This study examined the group mediation and regulation of online A2-level English learners during synchronous speaking tasks without teacher assistance. Based on Vygotsky’s Sociocultural Theory, this study examined technology-mediated interaction and student agency among 15 pre-intermediate adult students at a Mexican university. Using Rico’s (2016) framework, nine Zoom sessions were transcribed and coded for regulation strategies. Results show Participation Structure Regulation (n = 44) and Laughter and Empathy (n = 43) are common, emphasizing social bonding and emotional safety in language use. A peer regulator role emerged as a strategy in response to Zoom breakout room constraints. The study also found that peer scaffolding can lead to linguistic errors from stronger students. These results suggest that breakout rooms are autonomous spaces where affective regulation is vital but requires targeted teacher feedback to avoid fossilizing errors.
This study examines the relationships among technostress, digital fatigue, and occupational well-being among English Language Teaching (ELT) instructors, while examining the predictive role of technostress. The sample for this study included 472 ELT instructors working at public and foundation universities, as well as language institutes, across Türkiye. The participants completed three validated surveys: the Tripartite Occupational Well-Being Scale, which assesses subjective vitality, behavioral engagement, and professional growth; the Digital Fatigue Scale, which assesses the psychological and physical components of digital fatigue; and the Technostress Scale, which aims to assess the use of digital tools, institutional expectations, and the constant restructuring of technology. The findings indicated that the degree to which instructors experience technostress is a significant predictor of higher levels of digital fatigue, and that digital fatigue has a strong negative predictive role for occupational well-being. In addition, mediation analysis revealed that digital fatigue mediated the negative relationship between technostress and occupational well-being, indicating that higher technostress was associated with greater digital fatigue and, consequently, lower occupational well-being. Taken together, these data highlight the urgent need for institutions to provide targeted support through professional development opportunities, digital well-being policies, and more sustainable approaches to technology use in ELT settings.
Despite growing evidence for Artificial Intelligence (AI) in writing, little is known about combining Generative AI (GenAI) with peer feedback in L2 writing instruction. This mixed-methods study, grounded in AI-assisted language learning and collaborative learning theory, investigated the hybrid effect of GenAI and peer feedback through cooperative learning on the Writing Task 2 performance of 60 Iranian IELTS candidates. The experimental group received instruction integrating ChatGPT-5 and peer corrective feedback within cooperative activities, while the control group received parallel teacher-led instruction without GenAI feedback or structured peer collaboration. Pre- and posttests, assessed by certified examiners, showed the experimental group improved significantly in grammatical accuracy, lexical range, and discourse organization. Questionnaires and semi-structured interviews revealed that students perceived cooperative learning favorably and found GenAI feedback timely and useful for internalizing corrections, while peer collaboration reduced anxiety and enhanced idea development. Findings underscore the potential of large language models combined with peer feedforward to foster more effective and confident L2 writing, with implications for technology-enhanced instruction in high-stakes test preparation.
We investigated the use of hedges within Interactive Meta Discourse Markers (IMDM) in a longitudinal learner corpus, comprising one semester of written data from 35 tertiary-level EFL students. The analysis involved both group-level observations to track collective trends in hedge usage over time and individual-level analyses focusing on five students’ use of hedges throughout the semester. A frequency-based approach was employed to identify the most common hedges, which were then compared to native-speaker data to assess similarities and differences, as well as the collective tendencies of both groups regarding hedge usage. The findings revealed that should was the most frequently used hedge in the non-native corpus, with students notably not employing noun phrases in their arguments. The common hedging expressions suggested a pattern of authorial stance, and log-likelihood analysis showed similarities in hedge use. A comparison of the most frequently used hedges indicated an overuse of should within the non-native corpus. Additionally, a correlation was observed between students’ use of hedges and their exam performance over the semester, alongside positive interaction evident in the collective analysis.
Lexical chunks are essential for fluent language use, yet many EFL learners struggle to notice them in authentic input. This study investigated the effectiveness of a ChatGPT-aided chunking approach designed to enhance learners’ awareness of lexical chunks through explicit instruction and contextualized noticing activities. Seventy first-year Chinese EFL learners participated in a 13-week intervention integrated into a College English course, with complete data obtained from 62 students for statistical analysis. Using a one-group pretest–posttest design, learners completed chunk-recognition tasks before and after the intervention, followed by a language proficiency test and a perception questionnaire. Results showed a significant increase in the mean number of chunks recognized, from 11.39 (SD = 7.19) in the pretest to 20.81 (SD = 8.17) in the posttest, t(61) = −11.96, p < .001. Chunk recognition ability was also positively correlated with overall English proficiency, r(60) = .618, p < .001. Questionnaire results indicated that learners perceived the approach positively and reported improvements in multiple language skills. Despite these gains, chunk recognition remained relatively limited, suggesting that noticing lexical chunks is a more challenging process than is often assumed. The findings highlight the importance of explicitly fostering chunk awareness and suggest that generative AI can serve as a valuable tool for supporting chunk-based instruction in EFL classrooms.
