pedagogical, content, and technological knowledge interact during this process. Lesson artefacts, reflective forms, and semi-structured interview responses were analyzed. Results showed that participants actively employed a range of multimodal GenAI functions across instructional stages, with image-generation tools being the most frequently used (34 instances). They were aware of the Chinese character features and pupils’ cognitive and cultural characteristics to produce culturally attuned teaching materials. Rather than static overlap, content, pedagogical, and technological knowledge continuously shaped one another through iterative negotiation. Theoretically, the study operationalizes the TPACK framework into a GenAI-supported, multilingual primary setting and illustrates how the three knowledge domains co-evolve through recursive negotiation. Practically, it underscores the value of collaborative design, systematic cultural vetting, and the deliberate integration of interactive chatbots to enhance L2 vocabulary instruction.

This study, grounded in social cognitive theory and employing a pre-test/post-test quasi-experimental design, systematically observed 118 first-year accounting students from a regional public university in central China over eight weeks. The aim was to explore the associations between extracurricular asynchronous short-video chats, text chats, learners’ self-confidence, and foreign language proficiency among Chinese EFL (English as a foreign language) learners. Specifically, three intact classes were assigned to asynchronous video, asynchronous text, or business-as-usual control conditions. Outcomes were assessed with pre-/post- speaking proficiency tests and a validated self-confidence scale, and message/video logs were coded to triangulate engagement. The findings indicate that the text chat group showed no significant differences in their post-test results when compared to the control group. However, the video chat group exhibited statistically significant improvements in both self-confidence and foreign language proficiency in the post-test. These results provide important empirical support for the use of asynchronous communication tools in foreign language teaching, highlighting their potential to foster affective and cognitive gains in the short-video era.

Although interest in integrating Artificial Intelligence (AI) into language learning has increased, research examining the distinct effects of AI-mediated peer feedback (AIMPF) and traditional peer feedback (TPF) on L2 writing improvement remains limited. To address this gap in the literature, the present study explored the effectiveness of AIMPF in comparison to TPF in enhancing the accuracy and engagement (e.g. vigor, dedication, and absorption) in the writing of Iranian MA English students. Sixty intermediate Iranian English students enrolled in an academic writing course at a university in Iran were selected using purposive sampling and then randomly assigned to either a TPF group or an AIMPF group. The AIMPF group utilized an AI-supported feedback tool alongside peer review, while the TPF group received TPF sessions. Both groups completed pre- and post-tests, which included writing tasks and an engagement questionnaire. In addition, participants in the AIMPF group completed an open-ended survey to gather their attitudes and perceptions regarding AI-facilitated feedback. Results indicated that both groups demonstrated improvement in writing accuracy and engagement; however, the AIMPF group exhibited significantly greater gains in both areas. Qualitative data revealed that participants in the AIMPF group found AI-supported feedback beneficial for identifying language errors and supporting their revision process, although they also expressed reservations concerning the absence of human interaction within the feedback experience. These findings support that AI-supported peer feedback holds promise as a valuable method for developing L2 writing ability, potentially fostering both increased engagement and improved accuracy.

This study investigates the effects of digital reading on third-grade English as a Foreign Language (EFL) students’ motivation, attitude, reading comprehension, and vocabulary acquisition, supported by student and teacher views on digital reading. Reading is crucial for improving a foreign language, allowing learners to obtain information, gain diverse perspectives, and develop critical thinking. With the rise of digital literacy, understanding how children read on screens is essential for their education in today’s digital world. An explanatory sequential mixed-method research design was used, combining quantitative pretest-posttest quasi-experimental results with qualitative data. The study involved 23 third-grade EFL students from a private primary school in Turkey, participating in a six-week digital reading program. Data were collected through pretests and post-tests using the Digital Reading Motivation Scale (DMS), Attitude Toward Reading Scale (ATRS), and reading comprehension and vocabulary tests, supplemented by semi-structured interviews and teacher journals. Results showed significant positive effects of digital reading on students’ motivation, attitude, reading comprehension, and vocabulary acquisition. Both students and teachers found digital reading lessons beneficial for language development.

