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.

In this study, we apply a stated preference experiment and discrete choice modelling to examine Greek primary teachers’ preferences for reward and consequences strategies in supporting students with attention-deficit/hyperactivity disorder. We also investigate how these preferences differ based on teacher gender, educational attainment, and special education training. A total of 430 general and special education teachers completed 2,948 choice cards. Each card presented hypothetical scenarios combining five behavioural management attributes: verbal praise, responses to undesired behaviours, privilege removal, point-based systems, and tangible versus intangible rewards. Data were analysed using an alternative-specific conditional logit model. Verbal praise and intangible rewards (e.g., free time, token economies) received the highest preference ratings, while reprimands and privilege revocation were consistently disfavoured. Female teachers, those with postgraduate degrees, and special education trained educators assigned significantly greater utility to informational consequences and tangible rewards. Findings reveal a strong consensus among teachers in favour of positive reinforcement strategies and a reluctance to employ punitive measures. Professional development should emphasise reward-based techniques. Future research should link these stated preferences to actual classroom practices and student outcomes to assess their real-world effectiveness.

The implementation of evidence-based practices (EBPs) does not always lead to successful outcomes due to various contextual factors. The Evidence-Based Practice Attitude Scale (EBPAS; Aarons, 2004) assesses implementers’ attitudes towards adopting EBPs (ATE), helping to understand the discrepancy between planned and implemented EBPs. Despite the growing implementation of school-based EBPs, the EBPAS has seldom been applied to general education teachers. This study aimed to validate the EBPAS for primary school teachers in Singapore using content validity and confirmatory factor analysis and to examine how the contextual characteristics influence ATE. A total of 170 teachers from 10 schools participated anonymously in an online survey. Confirmatory factor analysis results supported the four-factor structure of the EBPAS. All subscales showed excellent to acceptable internal consistency, with Divergence being the lowest. Teachers with higher educational attainment were more likely to be open to adopting EBPs. Similarly, teachers’ perceived school leadership support was significantly associated with their ATE. However, neither years of teaching experience, years of supporting students with special educational needs, nor teacher efficacy in inclusive practices significantly predicted ATE. The study highlights the need for further refinement, particularly of the Divergence subscale, through the exploration of alternative constructs and validation with larger samples.

Autistic high school students overwhelmingly have a poor experience of school. Research into this stage of life is limited, and researchers have tended not to talk to autistic students directly, instead hearing from non-autistic observers such as teachers and parents. This study aimed to address this gap in our knowledge by interviewing autistic students in mainstream high schools about their experience of school and their ideas for how this could be improved. Ten autistic students (13 to 20 years old) in Australian high schools were interviewed. Students overwhelmingly reported a negative experience. Most said their ideal school would be one where teachers and peers had greater understanding about autism and teachers had training in autism. By including the student voice, this research makes a valuable contribution to our understanding of autistic students’ school experience, adding depth and detail, and including what they would like to see changed. Importantly, the interview data also challenged misconceptions about what autistic students prioritised. The voice of autistic teens can make an important contribution to policies and practices aimed at improving their experience of school.

This short research article interrogates the rise of digital platforms that enable ‘synthetic afterlives’, with a focus on how deathbots – AI-driven avatar interactions grounded in personal data and recordings – reshape memory practices. Drawing on socio-technical walkthroughs of four platforms – Almaya, HereAfter, Séance AI, and You, Only Virtual – we analyse how they frame, archive, and algorithmically regenerate memories. Our findings reveal a central tension: between preserving the past as a fixed archive and continually reanimating it through generative AI. Our walkthroughs demonstrate how these services commodify remembrance, reducing memory to consumer-driven interactions designed for affective engagement while obscuring the ethical, epistemological and emotional complexities of digital commemoration. In doing so, they enact reductive forms of memory that are embedded within platform economies and algorithmic imaginaries.

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 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.

To better understand language teacher turnover, this study closely replicates and extends McInerney et al.’s (2015) research, which found that teacher commitment predicted turnover intentions to schools (44.2%) and the profession (45.2%) among Hong Kong schoolteachers (N = 1,060). Given the relatively stable employment conditions in that context, the generalizability of these findings to more mobile populations, such as expatriate native English-speaking teachers (NESTs), remains uncertain. In this replication, (1) the population was changed to NESTs in East Asia, and (2) subgroup comparisons were extended to reflect distinctions relevant to the replication sample. Additionally, results were directly compared to the original. A total of 215 NESTs participated. Results showed similar directional patterns but stronger effects: commitment explained 51.8% of variance in turnover intentions to schools and 59.7% to the profession. Affective commitment was the strongest predictor, though NESTs reported lower commitment and higher turnover intentions than in the original study.