Responses to generative AI in higher education assessment have focused on the architecture of AI use: secured and open task designs, permission scales, declarations and process evidence. These approaches specify where and how AI may be used, but leave two harder questions unresolved: what capability an assessment claims the learner has, and what evidence could warrant that claim. This article argues that credible evidence of learning in AI-mediated assessment cannot be inferred from the artefact alone, but depends partly on the quality of the learner’s orchestration of the resources that shape it. Learning Orchestration is defined as the purposeful and accountable coordination of distributed cognitive, social, informational and technological resources, through cognitive-metacognitive, epistemic and ethical judgement, to advance learning, develop disciplinary judgement and produce work, decisions or claims that the learner can explain, justify and defend. The article distinguishes Learning Orchestration from self-regulated learning, evaluative judgement, AI literacy, epistemic agency, academic integrity and assessment validity. It contributes a learner-side account linking the quality of resource coordination to the claims about learning that assessment evidence can warrant. The resulting Assessment Claims Framework distinguishes independent capability, AI-supported orchestration and accountable verification of AI-shaped work as claims requiring different forms of evidence. Without these distinctions, universities risk certifying fluent AI-shaped performance while overstating what learners understand, can do independently or can responsibly verify and defend. Credible AI-mediated assessment therefore depends not only on regulating AI use, but on teaching, eliciting and judging the quality of learners’ orchestration.

This paper reports a mixed-methods study (N = 200; 100 secondary students, 100 teachers; 80 qualitative interviews) examining why both students and teachers remain in educational systems they independently acknowledge are failing them — a pattern consistent with status quo bias and loss aversion operating at the institutional level. One-sample tests against the scale midpoint confirm that both populations score significantly below the neutral point on engagement, creativity, and autonomy (all p < .001), while both project significantly above-midpoint effectiveness under redesigned conditions — producing a statistically significant projected capacity gap (students: t(99) = 4.72, p < .001; teachers: t(99) = 7.91, p < .001). Qualitative coding achieved good inter-rater reliability (κ = .74) and found zero defenders of the traditional system across 80 interviews. The qualitative data suggest that fear rather than conviction sustains institutional inertia — 46% of students and 33% of teachers expressed ambivalence rather than outright resistance, a finding with direct implications for implementation strategy: if the dominant orientation is ambivalence rather than resistance, change agents should invest in making transition visible and safe rather than constructing arguments for why change is necessary. These findings reframe the educational change problem: the barrier to transformation is not ideological conviction in favour of the traditional system but a well-documented psychological preference for the known over the unknown, with direct implications for how educational change theory should approach implementation.

Previous evaluations of school textbooks have predominantly focused on aggregate overall averages, thereby overlooking critical intra-textbook heterogeneity. This study aimed to identify latent profiles of cognitive involvement and assess the instability of instructional design within Iranian senior high school history textbooks. Utilizing a secondary data analysis of datasets derived from the Comparative Adjudication Protocol (CAP)—encompassing 61 lessons across 4 core textbooks—cognitive involvement indices for text, images, and questions were calculated using the William Romey (1968) method. To quantify internal heterogeneity, the Coefficient of Variation (CV) and Levene’s test were utilized, while K-means clustering with Euclidean distance (Hair et al., 2010) was employed to uncover latent profiles. The findings revealed that the History 3 textbook exhibited the highest instructional irregularity ("CV"=144.50% in the question component), whereas the Contemporary History of Iran textbook possessed the most uniform structure ("CV"=81.58%). Levene’s test confirmed a statistically significant difference in variances across the textbooks specifically within the question component (p<0.01). Furthermore, cluster analysis uncovered four distinct structural profiles, revealing that a meager 11.5% of the analyzed lessons fell into a structurally "balanced" cluster. These findings demonstrate that internal content dispersion, particularly in higher grade levels, poses a more severe pedagogical challenge than low average involvement. Consequently, future curricular interventions must prioritize variance reduction over a simple inflation of baseline averages.

