Limited research has examined the quality of language MOOCs and no existing instrument has been developed to gauge learners’ evaluation of LMOOC quality. This study develops an LMOOC Quality Evaluation Scale (LQES) and validates it in the Chinese context, which has the largest number of LMOOC learners in the world. The data were collected from 2,315 LMOOC learners in China using a mixed-method approach. Development and validation of the scale involved (1) generation of an initial item pool based on a semi-structured interview and literature review, (2) refinement of scale items through consultation of LMOOC experts and a focus group interview, (3) exploration of the factor structure of the scale using exploratory factor analysis, and (4) validation and confirmation of the final scale using confirmatory factor analysis. A four-factor model, comprising Instructional Design, L2 Teachers’ Competence, Teaching Implementation, and Technical Support, emerged and was validated. The 26-item LQES provides an original and comprehensive framework for understanding the complexities of LMOOC quality. This study highlights the critical factors underpinning the evaluation of LMOOC quality and paves the way for further refining of the instrument in future research.

This study investigated an 18‑week teacher education model grounded in technological pedagogical content knowledge (TPACK). Known as CATERR (comprehending, analyzing, teaching, evaluating, reflecting, and refining), this teacher education model cultivated the computer-assisted language learning (CALL) competencies of 43 content and language integrated learning (CLIL) preservice teachers (PSTs) from Taiwan. The model promotes peer coaching, where participants collaborate, reflect, and refine their teaching over three rounds. The study utilized a multi-method case study and triangulated the quantitative and qualitative data. Quantitative data refers to the TPACK-CLIL questionnaire administered before and after the teacher education model. Qualitative data included lesson plans, self-analysis, teaching demonstration videos, revised lesson plans, classroom discussion records, peer evaluations, and reflection notes. Data analysis involved paired-samples t-tests and descriptive statistics for the coding framework, thematic analysis for qualitative data, and a repeated measures ANOVA to compare three total scores across three rounds using scoring rubrics. Results showed that the CATERR teacher education model enhanced CLIL PSTs’ self-perceived and observed CALL competencies. Specifically, as “digital native” PSTs with high levels of technological knowledge (TK), they successfully transferred their TK into TPACK by adding pedagogical values and contextualizing the ICT tools in their CLIL lessons. Meanwhile, their ability to use ICT tools to facilitate interaction and students’ autonomous learning substantially improved. The theoretical and pedagogical implications for CALL teacher education research and practice are discussed.

Speaking is often challenging for language learners to develop due to factors such as anxiety and limited practice opportunities. Dialogue-based computer-assisted language learning (CALL) systems have the potential to address these challenges. While there is evidence of their usefulness in second language (L2) learning, the effectiveness of these systems on speaking development remains unclear. The present meta-analysis attempts to provide a comprehensive overview of the effect of dialogue-based CALL in facilitating L2 speaking development. After an extensive literature search, we identified 16 studies encompassing 89 effect sizes. Through a three-level meta-analysis, we calculated the overall effect size and investigated the potential moderating effect of 13 variables spanning study context, study design and treatment, and measures. Results indicated a moderate overall effect size (g = .61) of dialogue systems on L2 learners’ speaking development. Notably, three moderators were found to have significant effects: type of system, system meaning constraint, and system modality. No significant moderating effect was identified for education stage, L2 proficiency, learning location, corrective feedback, length of intervention, type of interaction, measure, and key assessment component. These findings suggest directions for future research, including the role of corrective feedback in dialogue-based CALL, the effectiveness of such systems across proficiency levels, and their potential in diverse learning contexts with the integration of generative artificial intelligence.

Consulting dictionaries during writing requires time and cognitive resources. ColloCaid, a writing assistance prototype freely available online, was designed to minimize the cognitive strain on writers by embedding a collocation database within the writing environment. Usability surveys have shown ColloCaid can indeed help. In this study, we go beyond user perceptions. Using authentic excerpts of student academic writing by 27 advanced L2 English speakers, we analysed (1) the lexical coverage of the tool, (2) the collocation changes prompted by the tool, (3) the reasons behind decisions to revise collocations, (4) the effect of revisions prompted by ColloCaid, and (5) the participants’ perceptions of using the tool to revise authentic writing assignments. Our findings indicate that ColloCaid offered good academic collocation coverage, that the participants tended to accept its collocation prompts with discernment, and that the revisions made resulted in more fluent texts overall.

This study synthesized 65 (quasi-)experimental studies published between 2010 and 2024 that examined the use of mobile applications to develop language learners’ vocabulary learning. Bayesian meta-analysis was adopted to assess (1) overall effect size; (2) subgroup analyses (i.e. education level, vocabulary knowledge, aspects of vocabulary learning, learning environment, sample size, mobile application type, gender, and cultural background); and (3) publication bias. A large effect size of 1.28 was found for the overall effectiveness of using mobile applications for vocabulary learning when we restricted the studies to long-term treatment duration of 10 weeks or above. Each moderator was analyzed and discussed, and implications for language teaching and research were provided.

The adoption of corpus technology in school classroom settings remains limited, largely due to insufficient technological pedagogical content knowledge (TPACK) training for pedagogical corpus use. To address this gap, we investigated how teacher education in corpus-based language pedagogy (CBLP), a subdomain of TPACK for corpus technology tailored to language teachers, influenced student TESOL teachers’ self-efficacy for independent language learning and teaching. Employing a mixed-methods approach, including a CBLP training intervention (n = 120), survey data (n = 96), and interviews (n = 8) with student teachers at a university in Hong Kong SAR, China, the research validates a theoretical model through confirmatory factor analysis and structural equation modelling. Results demonstrate that corpus literacy (CL) is foundational for effective CBLP implementation and development of independent learning self-efficacy, which in turn fosters innovative, resource-rich instructional strategies. CBLP also enhances teachers’ self-efficacy for student engagement, fostering more interactive and motivating classrooms. These findings emphasise the value of embedding CL and CBLP within TESOL teacher-education programmes to prepare future language teachers for self-efficacy within dynamic, technology-enhanced classrooms.

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.

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.