With increased emphasis on the need to develop all pre-service teachers’ (PSTs) intercultural competence and global English level as future teachers in the EU, this study analyzes PSTs’ perceptions of their experience in a telecollaboration exchange between two higher education institutions. This is to give insight into actual and perceived language and intercultural competence gains for PSTs with wide-ranging language levels (A1-C2). Using an online survey, the perceptions of 132 PSTs from Catalonia (Spain) were collected. They were asked to rate from 0 (lowest) to 10 (highest) the extent to which they perceived the exchange had helped them develop language skills and intercultural competence. In addition, actual English language gains were measured using the Oxford Placement Test (OPT) before and after the telecollaboration exchange. Results showed that, across all levels, intercultural competence was perceived as being developed the most, and grammatical accuracy, the least. PSTs with lower English levels found their vocabulary was greatly enhanced, but, as PSTs’ English level increased, their perceptions of vocabulary gains diminished. In fact, PSTs with higher proficiency perceived fluency and pronunciation as highly developed. Actual linguistic gains were found in all level groups, showing significant gains in all levels. These results illustrate how telecollaboration exchanges can be and are perceived by PSTs as beneficial in different ways depending on their different proficiency levels. Results offer insights to teacher trainers with PST cohorts with wide-ranging language levels.

This study examines the impact of WeChat-mediated task-based language teaching (TBLT) on Chinese EFL learners’ reading comprehension and engagement in a higher education context. Anchored in Sociocultural Theory and a multidimensional framework of engagement (behavioral, cognitive, emotional), the study explores how digital tasks mediated through WeChat shape learners’ reading development. An explanatory sequential mixed-methods design was adopted. Quantitative data were collected through a quasi-experimental procedure involving pre- and post-tests on reading comprehension and engagement, followed by semi-structured interviews. Results show significant improvement in reading comprehension and in behavioral and emotional engagement, while cognitive engagement showed only modest gains. Qualitative data suggest that WeChat’s functions - such as group chats, voice messaging, and resource sharing - supported learner participation, reduced communicative anxiety, and enhanced perceived task relevance. However, cognitive engagement appeared less responsive to the platform, indicating the need for deeper pedagogical structuring. The study proposes a differentiated model of engagement in digital TBLT contexts and highlights the dual mediating role of mobile platforms as both social and cognitive scaffolds. Findings contribute to research on mobile-supported language learning and offer pedagogical implications for designing socially situated reading tasks that align with learners’ digital practices.

Artificial Intelligence (AI) rapidly transforms language education, showing promise in literacy development, especially in reading and writing. However, its effectiveness in improving oral skills, which is crucial for communication in our interconnected world, requires further investigation. This study explored integrating corpus and AI technologies to enhance English pronunciation teaching and learning (PTL), encompassing both the production and perception of segmental and suprasegmental features across various English accents. The corpus and AI-aided PTL framework addresses the need for innovative PTL, particularly for diverse learners and growing intercultural communication (IC) demands. Three training sessions were designed. Session 1 introduced a pronunciation corpus with authentic speech data and AI-powered personalized feedback. Session 2 expanded corpus use with text-to-speech (TTS) tool for suprasegmental pronunciation practice. Session 3 focused on perceiving accented English through diverse speech samples from the corpus and TTS tool to enhance global communication competency. Thirty university students participated in an online self-access training course. Pre- and post-training tasks assessed pronunciation performance and perception of accented English. Results revealed significant reduction in inappropriate segmental features, such as substituting/θ/with/f/, and inappropriate suprasegmental features, such as inappropriate pause within chunks (e.g. cried | out). Enhanced understanding of diverse English accents was also observed. Post-training surveys and interviews provided qualitative insights. Participants valued the autonomous learning fostered by AI tools and appreciated exposure to diverse accents, recognizing its importance for global communication. These findings suggest the potential of corpus and AI-driven PTL, warranting further research into their long-term impact and adaptability across diverse learning contexts.