Frontiers in Education: Digital Learning Innovations

This study investigates how Chinese English interpreting teachers in China position themselves in relation to the growing presence of artificial intelligence (AI) in interpreter training. While existing research has largely focused on student attitudes, tools, and curriculum-level discussions, the perspectives of interpreting educators remain less visible, despite their role in shaping pedagogical practice and professional norms. Drawing on a survey of 156 interpreting teachers in Master of Translation and Interpreting (MTI) programs, the study examines multiple dimensions of AI orientation, including readiness (enabling conditions and AI evaluative literacy), reported use of AI-enabled tools, critical evaluation practice, perceived usefulness and future intention, profession-related concerns (threat to professional autonomy and perceived labor devaluation), and restrictive responses (restriction behavior frequency, human judgment/skill protection, and restrictive orientation toward AI integration). Descriptive results indicate above-midpoint levels of readiness and perceived usefulness/future intention, alongside moderate reported use with substantial variability. Correlational analyses show that enabling conditions are positively associated with tool use and with perceived usefulness/future intention, whereas AI evaluative literacy shows a small negative association with tool use and perceived usefulness, and no association with future intention. Reported use is strongly associated with perceived usefulness and positively associated with future intention. Profession-related concerns are also evident: restrictive orientation is strongly associated with autonomy threat and positively associated with perceived labor devaluation, and autonomy threat and labor devaluation are positively related. In a regression model including role and teaching experience, autonomy threat and labor devaluation do not show unique effects on restrictive orientation, while teaching experience is strongly associated with restrictiveness. The findings suggest that interpreting teachers’ engagement with AI is not captured by adoption-related constructs alone. Alongside perceived pedagogical value, profession-related concerns and experience-linked boundary-setting shape how AI is positioned in interpreter training. The study extends technology acceptance approaches by incorporating constructs related to autonomy and labor value, and it highlights the need for AI integration strategies that address pedagogical use as well as professional implications.

This perspective article is grounded in professional experience and classroom observation, and its aim is to raise an issue and open it for discussion rather than to settle it. AI is often presented to teachers as a way to make educational work easier, faster, and more flexible, yet they are simultaneously confronted with a growing array of AI platforms, agents, automation systems, and technical courses. This creates a practical tension: if AI is meant to reduce teachers’ workload, it is not obvious why using it well should require them to keep learning new technical systems. One source of this tension, we argue, is the weak distinction between AI literacy and prompt engineering. We take AI literacy in language education to be a broad competence concerned with how teachers, students, and institutions learn to live, work, study, and teach responsibly in a world shaped by AI, whereas prompt engineering concerns more specifically how users design, refine, evaluate, and revise prompts to guide AI tools toward useful and responsible outputs. For English and English medium instruction (EMI) teachers, we propose that this difference matters because these teachers do not need to master AI technically; they need to learn how to make it serve teaching, learning, language support, content understanding, and student participation. We refer to this teacher-facing capacity pedagogical prompting and distinguish it from both broad AI literacy and technical prompt engineering. To make the idea concrete, we offer the Pedagogical Prompting Feedback Cycle as a teacher-oriented way of translating existing instructional expertise into AI-supported practice. We present it as a tentative conceptual model rather than a validated framework: it is meant to provoke inquiry and design, and it requires empirical validation across diverse languages, disciplines, and educational settings.

The present study aimed to examine the relationships between critical thinking and education for sustainable development (ESD) practices, as well as to determine the levels of critical thinking, and education for sustainable development ESD practices among teachers. The study sample consisted of (145) teachers from the elementary, preparatory, and secondary educational stages in the United Arab Emirates. A descriptive correlational research design was employed. Two researcher-developed scales were administered: The Teachers’ Critical Thinking Scale and the Education for Sustainable Development Practices Scale for Teachers, along with their psychometric properties. The results revealed that there was no statistically significant relationship between critical thinking and ESD practices. In addition, the findings indicated that the levels of both critical thinking and ESD practices were high.

Reading texts on a screen has become a routine part of everyday life. While teaching reading in primary school, this calls for practical didactic approaches combining differentiation, digitalization and cross-curricular content. This study examines how digital differentiated cross-curricular reading materials (EFADIL) are implemented in Grade 3 classrooms in Austria. A mixed- methods design integrates teacher interviews (N = 34), classroom observations (N = 33), and student questionnaires (N = 605). The findings reveal tensions between perceived advantages, disadvantages, and enacted classroom practices. The materials provided a motivating learning environment and enabled differentiation and independent work. Simultaneously, they might lead to superficial and rapid task processing, along with variations in the use of the interactive digital features (e.g., glossary word function). Tasks requiring language production were often perceived to be more cognitively demanding. The study reveals instructional guidance, the teachers' role, and dimensions of instructional quality to be key factors in ensuring that digital differentiated materials support effective and inclusive reading instruction.

