Depth of Processing (DoP) is central to several models of instructed second language acquisition (ISLA) (Leow, 2015). While DoP has been extensively examined in vocabulary learning (e.g., Laufer & Hulstijn, 2001), its role in the acquisition of grammatical structures, particularly through oral and audio-visual modalities remains comparatively underexplored. Drawing on the Levels of Processing framework (Craik & Lockhart, 1972), this study investigated whether task-induced processing depth influences the acquisition of third conditionals and comparatives, and whether language aptitude (LLAMA B, D, F) predicts learning gains. Using a pretest–posttest design, four intact classes involving 108 Grade 8 EFL learners in India were assigned to one of the three experimental conditions differing in level and type of processing: Low DoP, High DoP Explicit, and High DoP Implicit. The treatment task required the participants to listen to an audio-visual story containing the target structures (third conditionals and comparatives) and complete analytic tasks (varying in different groups) before listening to the story for a second time. The oral performance of the participants was analysed for accuracy, and frequency of use of the target structures. A grammaticality judgement test (GJT) and an elicited imitation task (EIT) were used to assess the participants’ gains in the knowledge of the target structures. In addition, language aptitude (LLAMA B, D, F) was measured and modelled as a predictor of gain scores. Results showed a significant effect of processing condition on GJT performance with the High DoP Implicit group outperforming the High DoP Explicit group on total scores and comparatives. In contrast, ANCOVA analyses indicated no significant group differences on EIT outcomes once pretest performance was controlled, with pretest scores strongly predicting posttest performance. In oral production, processing effects were structure-specific: the Low DoP group showed greater accuracy gains for comparatives, while both High DoP groups revealed increased frequency of use of third conditionals. However, across all four measures, multiple regression analyses indicated that LLAMA B, D, and F did not significantly predict gain scores. Overall, the findings support a processing-by-structure pattern: deeper processing benefits were most evident for the more salient structure (comparatives) on an explicit, written measure, whereas complex, low-salience forms (third conditionals) showed limited accuracy change but increased frequency of use under deeper processing.

Artificial intelligence (AI) is transforming English language education (ELE) by enabling personalized learning, automated assessment, adaptive content generation, and immersive practice environments. This paper synthesizes current developments, identifies emergent trends, and offers evidence-informed predictions about how AI will shape classroom practice, curriculum design, assessment, teacher roles, and policy over the next decade. Drawing on interdisciplinary literature from computer-assisted language learning (CALL), intelligent tutoring systems (ITS), natural language processing (NLP), and educational policy, the paper argues that the most significant near-term impact will stem from hybrid systems that combine large language models (LLMs) with pedagogically informed scaffolding and teacher mediation. Key trends discussed include (1) ubiquitous personalized feedback and adaptive pathways; (2) automated, formative assessment with rich analytics; (3) realistic speaking/listening practice via multimodal conversational agents and immersive virtual environments; (4) AI-assisted material creation and differentiation for diverse learner needs; and (5) data-driven teacher support and professional development. Predictions address likely improvements in scalability and access, as well as persistent challenges: bias and fairness in language models, privacy and data governance, over-reliance on automated feedback, and the need for robust teacher training and curricular alignment. The paper concludes with practical recommendations for educators, institutions, and policymakers to harness AI’s affordances while safeguarding equity, transparency, and pedagogical quality. These include adopting hybrid human–AI workflows, emphasizing explainability and interpretability in tools, developing clear data-ethics policies, investing in teacher capacity building, and prioritizing research-practice partnerships. The analysis aims to be actionable for practitioners and decision-makers planning for an AI-augmented future of English language learning. Keywords: artificial intelligence, English language education, adaptive learning, large language models, assessment, teacher role, ethics

According to the 2026 ETS Human Progress Report, traditional job tenure is no longer a guarantee of security; instead, adaptability has become the single most critical capability for career survival and progress. Multilinguals have an advantage when it comes to adaptability. Educational Testing Service’s survey of over 32,000 workers and individuals across 18 countries presents […] The post ‘Speak the New Language of Adaptability’ appeared first on Language Magazine .