Previous evaluations of school textbooks have predominantly focused on aggregate overall averages, thereby overlooking critical intra-textbook heterogeneity. This study aimed to identify latent profiles of cognitive involvement and assess the instability of instructional design within Iranian junior high school social studies textbooks. Utilizing a secondary data analysis of datasets derived from the Comparative Adjudication Protocol (CAP)—encompassing 44 lessons across 2 core textbooks (Social Studies Grade 7 and Social Studies Grade 8)—cognitive involvement indices for text, images, and questions were calculated using the William Romey (1968) method. To quantify internal heterogeneity, the Coefficient of Variation (CV) and Levene's test were utilized, while K-means clustering with Euclidean distance (Hair et al., 2010) was employed to uncover latent profiles. The findings revealed that the Social Studies Grade 7 textbook exhibited the highest textual irregularity (CV = 186.84%), whereas the Social Studies Grade 8 textbook demonstrated more moderate text heterogeneity (CV = 134.78%). Levene's test confirmed a statistically significant difference in text variances across the two textbooks (p = 0.028), indicating that Grade 7 social studies is structurally more fragmented than Grade 8. However, cluster analysis uncovered three distinct structural profiles, revealing that 50.0% of the analyzed lessons fell into an "Active-Question Dominant" cluster, while 31.8% were classified as "Fully Passive" and 18.2% as "Hyper-Active." These findings demonstrate that middle school social studies textbooks—particularly at Grade 7—exhibit extreme textual heterogeneity and a persistent question-text disjuncture, where end-of-chapter tasks demand high analytical engagement while the textual content remains predominantly passive. Consequently, future curricular interventions must prioritize structural alignment between narrative content and assessment tasks, alongside systematic reduction of textual variance across grade levels.

Institutional responses to generative AI have largely relied on detection. Detection has failed, and Papers One and Two of this series argue that the failure is structural rather than technical: a finished piece of writing does not carry reliable evidence of the process that produced it, and no improvement in detection tools alters this. This paper proposes a different response. Rather than attempting to establish after the fact whether a student used AI, it redesigns the assessment so that success depends on a capacity AI cannot supply on the student's behalf. The instrument is oral defense, though not as a means of detecting deception. Oral defense is analyzed here as a forcing function: it alters what a student must do in advance in order to succeed, and therefore shapes behaviour before the assessment rather than adjudicating it afterward. The paper develops three connected components. The first is a research protocol that assigns AI a narrow role, the location of material, and reserves for the student the two operations that constitute ownership: verifying what each source argues, and constructing a position from the verified set. The second is a rule of exclusion: a claim whose primary source the student cannot obtain and read may not function as a premise in the argument, even in hedged form. The third is a two-probe defense that tests these two operations separately, incorporating a live friction point that cannot be anticipated and scripted. Ownership, on this account, is not authorship of the prose. It is verified provenance together with demonstrated independent synthesis. A student who used AI extensively and satisfies both conditions passes; a student who used no AI and satisfies neither fails. This is not a loophole in the framework but its central commitment. The paper is addressed to the secondary classroom rather than the university lecture hall, and the scope is integral to the argument rather than incidental. The mechanism developed here depends on an instructor who knows their students across a term, on class sections of roughly twenty to thirty, and on a developmental stage at which the knowledge being assessed is still forming rather than already established and merely being applied. Existing AI-resilient assessment frameworks, examined in Section 5, are designed for university cohorts in the hundreds, where that knowledge is assumed largely in place. The two scopes are not competing solutions to a single problem; they are solutions to different problems, and this paper claims only its own.

