This article examines how a top‐down English medium instruction (EMI) decision changed a language teacher's status and participation in faculty governance in a graduate engineering school at a national university in Japan. Using a longitudinal single‐case design, the study follows one language teacher across the period from policy announcement to early rollout of EMI implementation. Data comprise three milestone interviews, audio‐recorded research‐practice meetings, reflective journals and implementation documents. Two main findings emerged. First, EMI acted as a catalyst for cultural change by raising the status of the language teacher in a setting where language support had been dismissed as a distraction from “real” engineering. Second, EMI granted the language teacher access to collaborate with a senior content teacher, who served as a gatekeeper to institutional leadership. This collaboration resulted in the formal establishment of EMI support provision and repositioned language support as a recognized component of engineering education. We found that EMI transformed not only the teacher's participation in governance but also faculty‐wide decision‐making processes. The study demonstrates how process tracing can document the decision‐making mechanisms that contribute to institutional cultural change during a faculty‐level transition to EMI.

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.

The rise of GenAI‐powered image generation tools presents new opportunities for English learners to engage in digital multimodal composing (DMC) in both creative and critical ways. At the same time, it calls for a reconceptualization of DMC pedagogy in language education. In this article, we build on the concept of fine‐tuned prompt literacy (Kang & Yi, 2023) in the context of GenAI‐assisted multimodal writing and introduce a pedagogical framework informed by Systemic Functional Linguistics (SFL) to support its development and implementation. We conceptualize fine‐tuned prompt literacy at the intersection of multimodal literacy and GenAI literacy, emphasizing the need for students to envision meaningful GenAI outcomes and engage with GenAI tools critically and strategically. Drawing on our research on ESL students' photo essay creation with GenAI image generators, we show that separating instruction on multimodal and GenAI literacies may hinder students' process of creating multimodal resources in an intentional and rhetorically effective way. To address this gap, we present an SFL‐informed framework that guides students in designing prompts based on the ideational, interpersonal, and textual metafunctions. Teaching observation and students' writing from a first‐year ESL writing course illustrates the framework's potential to enhance GenAI‐integrated DMC instruction. This brief report offers both theoretical insights and practical strategies for teaching multimodal writing in the era of GenAI.

This study examined the use of artificial intelligence tools, which have garnered significant attention in recent years, in the assessment and evaluation processes of language education. For this purpose, student essays were scored by Turkish middle school teachers and artificial intelligence tools both with and without the use of a rubric, and the findings were evaluated based on generalizability theory. Additionally, the research findings were shared with participants to gather qualitative data, which were analysed using the inductive thematic analysis method to support the research results. The findings revealed that in evaluations conducted without a rubric, teachers were limited in their ability to distinguish individual differences and demonstrated low scoring consistency. In contrast, artificial intelligence tools were more effective in distinguishing individual differences and exhibited high consistency. In evaluations conducted using a rubric, scoring consistency increased in both groups, although, as in the first evaluation, artificial intelligence tools demonstrated a higher level of consistency. Regarding the research findings, teachers expressed that individual biases, mood, and professional experiences influenced their scoring processes and emphasized the potential of rubrics and artificial intelligence-supported feedback systems for achieving more consistent results. Artificial intelligence tools, on the other hand, highlighted their independence from subjective factors but stressed the need for more diverse and generalizable datasets to further enhance their evaluation capacities.

Globalization has revolutionized translation training, and English education policies have encouraged more practical, innovative, and, in this regard, oriented teaching approaches. This study investigates the multi-level effects of English education policies on translation training at the national (macro), institutional (meso), and classroom (micro) levels, with particular attention to how policy influences the adoption of innovative instructional models and students’ perceived learning-related outcomes. Using a mixed-methods design, qualitative data were gathered through semi-structured interviews with 78 respondents, including policymakers, administrators, instructors, and students, whereas quantitative data were collected through a survey of 436 translation students (undergraduate and postgraduate). Qualitative data were analyzed using NVivo, and SPSS and SmartPLS were used to analyze quantitative data. The results indicate that translation training (β = .373, p < .001) is significantly associated with English education policies. Moreover, translation training is positively related to innovative training models (β = .302), career readiness (β = .268), perceived training quality (β = .318), practical skill acquisition (β = .292), and intercultural competence (β = .285), all at p < .001. These findings illustrate the focal point of translation training as one of the critical mechanisms through which policy is connected to various perceived academic and professional outcomes. University cooperation and pedagogical development at the institutional and classroom levels are also associated with perceived quality and applicability of translation training programs. Its uniqueness lies in that it combines several dimensions of outcomes in one analytical framework and provides a clear understanding of policy–practice alignment. The results suggest that translating students need effective policy–practice integration to be associated with students’ perceived preparedness for global professional contexts.