Middle and high school teachers often face the challenge of addressing a wide range of literacy levels among their students. While some students are ready to read complex texts independently, others struggle with fundamental skills like phonics and decoding, leading to disengagement and a loss of motivation. At the same time, teachers may be limited […] The post Supports for Struggling Adolescent Readers appeared first on Language Magazine .

With the rapid growth of research in English‐medium instruction (EMI) in higher education, much research has been conducted through qualitative and quantitative approaches. Yet, little attention was paid to the use of mixed‐methods research (MMR) in this field. Therefore, this systematic review aims to examine previous literature that adopted MMR in EMI research, specifically evaluating the methodology across three aspects: epistemological orientation and purpose, MMR question formulation, and MMR design. Among 136 studies identified, substantial methodological gaps were found across all three aspects. First, only seven studies explicitly stated an epistemological orientation whereas 27 studies provided explicit purpose terminology. Second, only six studies explicitly presented an integrative MMR question, while 112 studies provided qualitative and/or quantitative research questions, and 18 studies lacked explicit research questions. Finally, MMR design characteristics were rarely described: (1) nearly half of the studies did not specify a design, (2) no study used specific sampling procedure terminology, and (3) questionnaires and interviews were the most frequently used instruments. Based on these methodological gaps, this study offers recommendations for strengthening future EMI studies that employ a mixed‐methods approach.

<p>When the American Academy of Pediatrics updated its<a href="https://publications.aap.org/pediatrics/article/157/2/e2025075320/206129/Digital-Ecosystems-Children-and-Adolescents-Policy"> <u>guidelines</u></a> around screen time for children and teenagers for the first time in 10 years this past January, many teachers and educators applauded the news. The document focused less on television and more on digital devices. It offered educators, families, pediatricians, and other stakeholders research-backed guidance on using digital devices to support children’s learning, rather than prioritize prolonged engagement. </p><p>However, many of the recommendations were not tailored to specific age groups. Recommendations like “create a family media plan” and “protect sleep” were helpful but offered few concrete steps for pre-K students. </p><p>That left early childhood educators asking what they could do for their young learners.</p><h2>The Risks in Pre-K Through K</h2><p>Unlike the physical world, digital content comes fast, in short bursts, and when kids get used to that, it has negative consequences. “Too much of this screen and things jumping in front of them, it could be damaging overall,” says Latoya Jones, a pre-K through fifth-grade media specialist in Broward County, Florida. “They are developing their cognitive processes, and if we’re teaching them to think in six-second bursts, maybe that’s not what we’re aiming for.”</p><p>When kids interact with devices rather than people, their emotional regulation can suffer, says Kristina Turner, a first-grade teacher at the Paterson, New Jersey, campus of<a href="https://collegeachieve.org/"> <u>College Achieve Public Schools</u></a>, a network of three K-12 charter schools. “A lot of the students, they are quick-tempered because they’re just used to quick things. If something takes too long, their behavior starts to heighten,” she says.</p><p>For the youngest learners in particular, “it is so important that they are learni

Second language (L2) classroom is a dynamic and evolving space shaped by ongoing interactions between teachers and students. Understanding how these dynamic interactions unfold from a psychological perspective is critical for advancing L2 learning and teaching. This study contributes to this endeavour by examining the reciprocal relationship between teacher enthusiasm and L2 student classroom engagement as well as the mediating role of perceived learning value in this reciprocal relationship. Data were collected from 317 English as a foreign language learners in Vietnam at three time points across an academic semester and analysed by means of random-intercept cross-lagged panel modelling. Results revealed that teacher enthusiasm and L2 student engagement were reciprocally related over time, and this reciprocity was mediated by students’ perceived value of L2 learning tasks. These mediated reciprocal relations remained robust even after controlling for prior levels of each construct. The findings highlight the co-regulated and cyclical nature of L2 classroom affective-motivational climate and offer practical implications for L2 pedagogy.