The global doctoral economy is undergoing profound structural transformation driven by geopolitical competition, technological change, artificial intelligence, industrial policy and strategic innovation (OECD, 2025; UNESCO, 2021). Doctoral education increasingly functions at the intersection of national innovation, scientific capability, technological sovereignty and economic resilience rather than solely as a mechanism of academic credential formation. Contemporary reforms consequently oscillate between two competing institutional logics: publication-driven academic productivity and innovation-oriented industrial relevance. Whilst both respond to legitimate policy pressures, neither adequately addresses the long-term problem of preserving knowledge continuity, methodological integrity and institutional resilience under conditions of geopolitical uncertainty. This paper argues that doctoral policy has become constrained by a false binary between publication accumulation and innovation acceleration. Rather than interpreting doctoral education as either a publication economy or an innovation economy, it introduces the concept of Regulated Doctoral Economies, developed within the broader Regulated Accumulation Policy (RAP) framework. Doctoral systems are conceptualised as dynamic knowledge ecosystems whose resilience depends upon regulating expansion through institutional constraint rather than publication metrics or commercial outputs alone. Drawing upon comparative policy developments across China, India and Europe (2024–2026), the study integrates higher education governance, innovation systems, research integrity, comparative policy analysis, Fisherian variance and volatility assessment to examine the institutional conditions under which doctoral systems preserve equilibrium whilst avoiding both publication inflation and innovation concentration. The analysis further explores the risks of policy imitation across South Asia, arguing that resilient doctoral systems emerge not through maximising publications or products independently, but through maintaining a dynamic balance between scholarly originality, supervisory capacity, methodological rigour, industrial relevance and long-term knowledge continuity. The paper concludes by proposing the Regulated Doctoral Framework as a comparative policy architecture for strengthening doctoral resilience across increasingly geopolitically fragile frontiers, positioning doctoral education as a critical component of scientific sovereignty, institutional stability and long-term civilisational knowledge continuity.

Self-Regulated Learning (SRL) is a critical factor in successful foreign language learning, particularly in Arabic, which is characterized by high linguistic complexity and limited exposure outside the classroom. This study aims to comprehensively examine the level and characteristics of self-regulated learning among students enrolled in an Arabic Language Education program within the context of higher education. A quantitative approach with a descriptive–explanatory design was employed. Data were collected using a five-point Likert-scale SRL questionnaire encompassing eight dimensions: learning planning, language learning strategies, self-monitoring, regulation of motivation and perseverance, time management and learning environment, utilization of learning resources, help-seeking and feedback, and self-reflection and evaluation. The participants consisted of 177 active students from the Arabic Language Education program. Data analysis was conducted using descriptive statistics and the Kruskal–Wallis test. The findings indicate that students’ overall SRL levels range from moderately high to high; however, considerable variation exists across dimensions and academic semesters. The highest levels of SRL were observed among students in the final semesters, whereas students in the early and middle semesters demonstrated relatively lower levels. No statistically significant gender differences in SRL were identified. The most dominant dimension was the utilization of learning resources, followed by self-monitoring and motivational regulation, while learning planning and Arabic language learning strategies emerged as comparatively weaker dimensions. These findings highlight the need to strengthen operational planning skills and cognitive-linguistic strategies to promote a more balanced and sustainable development of SRL among Arabic Language Education students.

Intelligent tutoring systems (ITSs) are widely used in K-12 education and are known to improve learning outcomes compared to traditional instruction. However, prior work has focused primarily on end-of-test performance, with little attention to learning processes, such as how much students learn at each practice opportunity. In particular, learning rates have been extensively studied in ITS environments but not for paper-based problem solving. We conducted a within-subjects classroom experiment with 97 middle-school students solving matched mathematics problems either with an ITS or on paper. We compared both overall learning gains and process-level learning rates, defined as improvement per problem-solving step. To enable this comparison, we introduce a novel method for converting paper-based work into step-level transaction data compatible with learning-curve modeling. Results show that ITS-supported practice led to significantly higher learning gains and substantially higher learning rates than paper-based practice. While students in the ITS condition exhibited consistent positive learning rates, learning rates in the paper condition were indistinguishable from zero, indicating little measurable learning during problem solving without feedback. These differences were especially pronounced for students with lower prior proficiency. This study provides the first direct comparison of learning rates between ITS and paper-based practice and introduces a generalizable methodology for analyzing learning processes in non-digital environments. The findings highlight the critical role of immediate feedback and adaptive support in enabling learning during practice and support the broader adoption of ITS in K-12 education.