<p>As he instructs bilingual learners in the humanities and social studies, Daniel Gomez sees big promise in artificial intelligence.</p><p>“Teachers in general cannot be knowledgeable about every single subject,” and certainly not in multiple languages, says Gomez, who teaches at the bilingual K-12 Ideal School in Tampa, Florida. AI can help close the gaps. “It will be able to pick up any topic students are interested in and handle the topic reasonably well.”</p><p>He isn’t alone in this assessment. Harvard researchers, for example, are actively exploring how AI-powered chatbots can support bilingual student learning and parent–child dialogues.</p><p>“There are lots of opportunities to use AI to adapt learning materials into different languages, which can expand access for bilingual learners,” says<a href="https://www.gse.harvard.edu/directory/faculty/ying-xu"> <u>Ying Xu</u></a>, an assistant professor of education at Harvard and one of the researchers involved in the work.</p><p>“Some projects are also exploring how AI can provide additional scaffolding to help children comprehend content in their home language,” she adds. “For example, a multilingual language model might keep the core learning content in English while offering explanations, prompts or supports in the child’s home language.”</p><p>Experts see great potential for AI to elevate bilingual education — if it’s done right.</p><h2><strong>Chatbots as Educational Aids</strong></h2><p>“Bilingual learners are obviously learning the language and culture of the classroom and the environment that they’re in,” says<a href="https://www.linkedin.com/in/krystlesalas/"> <u>Krystle Salas</u></a>, assistant director of special populations at Second Mile, which operates alternative education charter schools for at-risk students in several states.</p><p>“They don’t necessarily have access to the instructional materials or even just the conversations going on in the classroom — and certainly not at the level of their p

Grounded in the L2 motivational self system theory, this quasi‐experimental study investigated the impact of three motivational interventions on two outcome measures: L2 learning effort and L2 achievement. A total of 391 Chinese junior secondary students were assigned to four conditions: a control group receiving no motivational instruction, and three experimental groups exposed to either traditional motivational strategies (MSs), vision‐based MSs, or a combined approach integrating both types of strategy. The effects of the interventions were assessed through questionnaires and tests administered at three time points to monitor changes in both outcome variables. The respective and relative effects of the interventions were determined by two‐way mixed design ANOVAs and post‐hoc pairwise comparisons. Results indicated that all three motivational treatments produced significantly positive effects on both outcome variables immediately following the interventions. All three motivational treatments sustained their positive effects on L2 learning effort, but only the vision‐based and combined treatments maintained their impact on L2 achievement. Moreover, all motivational treatments had comparable effects on L2 learning effort both during and 4 months after the interventions. Although all interventions were similarly effective on L2 achievement during the treatment period, the vision‐based and combined interventions outperformed the traditional MS intervention in terms of sustained effects.

<p>Microsoft just released the third edition of its AI in Education Report, surveying more than 3,000 students, educators and leaders across six countries. Ira Apfel sat down with <a href="https://conference.iste.org/2026/program/search/detail_presenter.php?id=118445480">Pat Yongpradit</a>, general manager of global education and workforce policy at Microsoft, which sells AI tools including Copilot to schools, at <a href="https://conference.iste.org/2026/">ISTELive 26</a> in Orlando, to talk through what the data reveals about daily use, trust, training gaps, and where the policy backlash against AI in schools may be headed next.</p><h2>Pat Yongpradit on Adoption, Trust and Cheating</h2><p>Yongpradit walks through why daily AI use in schools still lags far behind overall adoption, even though nine in 10 educators, students and leaders report having tried the tools at least once. He addresses the recent drop in student optimism, framing it as a predictable dip on a familiar technology adoption curve rather than a warning sign.</p><p>The conversation also covers the wide gap between how much training school leaders believe their staff have received and what teachers and students say they have actually experienced. Yongpradit also shares his candid take on academic integrity, arguing that the panic around cheating has more to do with long-standing temptation than with any single tool.</p><p>Listen to the episode:</p><h2>Stories Mentioned in This Episode</h2><p><a href="https://www.linkedin.com/posts/patyongpradit_2026-ai-in-education-report-ugcPost-7475673037405679616-_qCk/"><u>2026 AI in Education Report</u></a> by Microsoft</p><p><strong>This Week with EdSurge</strong> is a weekly podcast from <strong>EdSurge</strong>. Subscribe to <a href="https://www.edsurge.com/newsletters">EdSurge newsletters</a> for more news and analysis on education and technology.</p>

