In technology-enhanced language learning (TELL), self-regulated language learning (SRLL) strategies are essential for supporting English as a foreign language (EFL) learners’ writing development. As collaborative learning becomes increasingly prominent in TELL, SRLL has expanded from individual regulation to collaborative contexts. However, limited research has compared how individual and collaborative SRLL contexts influence learners’ strategy use, writing performance, and learning behaviors. To address this gap, this study used WeChat as a mobile learning platform to compare university-level EFL learners’ SRLL strategy use, writing performance, and behavioral patterns in individual and collaborative self-regulated writing programs. Two intact classes were assigned to either a WeChat-based individual group (WIG) or a WeChat-based collaborative group (WCG). The collected data included SRLL strategy use questionnaire, writing scores, and WeChat learning logs. Results showed that the collaborative context promoted learners’ overall, cognitive, metacognitive, and behavioral SRLL strategy use, although no significant difference was found in motivational strategy use. The WCG also achieved higher writing performance and showed distinctive regulatory behaviors related to time monitoring and feedback awareness. These findings suggest that SRLL is a dynamic and cyclical process shaped by task demands, technological affordances, and social interaction. They also highlight the value of integrating individual and collaborative learning modes to support learners’ movement between self-regulation, co-regulation, and socially shared regulation.

This scoping review directs attention to artificial intelligence–mediated informal language learning (AI-ILL), defined as autonomous, self-directed, out-of-class second and foreign language (L2) learning practices involving AI tools. Through analysis of 65 empirical studies published up to mid-April 2025, it maps the landscape of this emerging field and identifies the key antecedents and outcomes. Findings revealed a nascent field characterized by exponential growth following ChatGPT’s release, geographical concentration in East Asia, methodological dominance of cross-sectional designs, and limited theoretical foundations. Analysis also demonstrated that learners’ AI-mediated informal learning practices are influenced by cognitive, affective, and sociocontextual factors, while producing significant benefits across linguistic, affective, and cognitive dimensions, particularly enhanced speaking proficiency and reduced communication anxiety. This review situates AI-ILL as an evolving subfield within intelligent CALL and suggests important directions for future research to understand the potential of constantly emerging AI technologies in supporting autonomous L2 development beyond the classroom.

Feedback is integral to second language (L2) writing instruction. However, large class sizes and limited teacher time often challenge the delivery of personalized feedback, prompting interest in AI-powered solutions such as ChatGPT (Escalante et al., 2023; Huete-García & Tarp, 2024; Steiss et al., 2024; Yoon et al., 2023; Zhang, 2024). This study evaluates a task-customized GPT model, “Belinda,” trained to assess A1-level Spanish learners’ writing and provide feedback. Two research questions guided the investigation: (1) Can Belinda accurately score beginner Spanish writing using a provided rubric? (2) Can Belinda deliver constructive qualitative feedback? Human and GPT-generated scores were compared for inter- and intrarater reliability, and qualitative analyses categorized the feedback for usability in the classroom. Results revealed moderate alignment between Belinda’s scores and human raters, though reliability of the GPT fell short of calibration benchmarks. Feedback quality varied, with Belinda often providing vague, incomplete, or inaccurate suggestions. Despite iterative training, the GPT struggled to balance error correction with encouragement, a critical need for novice learners. Additionally, inconsistencies in identical GPT versions raised concerns about reliability. While Belinda showed potential in automating feedback, its limitations in accuracy, contextual understanding, and positivity suggest it is not yet a viable substitute for human evaluation by itself. These findings emphasize the challenges of integrating AI into L2 instruction and call for the need for extensive datasets, robust training, and human–AI collaboration to achieve pedagogically sound outcomes. Future research should explore hybrid feedback models and scalable solutions to enhance AI’s role in language education without compromising learner progress or confidence.

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.

Generative artificial intelligence (GenAI) has been heralded by some as a transformational force in education. It is argued to have the potential to reduce inequality and democratize the learning experience, particularly in the Global South. Others warn of the dangers of techno-solutionism, dehumanization of learners, and a widening digital divide. The reality, as so often, may be more complicated than this juxtaposition suggests. In our study, we investigated the ways in which GenAI can contribute to independent language learning in the context of Pakistan. We were particularly interested in the roles of five variables that have been shown to be particularly salient in this and similar contexts: learners’ Generative Artificial Intelligence-mediated Informal Digital Learning of English (GenAI-IDLE) participation, AI Literacy, Foreign Language Enjoyment (FLE) and Foreign Language Boredom (FLB), and their second language Willingness to Communicate (L2 WTC). Employing a structural equation modelling approach, we surveyed 359 Pakistani English as a foreign language (EFL) learners to investigate their interrelationships between variables. The results demonstrate that EFL learners’ GenAI-IDLE activity directly and positively influences AI literacy and FLE. Students’ AI literacy and FLE play a chain-mediating role in the relationship between GenAI-IDLE participation and L2 WTC. However, FLB lacks predictive power over L2 WTC. We discuss the implications of these results for language learning, in particular in low-resource contexts.