Pattern making is a way of changing body measurements into shapes that can be cut from cloth and stitched into clothes. Many books explain pattern making, but they are often difficult for beginners to understand. This article explains the steps in a simple and clear way. It teaches how to make patterns for common clothes such as shirts, T-shirts, pants, shorts, cargo pants, churidar pants, chudithar tops, blouses, vests, trunks, bras and panties. For each dress type, the article explains what measurements are needed, how to take them, how to use simple formulas, and how to draw the pattern step by step. The article also explains how to draw parts such as sleeves, collars, cuffs, pockets, belts and waistbands. Twenty-three labelled drawings are included to make the steps easy to follow. At the end, common stitching mistakes are shown with simple ways to correct them. This article is useful for students, beginners, home sewers and small tailoring shops who want to learn pattern making in an easy and practical way. This edition extends the adult wardrobe with material written for early childhood education and care (ECEC) settings. It explains how to measure young children safely and respectfully, gives a height-based size chart for ages one to six, and adds two fully worked children’s drafts — an elastic-waist play short and a classroom art smock — that follow the same formula style as the adult blocks. A dedicated section then shows how educators, pre-service teachers and family-engagement programmes can use pattern making: as a source of teacher-made resources (dress-up costumes, doll clothes, puppets and dramatic-play props), as embedded early mathematics (measuring, halving and quartering, symmetry, and turning flat shapes into three-dimensional objects), as support for fine motor development and practical-life dressing skills in the tradition of Froebel and Montessori, and as low-cost professional development for practitioners. Safety rules for children’s garments, including the restriction of cords and drawstrings, are summarized so that everything an educator makes from this guide is safe for young children to wear and use.

Teachers’ professional noticing - the capacity to attend to, interpret, and respond to students’ mathematical thinking has become a central construct in mathematics education research. While substantial scholarship has documented how teachers notice students’ ideas, considerably less theoretical work has examined how professional noticing functions as a mechanism that shapes students’ opportunities for mathematical reasoning. This paper advances a non-empirical, theory-forward analysis that synthesizes research on professional noticing, professional vision, and mathematical reasoning to articulate how teachers’ noticing practices mediate classroom interactions and reasoning processes. Drawing on established conceptualizations of professional noticing (Jacobs et al., 2010; Sherin & van Es, 2009) and theories of mathematical reasoning and discourse (Lobato et al., 2013; Sfard, 2008), the paper proposes an integrative framework linking dimensions of noticing to forms of student reasoning. By reframing professional noticing as a relational and interpretive instructional practice rather than a purely cognitive skill, this paper clarifies its significance for mathematical reasoning and outlines implications for research, teacher education, and professional development.

This study examines the role of prior lexical knowledge in English vocabulary acquisition among Mexican learners.It argues that vocabulary instruction often underestimates the English-related words, cognates, and culturally familiar lexical items that students have already internalized through everyday exposure to media, technology, music, sports, and popular culture.Rather than treating learners as if they had no previous contact with English, the study proposes that teachers should identify and activate this existing knowledge to support more meaningful and efficient vocabulary learning.By connecting new lexical input with words students already recognize and use, instruction can promote comprehension, confidence, and motivation.The study concludes that recognizing learners' linguistic resources has important pedagogical implications for English language teaching in Mexico.

Recent research in applied linguistics has increasingly attended to translanguaging practices in digitally mediated contexts, yet relatively little is known about how translanguaging practices achieve edutainment within the context of the highly compressed short‐form videos as dynamic discourse on Douyin, China's localized counterpart to TikTok. Drawing on translanguaging theory and multimodal discourse analysis, the study analyzes a song video and a role‐play video for English language teaching. The findings show that content creators flexibly draw on diverse multilingual and multimodal resources. The orchestration of these translanguaging resources has created unique ways for the realization of edutainment in the song video and role‐play video, respectively. The findings also indicate that the use of diverse translanguaging resources in each type of video stimulates positive emotions. By providing the reconceptualization of edutainment, this study is positioned as a complementary study grounded in a specific empirical context. This study not only extends translanguaging scholarship into the dynamic context of short‐form videos on Douyin but also re‐examines the relationship between education and entertainment, enriching existing scholarly understandings of edutainment.