<p>Artificial intelligence giant Anthropic today upped the ante in its quest to win the increasingly competitive market for AI in education by debuting <a href="https://claude.com/solutions/teachers"><u>Claude for Teachers</u></a>, a free large language model designed for U.S. K-12 educators.</p><p>Claude for Teachers includes a library of teaching skills and a “direct connection to evidence-based curricula, mapped to academic standards in all 50 states,” the company <a href="https://www.anthropic.com/news/claude-for-teachers">said</a> in a statement.</p><p>The move by Anthropic is the latest in a series of initiatives by tech giants including <a href="https://openai.com/index/chatgpt-for-teachers/">OpenAI’s ChatGPT for Teachers</a>, <a href="https://elevateforeducators.microsoft.com/">Microsoft Elevate for Educators</a>, and <a href="https://edu.google.com/learning-center/google-ai-educator-series/">Google AI Educator Series</a> to earn market share in an increasingly competitive AI and edtech space.</p><p>“We built Claude for Teachers to close the distance between what the evidence recommends and what a teacher’s week allows,” the Anthropic statement continued.</p><p>Although some educators are skeptical of AI usage in the classroom, particularly in early childhood education, Anthropic cited<a href="https://scale.stanford.edu/research-in-action/understanding-evidence-base-ai-k12-education"> <u>Stanford research</u></a> suggesting that AI tools, when designed and used correctly, can aid teachers and improve student learning.</p><p><strong>Privacy and Security Measures</strong></p><p>Claude for Teachers has access to academic standards in all 50 states, so it can create lessons that are “scaffolded and aligned to teaching standards.” The tool’s library of skills, co-developed with Learning Commons, was piloted with teachers in Prospect Schools in Brooklyn, New York, and other sites. Anthropic said it plans to evaluate Claude for Teachers in the Detroit Public School

<p>In 2010, a young woman walked into my office at the childcare center I directed in Arizona. She was nervous. She didn’t have experience in early childhood education. She just needed a job.</p><p>In most centers, that’s where the story would end. Instead, I offered her a working interview — two observational hours in an infant classroom. When she came back into my office, she was beaming. “I love this,” she told me. “Just give me a chance. I’ll learn.”</p><p>Her name is Lindsay. Fifteen years later, she’s still teaching.</p><h4>Seeing the Educator</h4><p>Lindsay’s story isn’t just about passion or perseverance; it’s about support. We made a deliberate choice — over and over again — to see her as a whole person first and an employee second. We figured out scheduling so she could get her Child Development Associate (CDA) credential. We found coverage when she needed practicum hours elsewhere. I wasn’t there for every step of Lindsay’s journey that followed, but she and I have stayed in touch throughout all these years. She earned her associate’s degree and then her bachelor’s degree; she grew from part-time infant teacher to lead teacher to program coordinator.</p><p>Lindsay’s story shows us what happens when an educator is truly seen — something our field has yet to get right. What gets overlooked most in conversations about the quality of early childhood education is that educators are engineers. Every learning opportunity a young child experiences is designed, built and brought to life by a teacher. There is no curriculum three-year-olds activate for themselves. Every moment of discovery, every language-rich exchange and every carefully scaffolded small group experience is built by someone. A teacher looks at a group of children, weighs their individual needs, considers the family’s hopes, aligns these needs and hopes to learning objectives and makes a decision about what to put in front of those kids at that moment. And they do this all day long.</p><p>These edu

Douglas Fisher and Nancy Frey believe professional learning communities can fulfill their promise as powerful drivers of equity, efficacy, and lasting school improvement The idea of professional learning communities (PLCs), groups of educators collaborating to improve their learning and the outcomes for students, has been around for many decades (Hord, 1997). In fact, the original [&#8230;] The post The Next Generations of PLCs appeared first on Language Magazine .