As the central construct in task-based language teaching (TBLT), “task” has been defined in a myriad of ways. Though popular criterion-based definitions offer a tantalizingly straightforward description of the construct, they have, at times, proved difficult to apply in practitioner contexts. With over 17 definitions identified in the literature, prominent scholars have advocated for efforts that offer a more unified understanding of “task” that may be better able to support task-based language teaching in implementation initiatives. This exploratory, qualitative study examines how two cohorts of K–16 U.S.-based world language teachers ( n = 35) approached task design in a task-based teacher education program from a materials development perspective. An analysis of the 114 activities teachers created revealed that although submissions had task characteristics, the intricacies of their designs were not adequately captured by traditional task criterion. Subsequent analysis following a directed content approach identified five distinct categories of activity design. Advancing previous interest in expanding the binary distinction of “task” versus “non-task,” we drew on these categories to develop the Activity Design Typology. The typology serves as a resource for understanding, explaining, and evaluating instructional tools for their pedagogic and empirical value. Use cases for the task typology are discussed in relation to initiatives aimed to further instructed second language acquisition research agendas and support TBLT implementation efforts.

This study builds on insights into the crucial role learner emotions play in their flourishing. It investigates learners’ and teachers’ perspectives on how to enhance positive emotions both inside and outside modern foreign language classrooms in England, amidst a language crisis and reduced modern foreign language uptake across the United Kingdom. A thematic qualitative text analysis of 18 semi-structured interviews with secondary-level teachers and learners from 7 schools across England revealed that both learners and teachers emphasise the significant impact of teachers on fostering positive emotions in the classroom. They also showed that students lack awareness of the benefits of learning languages beyond English. Furthermore, the interview data suggests that the government, schools, school leaders, and parents must also engage to elevate the status of modern foreign languages and promote positive social change.

Laura Stewart, Terrie Noland, and Jeanne Schopf offer practical strategies for the secondary classroom The 2024 NAEP Report Card delivered a stark reminder: only 30% of eighth graders and 35% of twelfth graders are reading at a proficient level. These are not abstract statistics. They represent students like the eighth grader who threw a computer [&#8230;] The post Adolescent Literacy Can’t Wait appeared first on Language Magazine .

The rapid advancement of artificial intelligence (AI) has brought both opportunities and challenges to the field of education. Within this evolving landscape, the emergence of the AI divide has become a pressing concern, particularly in teacher education and EFL instruction. This study developed a three‐level three‐factor conceptual framework to explore the AI divide through the dimensions of AI access, AI capability, and AI outcomes, while also considering technological, personal, and institutional influences. Using this framework, the study employed a comparative research design to examine how novice and veteran EFL teachers integrated AI differently in their teaching practices. The findings revealed that the AI divide between novice and veteran EFL teachers mostly occurred at the second level (AI capability) and third level (AI outcomes), rather than the first level (AI access). These disparities were shaped by a combination of technological, personal, and institutional factors, including perceived ease of use of AI, educational background, social support, and access to AI‐related professional development. This study extends the concept of AI divide into the field of EFL education and offers valuable theoretical insights and practical implications for more equitable AI integration in EFL teaching.

AI tools are increasingly embedded in second language acquisition (SLA), yet their integration raises unresolved tensions between technological potential and pedagogical practice. While AI applications promise efficiency, personalization, and extended opportunities for interaction, these affordances are often overstated or disconnected from authentic learning, cultural exchange, and equity. At the same time, teacher preparation programs have been slow to adapt, leaving teachers underprepared to critically evaluate AI-mediated language learning environments. This chapter examines AI in SLA through the interpretive frameworks of the CoI and SL2, positioning teacher digital competence as a mediating construct between technological capability and educational value. Traditional SLA approaches provide historical context, while adaptive systems, chatbots, and GenAI are analyzed in terms of presence, interaction, authenticity, and cultural depth. Three themes emerge: Expanded opportunities for cognitive and social presence, persistent risks related to inequity, and the central role of teacher judgment. The chapter concludes that the educational value of AI depends less on technical innovation than on the preparedness of teachers to use it critically, ethically, and inclusively to design and mediate AI-supported language learning experiences.