This study investigates the impact of an extensive reading program on the English learning motivation of young middle-school learners of English as a foreign language, utilizing the self-determination theory framework. Eighty-one Grade 7 students participated in a semester-long extensive reading program designed to provide supportive conditions by allowing students to select reading materials that matched their proficiency levels and personal interests. Employing a mixed-methods approach, quantitative data were collected through pre- and post-motivation questionnaires measuring intrinsic motivation, external regulation, introjected regulation, and identified regulation, whereas qualitative data were obtained from student interviews and teacher focus groups. This study identified a multi-factor structure of motivation. It also revealed a significant increase in overall motivation, with intrinsic motivation, identified regulation, introjection regulation, and external regulation notably enhanced. Qualitative findings identified key factors contributing to motivational changes, including heightened interest in English classes, English subject, and English language; a greater sense of achievement; improved academic performance; and increased social interactions during reading activities. The study confirms extensive reading as an effective approach for fostering motivation of learning English and suggests its broader implementation among middle school learners of English as a foreign language.

Self-efficacy plays a critical role in language learning motivation, performance, and achievement. However, limited research has examined how different dimensions of English self-efficacy evolve over time and the mechanisms underlying such development in technology use environments. To address these gaps, the present study adopted a mixed-methods design. A total of 102 university students completed questionnaires at three time points over a 15-week period, and 10 participants subsequently took part in semi-structured follow-up interviews. Quantitative findings revealed a significant increase in overall English self-efficacy, as well as in three specific dimensions: speaking, listening, and writing self-efficacy. Qualitative findings further demonstrated how the four sources of self-efficacy, shaped by technological affordances, were associated with students’ efficacy beliefs and helped explain why reading self-efficacy did not show a statistically significant increase within the same learning ecology. The study concluded by discussing pedagogical implications for strengthening reading self-efficacy and optimizing the use of technology in language learning, as well as limitations of the study and directions for future research.

A test taker may underperform for reasons not fully attributable to language proficiency, including psychological or contextual influences such as anxiety or illness. IELTS One Skill Retake (OSR) was launched in 2022, allowing test takers to retake, within 60 days, a single component in which their initial performance may have been affected by extenuating circumstances. This study examines outcomes associated with OSR by analysing test-taking patterns and score changes among over 20,000 OSR test takers from its launch through Spring 2024. It also reports survey findings from 578 OSR test takers on their experiences, including whether they achieved target scores and their perceived reasons for not achieving the desired score on the original full test. Across skills, average OSR component scores were higher than the corresponding scores on the original full test, and overall-band changes among OSR test takers were comparable to those observed among short-interval full-test repeaters (⩽60 days). Survey responses commonly cited factors such as insufficient preparation, stress and anxiety, and fatigue and lack of focus as perceived contributors to underperformance on the initial test. The findings contribute empirical evidence relevant to the interpretation and use of one-skill retake scores and to ongoing discussions of the validity and equity implications of retake policies in large-scale language testing.

The development of research agendas is a crucial component of the process of reclaiming teaching as a site of knowledge production in language education research. However, in the TESOL field, the research agenda (RA) has traditionally been defined in relation to a gap between research and practice. Researchers often justify their work by highlighting significant disciplinary knowledge gaps. Therefore, when developing an RA, we must ask ourselves: (1) Whose interests does the RA serve? (2) Who should be involved in setting the RA? And (3) how can the RA be created and promoted? Engaging proactively with these questions encourages collaboration with practitioners and strengthens the link between research and practice. This approach helps us establish inclusive research agendas that benefit knowledge producers, including teachers, students, policymakers, and researchers. An RA should emerge from a collaborative and consultative process involving both researchers and teachers as practice researchers. Collaborative RA development involves several steps: (1) understanding the needs of the educational context; (2) formulating a preliminary research design and questions that incorporate practitioners' perspectives; (3) conducting a literature review to help researchers and teachers as knowledge producers understand the research's significance; (4) selecting methods aligning with the research design and questions, ensuring consistency with the RA; (5) providing readers with detailed information about the RA to offer context; and (6) publishing in a medium and forum aligned with the RA. This article discusses these steps in detail, outlines strategies for their implementation, illustrating the collaborative process for developing research agendas in TESOL. From a practical perspective, this paper argues for the agentic involvement of both researchers and practitioners in developing RAs, highlighting the connections between their roles in the research process and raising researchers' awareness of research practices that foster a virtuous circle between research and practice.