<p>In the span of just two years, artificial intelligence has moved from an emerging curiosity to an operational reality in American K–12 classrooms. Students are using it to draft essays and build interactive study apps. Teachers are using it to generate lesson plans, differentiate instruction and develop assignments. Administrators are using it to summarize data, create chatbots and organize teams. And most school districts are scrambling to update or create policies that reflect the ever-changing world of AI. But what does an AI policy landscape look like? How many districts have formal AI policies at all? What do those policies say? And what does the distribution of approaches reveal about the state of readiness, equity and strategic thinking in K–12 education? To answer those questions, we built the<a href="https://policy.aiandeducationstudio.com/"> <u>AI School Policy Database</u></a> by using a five-level AI policy continuum to code 122 districts and schools across 38 states and then analyzed the results. What we found is a snapshot of a field that is neither panicking nor confidently leading: it is waiting, watching and managing uncertainty one teacher-directed decision at a time.</p><h2>What This Snapshot Tells Us</h2><p>The dataset does not capture every district/school in America; it is a structured sample. But the patterns it reveals are consistent and interpretable. Taken together, they point in the same direction.</p><ul><li><p><strong>Most districts/schools are reactive. </strong>The dominant posture is conditional permission, teacher-delegated decision-making and a wait-and-see attitude toward systemic AI integration.</p></li><li><p><strong>Some districts/schools are still trying to stop the clock. </strong>Nearly 30% are actively restricting or prohibiting AI use, an approach that is increasingly difficult to sustain as student access to AI tools extends far beyond school networks.</p></li><li><p><strong>Only a small fraction is daring to lead.</str

Generative artificial intelligence (GenAI) enables foreign language learners to extend their learning beyond formal instruction and develop their autonomy. However, research has not adequately examined how learners regulate their learning with GenAI or how their GenAI literacy and multiple types of interactions influence their self-regulated learning (SRL) in GenAI-supported informal digital language learning settings. We address this gap by analyzing data from 343 Chinese university foreign language learners through partial least squares structural equation modeling (PLS-SEM) and artificial neural networks (ANN). PLS-SEM showed that awareness and evaluation significantly predicted GenAI-supported SRL (GenAI-SRL), whereas usage and ethics did not. Student–student, student–teacher, and student–GenAI interactions emerged as facilitators of GenAI-SRL. These three interaction types also significantly influenced most GenAI literacy dimensions, with three of them predicting awareness, usage, and evaluation, while only student–student and student–GenAI interactions significantly predicted ethics. Mediation analysis demonstrated that awareness and evaluation partially mediated the effects of student–student and student–teacher interactions on GenAI-SRL. The mediating pathways through student–GenAI interaction were not significant. ANN models identified student–student interaction as the strongest predictor of GenAI-SRL. These findings inform GenAI literacy development and the design of systems to support GenAI-SRL in informal learning contexts.

The growing body of research on the effects of computerized dynamic assessment (C-DA) on second language (L2) learning underscores the need for a comprehensive research synthesis to identify future research directions and inform the application of C-DA in L2 educational contexts. This meta-analysis employed a three-level modeling approach to examine the effectiveness of C-DA in improving L2 learners’ performance. It synthesized 27 effect sizes from cake format designs, in which mediation is embedded within the test sequence, and 24 effect sizes from sandwich format designs, where mediation is delivered between a pretest and a posttest, across 35 studies published between 2000 and May 27, 2025. This study also investigated the key variables that moderate C-DA effectiveness. Findings reveal large, significant positive effects of both the cake and sandwich formats on L2 performance improvement (cake format: g = 2.120, p < .001; sandwich format: g = 1.676, p < .001), with the cake format tending to yield larger effect sizes. This may be because the cake format captures gains during mediation, whereas the sandwich format reflects post-mediation outcomes. Moderator analyses show that the number of items, test content, and learners’ first language affect C-DA effectiveness in promoting L2 performance. Drawing on the synthesized findings, this study contributes to theoretical, methodological, and technological understandings of C-DA and offers suggestions for future research in this domain.