IntroductionThis paper aims to identify effective strategies for integrating Artificial Intelligence (AI) in higher education (HE), specifically addressing the challenges related to AI literacy, ethical concerns, and institutional variations. It provides a framework for incorporating an “embracing” approach to AI adoption, characterised by strategic incorporation of AI tools and underpinned by AI literacy and constructive AI skepticism.MethodsThe paper employs three main approaches. First, we explore current challenges faced by instructors in taking an embracing approach, particularly in relation to AI literacy. Second, we introduce instructor AI skepticism from a multi-layered perspective. Third, we present a series of case studies from universities in Northern Ireland reflecting on challenges of the embracing approach across STEM, Humanities, and Social Sciences, and how these challenges have been addressed.ResultsAn exploration of AI adoption across different subject areas in four case studies demonstrates substantial variability in AI literacy, AI skepticism and acceptance across disciplines. Nonetheless, broader common themes emerge across the case studies. These themes generally involve critical thinking and application of subject area-specific theories to new contexts. Crucially, structured instructional approaches significantly reduce misconceptions and enhance ethical AI usage.DiscussionThis study uniquely integrates discipline-specific case studies and institutional context analyses. It contributes to understanding AI literacy as a multidimensional skillset. It further positions AI literacy as a critical means of addressing the underrepresented instructors' multilayered AI skepticism, with an emphasis on ethical considerations and critical student engagement. Our recommendations highlight the necessity for targeted faculty training, clear institutional policies, and structured curriculum integration strategies to foster AI literacy, address instructor skepticism, and enhance ethical awareness effectively.

Social networks have transformed the way young people relate to knowledge. The intended pedagogical use in higher education is still barely theorized. Our article proposes a theoretical design of a private Facebook group as a connectivist learning space for university students in the first levels of the Education career. The objective is to theoretically argue how a private Facebook group, structured under connectivist principles, can constitute an intentional learning node that favors the collective construction of knowledge about the role of the teacher in the digital age. Methodologically, we adopt a non-empirical approach, inscribed in the conceptual and theoretical tradition, which articulates the principles of connectivism of Siemens and Downes with the pedagogical possibilities of existing social networks. We include conceptual instructional design without reaching its empirical implementation. Our paper posits a closed, moderated space on a well-known social platform where students can reduce technological friction, activate authentic cognitive and relational connections. We conclude that the instructional design of the group is the determining condition for the emergence of connectivist learning and not the platform itself.

This paper conceptualises the SAGE Defend step, the sixth stage of the Structured AI-Guided Education framework, as a format-agnostic assurance checkpoint for AI-integrated higher education assessment. The study responds to a verification gap identified in earlier SAGE research, in which process documentation and AI interaction logs were found to support transparency but not, by themselves, to verify individual ownership of reasoning in group-based AI-integrated submissions. Adopting a design-informed conceptual approach grounded in design-based research principles, the paper integrates a multi-year programme of empirical SAGE studies, a structured synthesis of the assurance-task literature, and diagnostic observations from three Defend-proximate assessment implementations across undergraduate and postgraduate units at Central Queensland University. It distinguishes between assurance tasks that directly require students to demonstrate reasoning or performance, controlled assurance conditions that restrict the assessment environment, and corroborative assurance signals that provide corroborating but non-stand-alone evidence. On this basis the paper proposes a three-class assurance-task typology, an epistemic matching framework, and six design principles for embedding SAGE Defend within assessment sequences. It further argues that assurance should be distributed across the assessment sequence of a unit, so that each learning outcome is verified at a point and intensity proportionate to its stakes rather than concentrated in a single terminal examination. The paper frames this response as assurance by design, an approach that, echoing the established engineering principles of security by design and privacy by design, builds verification into the assessment sequence rather than appending it after the fact, and it names the compounding cost of the retrofitted alternative as assurance debt. Rather than presenting SAGE Defend as an oral examination model or claiming empirical validation of a single format, the paper positions Defend as a design principle through which educators can align verification tasks with the cognitive, professional, or technical competency being assessed. The contribution is therefore conceptual and practice-informed, offering a structured basis for the future empirical validation of specific Defend formats across disciplines, cohorts, and delivery modes.