Generative artificial intelligence (GenAI) is advancing at a remarkable speed, and its promise in transforming current practice in language assessment has been articulated by applied linguistics researchers. An emerging application of GenAI is to integrate the technology into Spoken Dialogue Systems (SDSs) to simulate human interlocutors for the purpose of speaking practice or assessment. Despite rapid technological advances, the issue of whether an AI agent can replace a human examiner in one-on-one, high-stakes interactive speaking tests remains contentious. Building on a lively academic debate on this topic at the 2025 Language Testing Research Colloquium (LTRC) in Bangkok, this Viewpoint presents arguments both for and against this proposition in terms of construct theory, practicality, washback, fairness, ethical uses of AI, and so forth.

<p>For decades, hip-hop artists have been invited into schools as guest speakers, workshop leaders and visiting performers. They’ve mentored young people after school; started nonprofits; taught music production, poetry, history and entrepreneurship; and helped generations of students find their voices.</p><p>Yet many of these artists lack the one credential schools still value most: a bachelor’s degree.</p><p>But what if these artists have been teaching all along?</p><p>A new partnership between <a href="https://collegeunbound.edu/">College Unbound</a> and the <a href="https://www.hiphopeducation.org/">Hip-Hop Education Center</a> aims to answer that question, not by teaching artists how to become educators but by recognizing the expertise they’ve developed through decades of community leadership, cultural work and mentorship.</p><p>The program — a Bachelor of Arts in organizational leadership and change designed specifically for hip-hop educators and cultural leaders — isn’t a separate degree or a simplified version of one. Students complete the same degree requirements as every other College Unbound student. “It’s the same degree,” says College Unbound President Adam Bush. “It’s simply lived differently.”</p><p>The program is about more than hip-hop. It asks a question that reaches far beyond music: Who gets to decide what expertise looks like?</p><h2>A Degree Years in the Making</h2><p>Although the bachelor’s degree officially launched this year, its roots stretch back decades.</p><p>Hip-hop emerged outside traditional institutions, often in response to systems that excluded Black and Brown communities. As hip-hop education gained legitimacy, educators wrestled with a difficult question: How can we preserve the authenticity of the hip-hop culture while creating pathways that allow practitioners to teach at schools and colleges?</p><p>Long before there was a curriculum at College Unbound, there were conversations between Bush, educator and author Sam Seidel, and