This study explores the experiences of an undergraduate multilingual international student in becoming a science writer at a large public university. Taking the lenses of identity and investment, language socialization and multiliteracies, we address the question: How does a college‐aged multilingual science student navigate disciplinary writing over time? Interviews using talk around text based on the student's disciplinary writing samples over 3 years tracked his unique learning progress. Findings reveal he employed strategies including translingual, multimodal, and interdisciplinary practices and used GenAI to participate in disciplinary writing tasks, particularly when instruction and feedback were limited or harmful. The student gradually shifted from seeing disciplinary writing as a high‐stakes evaluation to understanding it as an agentive process of enacting future professional identity. By centering the lived experience of a multilingual writer, the study offers insights into the challenges and possibilities of disciplinary writing development in higher education (HE) classrooms. It provides a window into how students demonstrate resilience and agency to support learning, build disciplinary knowledge, and envision professional futures. The findings call for more linguistically responsive pedagogical practices in HE science, technology, engineering, and mathematics fields that go beyond writing as a tool for assessment, but rather a vital space for disciplinary engagement and identity formation.

In this collaborative autoethnography, we, as three university‐based language teacher educators in Türkiye, explore how our community of practice (CoP) acted as a catalyst in navigating our identity tensions, boosting our wellbeing and contributing to our agency. Grounded in Wenger's (2009) framework of CoP, we embarked on a critical journey of navigating the tensions between our aspired and assigned identities as teacher educators in a dialogic and reflective third space of a CoP. The collegial space of our CoP enabled us to enact our agency and nurtured our wellbeing, embracing us in a safety net of solidarity and empowering us professionally in neoliberal academia. Conceptualizing the identity–wellbeing–agency triangle as a mutual, recursive and interdependent process nested in a CoP, this study showcases how LTEs reclaim their legitimacy, nourish their wellbeing and enact their agency in a safe dialogic space. Such a reconceptualization may bring new insights to LTEs in different contexts.

<p>Professional development is crucial in ensuring teachers can put AI to work effectively. But a <a href="https://journals.sagepub.com/doi/10.1177/87568705251387039?__cf_chl_f_tk=CAZIy4i9YfjGxGZv7zfF4.IgSEpqSim3svGIjQdwFNE-1783002978-1.0.1.1-oZKjnpZ6Usv2XLN4p44VfwNtT9YFjnueGWjzNT3pBX8">recent study</a> from Texas Tech University found that PD around AI may not be readily available in rural schools. Another <a href="https://www.mdpi.com/2071-1050/17/6/2748#Results">study</a> compared elementary school students in rural areas to students in urban areas and found that rural students scored “statistically significantly lower” in Information and Communications Technology access and literacy, resilience, and online learning capabilities.</p><p>“The resources are limited. There is not much support out there for our rural educators,” says Nikkolina Prueitt, a co-author of the study. For rural schools to get the most out of AI, “we will need to build that knowledge base.”</p><h2>Closing the Gap</h2><p>AI has the potential to give rural teachers a pedagogical boost. It can provide instructional support, “like creating differentiated instruction, adapting lessons, drafting individualized education plans,” Prueitt says.</p><p>And AI can expand rural students’ understanding of the world, says Amanda Robinson, an elementary teacher at Pikeville Elementary, a Title I school in Eastern Kentucky. “AI opens the students’ horizons.”</p><p>In a rural community, AI can students to “experience new learning, outside of their communities,” said Dr. LeeAnn Lindsey, director of edtech and innovation at Northern Arizona University. But she sees rural schools struggle to embrace that potential, due in part to a lack of in-house expertise. “Our big urban and suburban school districts, they have technology integration coaches who have been diving into the AI work for the past three years,” she says. “Rural school districts often don’t.”</p><p>To help close the gap, Northern Arizona last fall le

Technological advancements have influenced note‐taking practices in classrooms as well as their treatment in standardized language assessments. Many high‐stakes English listening tests (e.g., TOEFL iBT, IELTS) include varying note‐taking guidelines, often established without empirical support. This study examined the effects of note‐taking modes on listening performance and note content. A total of 305 L1‐Korean L2‐English adults were randomly assigned to handwriting ( n = 102), typing ( n = 102), or no note‐taking ( n = 101) conditions and completed TOEFL iBT lecture comprehension tasks. Linear regression results revealed no significant differences in overall test scores across modes. Uniform Differential Item Functioning (DIF) analyses indicated that one item favored handwriting and another favored typing, while non‐uniform DIF emerged for the same items only among lower‐ability participants. When examining note‐taking features, no significant differences were found in word count or translanguaging across modes. However, handwritten notes contained more information units, verbatim transcription, and nonlinguistic elements. The overall lack of significant performance differences, together with finer‐grained differences at the item and note‐content levels, calls for test developers to reconsider note‐taking policies in terms of test validity and fairness. Pedagogical implications are discussed, highlighting the importance of granting students autonomy to choose note‐taking modes and providing instructional sessions to experience various note‐taking strategies.