IntroductionThis scoping review examines the growing intersection between lifelong learning (LLL) and artificial intelligence (AI), focusing on whether the promises of assistance, personalization, and automation are aligned with the broader educational, social, and equity-oriented aims of LLL. The review addresses a central concern: although AI is increasingly promoted as a transformative resource for lifelong education, its deployment may not always correspond to the paradigms, geographical contexts, and vulnerability profiles emphasised in LLL literature.MethodsA scoping review was conducted using systematic search and reporting procedures informed by PRISMA 2020 and PRISMA-ScR. Two complementary corpora were analysed: 110 conceptual and empirical articles focused on LLL, and 79 articles addressing the application of AI within LLL contexts. The analysis combined categorical coding with exploratory statistical procedures, including chi-square tests and Cramér's V, to examine descriptive associations between LLL paradigms, geographical regions, vulnerability profiles, and the three dominant promises of AI.ResultsThe findings show that LLL paradigms display descriptive variation across regions and are associated with different vulnerability configurations, including socio-economic, territorial, demographic, and human-diversity-related dimensions. In contrast, AI-related articles show weak descriptive associations with LLL paradigms, geographical contexts, and vulnerability profiles. Although personalization appears as a prominent AI promise, assistance and automation are also present across the corpus, usually without strong conceptual alignment with the equity-oriented and socially grounded concerns of LLL.DiscussionThe review identifies a misalignment between the theoretical and policy-oriented framing of LLL and the practical deployment of AI in educational contexts. This suggests the need to move beyond technology-centred approaches and to design AI-supported lifelong learning initiatives that are explicitly connected to regional needs, learner vulnerability, social justice, and human development. The review contributes an analytical framework for examining how AI promises can be critically aligned with inclusive and transformative visions of lifelong learning.

IntroductionArtificial intelligence (AI) is rapidly reshaping higher education globally, influencing teaching, learning, administrative processes, and institutional service delivery. Despite growing scholarly attention, evidence remains fragmented across disciplines, particularly regarding the combined influence of AI integration, adaptive leadership, and digital infrastructure readiness on service delivery outcomes. This systematic literature review synthesises global evidence to develop an integrated understanding of AI-driven transformation in higher education.MethodsA systematic literature review was conducted following PRISMA guidelines. Peer-reviewed studies published in English between 2019 and 2024 were retrieved from Scopus, Web of Science, IEEE Xplore, SpringerLink, ScienceDirect, and Google Scholar. Studies were selected based on their relevance to artificial intelligence, adaptive leadership, digital infrastructure readiness, and service delivery in higher education. Following eligibility assessment, 155 studies met all inclusion criteria and were included in the final synthesis. Data were extracted using a structured framework and analysed through thematic synthesis across five domains: AI integration, adaptive leadership, infrastructure readiness, service delivery transformation, and barriers to sustainability.ResultsThe synthesis indicates that AI is associated with improvements in teaching, learning, and administrative efficiency through learning analytics, intelligent tutoring systems, chatbots, and predictive modelling. Adaptive leadership emerged as a key enabler of AI adoption by fostering innovation, managing resistance, and aligning institutional strategies with digital transformation goals. Digital infrastructure readiness influenced the scalability and effectiveness of AI systems, with disparities between high-income and low-income regions. The reviewed literature suggests that AI-enabled service delivery improves responsiveness, personalisation, and student satisfaction, although ethical concerns, infrastructural constraints, and resistance to change limit implementation.ConclusionAI-driven transformation in higher education is multidimensional and context-dependent. Its success relies on the interaction between technological capability, adaptive leadership, and institutional readiness. Strengthening infrastructure, ethical governance, and leadership capacity is essential for achieving sustainable and equitable AI-enabled service delivery in higher education.

Digital transformation has intensified the need for educational approaches capable of fostering meaningful civic participation within digitally networked societies. While digital citizenship has become an important focus of contemporary education, empirical evidence examining how digitally mediated participatory learning contributes to measurable civic competence development remains limited, particularly within Global South contexts. This study investigates whether participation in Project Citizen Digital, a digitally mediated participatory civic learning model, is associated with changes in students’ civic competence in online higher education. A quasi-experimental one-group pretest–posttest design was employed involving 80 undergraduate students enrolled in an online civic education course in Indonesia. The study operationalized civic competence as a multidimensional construct comprising civic knowledge, participatory civic skills, and civic dispositions. Data were collected using validated survey instruments and analysed using paired-sample statistical procedures and effect size estimation. The findings indicate statistically significant improvements in overall civic competence following participation in the intervention (p = .003), with the largest gains observed in civic knowledge and participatory civic skills. Civic dispositions demonstrated comparatively limited short-term change. These findings provide initial empirical evidence suggesting that structured digitally mediated participatory learning environments may support measurable civic competence development. The study contributes to digital civic education scholarship by operationalizing civic competence as a measurable educational outcome within digitally mediated learning environments and by providing empirical evidence from a rapidly digitizing Global South higher education context.