While emotional design in educational AI is often presented as a universal benefit, this study challenges that assumption, investigating when, for whom and how it impacts L2 vocabulary learning. This paper reports on the first phase of a larger project, analysing data from a quasi‐experimental study with 147 pre‐service teachers who interacted with either an emotional or a regular AI agent. Data included pre‐/post‐tests, questionnaires and complete AI chat histories. Quantitative analysis revealed no overall difference in vocabulary acquisition or other affective variables. A more nuanced pattern emerged in an exploratory subgroup analysis: contrary to prevailing assumptions, the regular agent—not the emotional one—preserved significantly better learning attitudes ( p = 0.001) and intrinsic motivation ( p = 0.038) for learners with lower baseline proficiency. A possible mechanism is suggested by a significant negative correlation between self‐reported cognitive load and vocabulary scores, found only in the emotional AI group: under high task difficulty, the emotional agent's verbose scaffolding may have added extraneous cognitive load rather than serving as a supportive buffer. We treat this correlational evidence as tentative rather than demonstrated. To explain these divergent outcomes, a qualitative analysis of interaction patterns identified four distinct learner archetypes—from the high‐agency ‘Constructive Inquirer’ to the passive ‘Attentive Pupil’. The findings indicate that the emotional AI's efficacy might be moderated by these profiles; its verbose emotional scaffolding may have added extraneous processing demands for the vulnerable ‘Attentive Pupil’ while being perceived as an inefficient frustration by the task‐oriented ‘Demanding Critic’. The study concludes that a one‐size‐fits‐all approach to emotional AI is suboptimal. Practitioner notes What is already known about this topic Emotional design in digital learning environments is widely considered beneficial for enhancing learner engagement, motivation and positive emotions. Affective AI systems are increasingly being developed for education with the goal of providing personalized and psychologically supportive instruction. Individual learner differences, such as prior knowledge, motivation and confidence, are known to be significant factors that impact the effectiveness of any educational intervention. What this paper adds This paper demonstrates that the benefits of emotional AI are not universal. Its efficacy is highly conditional, with the regular agent—not the emotional one—preserving better learning attitudes and motivation for lower‐proficiency learners under high cognitive load conditions. It tentatively provides a new explanatory framework of four learner archetypes (the Constructive Inquirer, Demanding Critic, Attentive Pupil and Disengaged Bystander) based on observable interaction patterns (agency and questioning effectiveness), which explains why different learners react divergently to the same AI design. It identifies a cognitive load threshold boundary condition: under high task difficulty, verbose emotional scaffolding may exceed learners' working' memory capacity and act as an extraneous processing burden rather than affective support, consistent with experimental evidence on the moderating role of task difficulty in emotional design. It reveals a potential design tension: emotional support is not uniformly beneficial—verbose affective scaffolding intended to help a passive learner (the ‘Attentive Pupil’) may instead add extraneous cognitive load under demanding tasks, while being perceived as an inefficient frustration by a high‐agency, task‐oriented learner (the ‘Demanding Critic’). Implications for practice and/or policy The design and implementation of educational AI must move beyond a ‘one‐size‐fits‐all’ model. Practitioners should select and advocate for tools that can adapt their emotional persona based on user needs, rather than applying

Shadowing is a technique that has been shown to significantly improve English language learners’ (ELLs) oral fluency and comprehensibility. However, previous research showing dramatic gains over time for ELLs has varied in design, with some studies including control groups and others not, thus complicating interpretations of efficacy. These studies have also been conducted in English and other languages with attention to advanced-level ELLs and beginning-level ELLs. Additional research is needed to provide insight into the effect of shadowing on intermediate learners, as well as using treatment and control groups to get a more accurate picture of identified changes in fluency, comprehensibility, accentedness, and imitative ability. A ten-week quasi-experimental study of text shadowing practice was conducted with intermediate-level adult ELLs studying in a large western university’s intensive English program (IEP). Speech samples from the pretest and posttest were rated by non-expert native English-speaking raters for fluency, comprehensibility, accentedness, and the quality of imitative speech on nine-point Likert scales, as has been used in other pronunciation studies. A repeated-measures ANOVA showed that all participants improved significantly from the pretest to posttest in fluency and comprehensibility, with a reduction in their accentedness. While raw gain scores tended to be higher in the treatment group than the control group, the analysis showed no statistical difference between the groups in any of the four measures. The treatment group participants did share positive qualitative feedback regarding shadowing, emphasizing the value of this dynamic activity in an oral communication curriculum.

New research suggests that the more languages people speak, the younger their brains appear. Learning more languages, especially when younger, and achieving fluency in a second language also seem to slow brain aging. The research was presented at the recent Federation of European Neuroscience Societies (FENS) Forum 2026 by Dr Lucia Amoruso from the Basque [&#8230;] The post Bilingualism Delays Brain Aging By 6 Years appeared first on Language Magazine .