This study explores teacher code alternation in an English medium instruction classroom at a higher education institution in Sri Lanka. Acknowledging that code alternation is common in multilingual settings, this case study highlights its various functions, motivations, and implications for classroom purposes, addressing a gap in research within the Sri Lankan higher education context. Data were collected through classroom observations, transcriptions of recorded lessons, and semi-structured interviews. The analysis revealed five distinct functions of code alternation that aid the learning process. As a strategic linguistic tool, code alternation enhances comprehension, manages classroom dynamics, and builds rapport. Specifically, it serves to alleviate the stress of using English, foster a comfortable environment, and clarify complex ideas, thereby underscoring the importance of teacher–student perspectives on the use of code alternation for inclusive, multilingual education.

Second language (L2) grit, a noncognitive ability that underlies the pursuit of language goals, has attracted growing scholarly attention. However, the grit–goal relationship—including why certain goals are set and how grittiness relates to their pursuit—remains underexplored, as L2 grit has largely been examined in relation to researcher‐selected proxies of goals. In this context, this mixed method study applied the goal self‐concordance framework (Henry, 2021) to analyze how goals aligned with one's sense of self and future direction are pursued, adjusted, and sometimes abandoned and how L2 grit relates to these processes. Questionnaire and interview data were collected from fourth‐year English majors in Japan ( N = 8) to examine how L2 grit related to their goal development histories. The findings suggest that (a) L2 grit levels do not always correspond to the attainment of self‐concordant goals and (b) attention to multiple layers of goals may be important for understanding which aspects of L2 grit are associated with the pursuit of self‐concordant goals.

The Habla Futures Bilingual Conference is a free, international virtual gathering for bilingual education researchers, policymakers, and educators from across the globe, hosted by the Habla Futures Initiative in partnership with the UCI Bilingualism Matters California Initiative, OCDE Project GLAD, the California Department of Education, and Californians Together. Date: Saturday, July 11, 2026Time: 9:00 AM [&#8230;] The post Free Virtual Conference on Bilingualism on July 11 appeared first on Language Magazine .

<p>As a current high school student, the first thing I noticed about chemistry was that it was not only hard because of math or vocabulary. It was hard because so much of it was invisible.</p><p>In class, I could write a chemical formula. I could memorize that a molecule had a certain shape or that a reaction moved in a certain direction. But much of chemistry did not feel like something I could touch, move, test, or explore. The most important parts happened in a way I would never see.</p><h2>A Two-Dimensional Perspective</h2><p>Chemistry is a subject built on relationships. Atoms connect, electrons move, bonds form, molecules bend, acids and bases shift equilibrium, gases respond to pressure and temperature, and reactions balance because matter must be conserved. When those ideas stay flat on a page, students memorize answers without understanding the system behind them.</p><p>As a rising 12th-grade student at Suitland High School in Prince George’s County, Maryland, I set out to build a solution because I wanted a serious chemistry workspace students like me could use. I wanted something visual and interactive that could run on school devices without complicated setup — without the need for students to create accounts, download resources, or collect student data. I also wanted something that could open in a browser and allow students to begin learning right away.</p><p><a href="https://atomency.com/">Atomency</a> started from a simple question: What would chemistry look like if students could build, manipulate and test ideas instead of only reading about them?</p><p>I began by building a workspace where students could create molecules and see structure-based information. From there, I added VSEPR-style analysis so students could connect formulas to molecular geometry instead of treating shapes as something to memorize from a chart. Then I kept expanding it: I incorporated reaction simulations, nuclear decay tools, kinetics tool, acid–base and pH tools, gas-law mo