The rapidly growing landscape of artificial intelligence and its integration in higher educational institutions of India from 2022 compelled us to cautiously analyse the changing dynamics of social science education per se. Most higher education institutions in India now allow students and researchers to use artificial intelligence. India is known as a talent superpower and according to the Stanford AI Presentation Index, it ranks first in skill penetration related to artificial intelligence. In the 2025–26 budget, India committed ₹500 crore to establish Centres of Excellence in education, marking a strategic investment to elevate academic innovation and infrastructure. Additionally, India's National Education Policy 2020 (Ministry of Human Resource Development, 2020) focuses on promoting AI and technological innovation. Given the intense push to promote technology and artificial intelligence in education, this research paper aims to analyse and evaluate the different policies impacting the usage of artificial intelligence in social science research at higher education institutions in India. The method is based on evaluating the textual content of documents by using human coding. The limitation of this research is that it is based on scanning textual documents and is limited to understanding the usage of AI in teaching and learning for the social science stream. In this process, it aims to deploy critical technology studies with the digital ethics framework. As a result of analysing the policy texts, we find that AI's invisibility is the major issue. Algorithmic bias, trained on historical data reflecting caste, class, religion, region, race and gender disparities, and plagiarism via generative tools erodes authors' dignity. Many of the detection tools are neither precise nor dependable in detecting AI usage and misuse. As educators, it is of urgent necessity to prioritise pedagogy that values human creativity, integrity and judgment. Similarly, too much dependence of Gen-Z learners on AI erodes critical thinking. This violates NEP 2020's vision of balancing innovation with ethical safeguards. Present mechanisms to regulate and monitor AI usage in higher education and in the country are fragmented and indirect. With AI deployment occurring at such a massive scale, establishing a robust India-centric regulatory framework to govern its use is no longer optional but essential.

This meta-analysis examines the relationship between varying levels of self-regulated learning (SRL) support (ranging from limited to advanced) and learning outcomes. While previous literature reviews have explored the topic of SRL interventions from many angles, the spectrum of SRL support has not been systematically investigated. This study addresses this gap by analyzing 39 articles published between 2020 and 2025. The meta-analytic findings indicate that educational interventions incorporating higher levels of SRL support statistically significantly enhance affective, behavioral, and cognitive learning outcomes. Our analysis identified SRL support spectrum: control learning environments consistently provided low levels of SRL support across all three SRL phases, whereas experimental environments offered moderate support. Moreover, the level of SRL support in experimental learning environments was significantly associated with effect sizes of learning outcomes, with a particularly strong effect on behavioral outcomes. Notably, only a limited number of studies examined experimental educational settings that implemented advanced or near-advanced SRL support. These findings offer valuable insights for SRL research and have implications for the design of targeted interventions aimed at fostering highest levels of SRL support currently recognized as Advanced. We discuss the results from multiple perspectives and highlight directions for future research in optimizing SRL implementation.

BackgroundThe integration of assistive technologies (AT) in the intervention for children with Autism Spectrum Disorder (ASD) is critical. However, the mechanisms through which professional training translates into practical competence remain under-explored. This study examines a moderated mediation model to understand how academic domain and educational expectations influence this transition.MethodsA cross-sectional study was conducted with 227 students (97.4% female, ages 18-45) from the University of Bucharest, specializing in Special Psychopedagogy (48%), Pedagogy (25.1%), and Primary/Preschool Education (26.9%). Participants completed assessments regarding their preparation/training, perceived educational needs/expectations, and practical experience in AT. Data were analyzed using moderated mediation analysis (Delta method) to assess conditional direct and indirect effects.ResultsModerated mediation analysis revealed that all interaction paths were significant (p < .05). A key finding was the “filter effect” of educational expectations: as student needs increased, the conditional indirect effect of training on practice significantly strengthened (β Low = 0.230, p = .020; β High = 0.511, p < .001). Conversely, the conditional direct impact of training on practice weakened as expectations rose (β Low = 1.395, p < .001; β High = 0.805, p < .001). Notably, while 70.5% of students reported below-average formal training, 100% demonstrated above-average practical application, indicating a heavy reliance on subjective expectations and domain-specific context.ConclusionsProfessional training in AT does not produce uniform outcomes. Its efficacy is maximized when calibrated to the specific academic domain and the individual motivational structure of the students. For clinical and educational practice, these findings suggest that “one-size-fits-all” training programs are suboptimal. To effectively translate theoretical knowledge into high-quality interventions for children with ASD, programs must integrate students’ subjective expectations and the unique requirements of their specialization.