<span class="field field--name-title field--type-string field--label-hidden">How to use AI to support reflection and autonomy in teacher education – webinars</span> <span class="field field--name-uid field--type-entity-reference field--label-hidden"><article class="user-profile-card profile"> <div class="user-profile-card-inner"> <div class="avatar"> </div> <div class="username-row"> <div class="username h6"> TeachingEnglish </div> </div> <div class="profile-bio small"> <p>This resource was developed by the TeachingEnglish editorial team.</p> </div> </div> </article> </span> <span class="field field--name-created field--type-created field--label-hidden"><time datetime="2026-07-17T19:28:11+00:00" title="Friday, July 17, 2026 - 19:28" class="datetime">Fri, 07/17/2026 - 19:28</time> </span> <div class="layout layout--onecol"> <div class="layout__region layout__region--content"> <div class="magazine-block-hero-content block block-layout-builder block-field-blocknodemagazinefield-hero-content"> <div class="break-out hero-bg"> <div class="container-fluid px-0"> <div class="field field--name-field-hero-content field--type-entity-reference-revisions field--label-hidden field__items"> <div class="field__item"> <div class="paragraph paragraph--type--image paragraph--view-mode--default"> <img loading="lazy" src="https://www.teachingenglish.org.uk/sites/teacheng/files/styles/wide_1920_x_700_focal_point_crop/public/Getty%201781988444.jpg?h=9eb0d413&amp;itok=aixuaJPZ" width="1920" height="700" alt="A person uses a smartphone while standing next to a large blue-lit digital display. The scene represents engagement with digital technology and online information." class="img-fluid image-style-wide-1920-x-700-focal-point-crop"> </div> </div> </div> </div> </div> </div> <div class="hidden block block-system block-system-breadcrumb-block"> <nav role="navigation" aria-labelledby="system-breadcrumb"> <h2 id="system-breadcrumb" class="visually-hidden">Breadcrumb</h2> <ol class="breadcrumb"

Given the emerging challenges and opportunities of generative artificial intelligence (GenAI) in teacher education, this study designs, implements, and evaluates an integrated educational system aimed at enhancing the critical analysis skills of pre-service teachers. This qualitative case study was conducted with the participation of history and social science pre-service teachers at Farhangian University. Data documenting their experiences within an AI-assisted content analysis project was collected via team and individual evaluation forms and analyzed using the thematic analysis method. The findings revealed dual-layered technical and human challenges, alongside multifaceted technical, pedagogical, and soft skill learning outcomes. More importantly, the results demonstrated a dialectical relationship between challenge and learning, conceptualizing the role of AI as a "pedagogical assistant" that fosters transformative learning by deliberately engineering cognitive conflict. We conclude that the effectiveness of AI in teacher education depends on its integration into a meticulously structured educational system—one where challenges are systematically reframed as learning opportunities and technology serves as a catalyst for shaping the professional identity of innovative educators.

Background: Despite theorists like Sam Wineburg emphasizing the development of historical thinking, history textbooks in centralized educational systems continue to be dominated by rote-learning approaches, leaving the systematic evaluation of their cognitive balance—particularly at the upper secondary level—largely neglected. Objective: This study aims to analyze the cognitive balance of upper secondary history textbooks (History 1, 2, 3, and Contemporary History of Iran) and evaluate the efficacy of the Comparative Adjudication Protocol (CAP) within a network of human auditors. Methodology: Engaging 43 pre-service teachers divided into 15 independent research teams, this study analyzed the content of 4 textbooks comprising 61 independent lessons using the William Romey technique and the CAP protocol. The process was executed across four distinct phases: capacity building, independent AI-assisted auditing, blind peer auditing, and discrepancy adjudication. Findings: The overall text involvement index was exceptionally low (0.019), far below the active learning threshold of 0.4, with 96.7% of the lessons falling into the passive "banking education" category. The image involvement index was absolute zero (0), and while the question involvement index was 1.29 (with an unbalanced distribution ranging from ∞ to 0.06), it revealed a severe structural gap between passive textual content and active analytical expectations. The CAP protocol demonstrated high reliability, achieving an 86.4% final inter-rater agreement and yielding a 12.5% increase in initial consensus. The dominant discrepancy pattern (A vs. B at 48.3%) indicated that distinguishing "objective historical facts" from "author interpretation" remains the primary analytical challenge for human coders in historical texts. Conclusion: Despite their narrative essence, high school history textbooks exhibit a highly passive cognitive structure lacking a coherent strategy to foster active student engagement. Implementing the CAP protocol within the R2A-TACI-PACT conceptual framework underscores the vital necessity of a "Human-in-the-Loop" (HITL) approach in historical content analysis. Curriculum revision and empowering pre-service teachers with algorithmic auditing literacy are highly recommended.