Although many teacher/researchers have expressed enthusiasm about the benefits of digital multimodal literacies, empirical evidence of the value of engagement with multimodal ensembles for L2 students as authors and readers is limited. This study compared the quality of digital multimodal composing (DMC) and monomodal writing and explored the impact of both on readers' new knowledge building. Fifty Korean university EFL students were assigned to two groups: DMC and monomodal writing. Both groups completed causal analyses and then reviewed their classmates' work, reporting on what they, as reader‐viewers, learned about the composers' topics, in addition to responding to a perception survey. Thus, students had the opportunity to be both producers and consumers of digital multimodal or monomodal texts depending on their group. Students' DMC and monomodal compositions were scored using a multitrait rubric. Survey responses were analyzed for perceptions of new knowledge gained, whereas comments on fellow students' projects were analyzed for the amount of new informational and issue‐oriented knowledge gained as DMC and monomodal essay readers. The findings indicated that the quality of DMC and monomodal writing conditions was similar. With respect to reader perspectives, there was also no significant difference in the perception of new knowledge gained and reports of new issue awareness for the two conditions; however, DMC outperformed monomodal writing regarding reader‐reported new informational knowledge units gained. The findings suggest that although the student‐participants did not perceive DMC as having an advantage over monomodal composing for conveying new information and issue awareness, the students did appear to be able to produce and consume (as reader‐viewers) multimodal ensembles in such a way that new knowledge, measured in information knowledge units, became more salient for them in DMC than in traditional monomodal texts.

This paper focuses on integration in the data analysis of a qualitatively oriented mixed methods study, illustrating this process through a worked example of interactions with social actors shaping a TESOL graduate's professional experiences. The paper outlines our analysis of visual and textual data from interviews that employed an embedded social network data elicitation tool. We developed and integrated layered descriptions and interpretations using the following strategies: (1) quantitative indicators and qualitative structural analysis to detail network features of visual data and facilitate further analysis and (2) reflexive thematic analysis procedures and qualitative comparative analysis strategies to explore patterns within the textual data. These analyses were brought together to develop a holistic understanding of network features and associated practices, as well as their influence on the TESOL graduate's professional experiences. Finally, this paper highlights how the application of multiple data analysis strategies can support the development and integration of layered inferences, thereby making explicit the complex process of knowledge construction in TESOL within a qualitatively oriented mixed methods approach.

Learners of a second language often vocalize newly learned items, and previous studies have shown that this practice facilitates the learning of single words and multiword expressions. However, these effects have been demonstrated only in mixed-list designs, where a single list of items is learned using multiple learning methods, and have not been tested in pure-list designs, where each list is learned with one consistent method. Moreover, prior research has relied almost exclusively on written recognition tests, leaving spoken-modality tests and recall measures largely unexamined. This study employed a within-participant, pure-list design in which each list was associated with a single learning condition, and each participant studied a total of four lists. The participants were 68 Japanese learners of English. Participants learned 64 verb–noun idioms assigned across four active learning conditions (Listening, Repeating, Reading while Listening, and Reading while Repeating), with 16 items in a fifth condition serving as a no-exposure Control condition. Learning outcomes were assessed with three test types (form recall, form recognition, and meaning recognition) administered in two modalities (written vs. spoken). In the written test modality, form-recognition performance was higher for the Repeating and Reading-while-Repeating conditions, whereas in the spoken test modality, the strongest form-recall gains were produced for the Reading-while-Repeating condition. These results highlight modality-specific benefits of vocalization and are discussed in relation to the strong-trace account and the transfer-appropriate processing framework.

The present study investigated the effects of self-regulated strategy-based speaking instruction on English-as-a-foreign-language learners’ speaking performance and strategy use. Despite extensive research on self-regulated learning in various educational contexts, its application to second language speaking development remains underexplored. This explanatory mixed-methods quasi-experimental study employed self-report questionnaires, speaking assessments, and semi-structured interviews for data collection. A linear mixed-effects model results revealed that the experimental group ( n = 54, receiving the self-regulated learning strategy-based intervention) demonstrated significantly greater improvement in speaking performance compared to the control group ( n = 53, receiving traditional instruction), with effects sustained at delayed post-testing. Quantitative analyses further indicated significant increases in participants’ use of self-regulated learning strategies. Qualitative findings corroborated these results, revealing more frequent, deliberate, and sophisticated strategy application among intervention participants. Interview data also suggested differential intervention effects based on learners’ initial speaking proficiency levels. These findings underscore the efficacy of explicitly integrating self-regulated learning strategies into speaking instruction to enhance learner autonomy and speaking performance in English-as-a-foreign-language contexts.