Education is a key driver of national development, and technology is transforming assessment methods globally. Although traditional examination formats, such as multiple-choice and essay tests, dominate assessment, subjective human scoring often leads to inconsistencies. Artificial intelligence-based automated scoring systems offer a promising solution. Therefore, this study uses a developed artificial intelligence software based on a contextual similarity model to score mathematical items in secondary school Economics and validates the AI-generated scores against human experts. A correlational research design was adopted for this study. The population comprised 274,978 Economics students from South-west Nigeria, 1,008 of which were sampled through a multi-stage sampling procedure. An AI-based software was developed based on the waterfall model. It was validated by three software experts, and a Cronbach alpha of 0.86 was determined. The data were analysed using descriptive statistics and a Pearson Correlation Coefficient at an alpha level of 0.05 significance. The study’s findings revealed a significant relationship between the AI software and the 12 human experts in scoring Economics Mathematical items. The intra-class correlation coefficient (ICC) indicated a high level of agreement (r = 0.86), P < 0.01). The study concluded that the scoring model possesses the ability to award scores to mathematical computation items in economics that are equivalent to the scores awarded by human expert markers. The study, therefore, recommends that teachers, evaluators, and various examination bodies use this Artificial Intelligence software in scoring mathematical computation items in economics to ensure consistency and save on cost and time.

This study examines how full-time undergraduate and graduate students at a leading technical university in Uzbekistan engage with generative AI tools and how this relates to cognitive regulation, motivation, self-regulation, and ethical awareness. Using a quantitative exploratory design, an online survey was administered to 381 participants during the first (autumn) semester of the 2025–2026 academic year. A preliminarily screened 30-item instrument was developed across seven scales AI Use, Digital Attention, Cognitive Load, Motivation, Self-Regulation, Neuro-AI Pedagogical Competence, and Ethics and Academic Autonomy with internal consistency ranging from α = 0.717 to α = 0.895; content and discriminant validity have not yet been confirmed on independent samples. Group comparisons were conducted using Mann–Whitney U with Cohen's d on a balanced subsample (n = 302; 151 per group), constructed to avoid confounding group-size asymmetry with substantive group differences. Significant between-group differences were found on six of seven scales (p < .001): Ethics and Academic Autonomy (d = 1.47), AI Use (d = 1.29), Self-Regulation (d = 1.14), Neuro-AI Pedagogical Competence (d = 1.14), and Digital Attention (d = 0.99) showed large effects; Motivation showed a medium effect (d = 0.70). Cognitive load did not differ significantly (p = .296). Multiple regression (n = 372) identified Ethics and Academic Autonomy (β = 0.446), Self-Regulation (β = 0.279), and Motivation (β = 0.178) as the strongest predictors of Neuro-AI Pedagogical Competence (R2 = 0.799). The findings suggest that higher scores on Neuro-AI Pedagogical Competence were associated with stronger ethical awareness, self-regulation, and motivation, rather than with frequency of AI use. The study contributes empirical evidence from Central Asian higher education and supports the design of level-differentiated AI-integrated curricula.

This study examines the relative predictive contribution of artificial intelligence (AI) learning and use, compared with academic trajectory and sociodemographic variables, in explaining scientific productivity profiles among undergraduate students participating in university research seedbeds in an emerging Latin American context. A quantitative, non-experimental, cross-sectional design with supervised predictive modeling was employed, using census data from 57 students at a Peruvian university. Six dimensions of AI learning and use, along with composite indices and academic trajectory variables, were evaluated against a binary scientific productivity profile. Four machine learning classifiers (penalized logistic regression, Random Forest, Extra Trees, and XGBoost) were compared using repeated stratified cross-validation, and model interpretability was assessed through permutation importance. Results indicate that penalized logistic regression achieved the highest predictive performance (accuracy = 0.842; balanced accuracy = 0.845; F1-macro = 0.835; AUC-ROC = 0.894). The most influential predictors were time of participation in the research seedbed, age group, and gender, whereas AI-related composite indices exhibited negligible or marginally negative contributions. These findings suggest that, in this sample, self-reported AI capabilities were not the dominant predictors of scientific productivity once formative trajectory variables were considered. Instead, sustained formative engagement and academic trajectory emerge as stronger determinants, challenging the prevailing assumption of AI as a universal accelerator of research performance. The study highlights the need to reorient institutional strategies toward strengthening research training environments and integrating AI within the full scientific production cycle.