Although the learning benefits of peer feedback in English-as-a-foreign-language writing instruction have been well recognized, the process and effects of peer feedback are largely reliant on teacher agency in conducting such activities. However, inadequate research has been conducted on this issue, leaving the dynamic features and causes of teacher agency in peer feedback largely unknown. Drawing on an ecological perspective, this comparative case study examined how two English-as-a-foreign-language teachers from two universities enacted agency in organizing peer feedback activities in their writing instructions. Multiple data including classroom observation, interviews and relevant teaching documents were collected and coded for themes. The study revealed that both teachers exerted dynamic agency in creating peer feedback experiences for students, but showed considerable differences before, during and after peer feedback. Overall, the novice teacher shifted from showing proactive agency to constrained agency, whereas the experienced teacher moved from demonstrating automatic agency to autonomous agency. This stark contrast was shaped by the synergism of different temporal affordances and constraints in relation to individual and contextual factors. The study highlights the need to provide tailored support to enhance teacher agency in dealing with possible challenges in facilitating students’ experiences with peer feedback.

This mixed-methods study examined how signed literacy instruction is currently taught in United States deaf education classrooms (PreK-3) and to what extent that instruction reflects evidence-based composing instruction. Signed literacy instruction means teaching students to view, analyze, and compose texts in a signed language. A composing-forward approach treats students’ production of signed texts as the central instructional activity, with language skills taught in service of that composition. This approach is the core of Strategic and Interactive Signing Instruction (SISI), a pedagogical framework for signed literacy. Participants were 38 teachers across 11 deaf education programs. Each teacher completed a survey on their instructional practices and was observed teaching lessons selected to demonstrate support for deaf children’s signing skills. Survey and observation data were analyzed separately, then compared for agreement, complementarity, and divergence. Nine of 38 teachers included signed composition in their observed instruction. Of those 9, only 6 (16% of the full sample) met the evidence-based composing instruction threshold. These 6 teachers consistently treated signed language as the instructional target and embedded strategy, interactive, and language instruction within a composing cycle. The remaining teachers taught isolated grammatical features in signed language or used signed language to scaffold print literacy. Findings suggest that signed composition appears in a minority of classrooms, and that even when it appears, it is not consistently taught with the instructional features evidence suggests it requires.

Many learners of English as a foreign language struggle to develop second language speaking fluency because of limited opportunities for authentic communication with linguistically diverse English users in English as a multilingua franca context. This study aims to address this problem by investigating the effects of communication strategy instruction, an approach believed to facilitate communication in English as a multilingua franca contexts. This study compared two approaches of communication strategy instruction, explicit and implicit, combined with low-immersion virtual reality. Over 11 weeks, 159 Vietnamese undergraduates were randomly assigned to one of three instructional conditions: Explicit + virtual reality, Explicit + non-virtual reality, and virtual reality Implicit. Data sources were video recordings of pre-, post-, and delayed speaking tests, as well as learners’ reflections. The data were analyzed using the ELSA Speech Analyzer to measure second language speaking fluency and qualitative content analysis to explore learners’ perceived affordances and challenges of virtual reality implementation. Results showed that the Explicit + virtual reality group achieved the greatest gains in second language speaking fluency, whereas both virtual reality groups (explicit and implicit) outperformed the non-virtual reality group in confidence, perceived language skills, and transcultural competence. On the delayed test, although no significant difference was found between the two virtual reality groups, all three groups showed improved second language speaking fluency, with both virtual reality groups still outperforming the non-virtual reality group. The study introduces Emergent-Dialogue Virtual Exchange, a new pedagogical model grounded in unscripted, spontaneous communication via virtual reality. Emergent-Dialogue Virtual Exchange positions low-immersion virtual reality as a scalable, contextually responsive space for English as a multilingua franca communication. These findings underscore the pedagogical potential of combining communication strategy instruction with virtual reality to support fluency, adaptability, and intercultural competence in the English as a multilingua franca context, bridging the gap between the classroom and the complex realities of global English use.