Digital literacy has become a foundational competence for participation in contemporary educational, social, and economic life. However, debates surrounding children's digital engagement often oscillate between concerns about online vulnerability and assumptions that expanding connectivity automatically promotes digital inclusion. This study examines how digital access, digital literacy, educational engagement, and vulnerability intersect among Basic 8 students in Ghana. The study employed a mixed-methods design involving a cross-sectional survey of 823 students drawn from 24 public schools across three districts in Ghana's Central Region, complemented by a six-week ethnographic study in three selected schools. Composite indices were developed to measure digital access, digital literacy, educational engagement, and vulnerability, all demonstrating acceptable reliability (Cronbach's α = 0.76–0.88). Correlation and regression analyses were used to examine relationships among key constructs, while qualitative data provided contextual insights into students’ digital experiences. Findings reveal a paradoxical pattern of digital inclusion. Despite limited ICT infrastructure within schools, students reported substantial engagement with digital technologies through home-based access and mobile devices. Digital access was positively associated with digital literacy (r = .174, p < .001) and educational engagement (r = .444, p < .001), while vulnerability was negatively associated with digital access (r = −0.692, p < .001), digital literacy (r = −.105, p < .01), and educational engagement (r = −0.284, p < .001). Importantly, an access–competence gap was identified among 21.6% of students, indicating that access exceeded competence. Regression analysis further demonstrated that this gap was the strongest predictor of vulnerability (B = 0.326, p < .001), suggesting that exposure to digital environments without corresponding competencies increases susceptibility to online risks. The findings challenge access-centred approaches to digital inclusion by demonstrating that connectivity alone does not guarantee safe, meaningful, or empowering digital participation. The study argues for competence-centred digital inclusion policies that integrate digital literacy, online safety, information verification, and critical media education within basic education systems. By operationalising the concepts of vulnerability and the access–competence gap, the study advances understanding of digital inequality in Ghana and comparable low-resource contexts across sub-Saharan Africa.

Virtual Reality (VR) being an promising tool for immersive learning nowadays, nevertheless, in K-12 classrooms across the UAE, it's still underexplored thus highlighting a research gap in VR in the UAE context. This study collected data from twenty-five teachers in a single UAE private school who used VR with their students. Using qualitative focus group interviews and data thematic analysis, the study explores the teachers' hands-on experiences, perceived educational impacts, challenges, and practical considerations regarding VR integration in the UAE educational context. The findings discussed the discrepancies of VR's perceived educational benefits and the issues associated with integrating VR into education. Themes identified include student involvement and engagement in VR learning, lack of cultural-related curriculum for Arabic implementation, strategies used by teachers to create innovative solutions for classroom delivery with the lack of resources, technical barriers for both students and teachers, and safety-related issues concerning young children using VR technology. One point that emerged was the difference in what VR was presumed to accomplish regarding teaching and learning and how effective VR is when being actively implemented in an actual educational environment. This study highlights the need for Emirati localized content development, robust infrastructure, and continuous educational support to benefit from the VR's full potential. This paper contributes to the growing body of literature on immersive learning by providing a detailed, context-specific model for VR adoption in a non-Western educational system, offering valuable understandings for educators, policymakers, and technology developers.

IntroductionThe growing availability of generative artificial intelligence (AI) tools has created new opportunities to enhance language learning, particularly speaking practice. However, studies on the effectiveness of AI voice-chat applications in enhancing English as a Foreign Language (EFL) learners’ speaking skills and autonomy remain limited.MethodsThis mixed-methods quasi-experimental study investigated the effects of Microsoft 365 Copilot voice chat on speaking fluency, interactional competence, and learner autonomy of 52 university English as a Foreign Language (EFL) learners over eight weeks. The experimental group (n = 26) engaged in voice-based speaking tasks with Copilot, while the control group (n = 26) completed parallel peer-to-peer speaking activities.ResultsQuantitative analyses revealed that the experimental group demonstrated greater improvements in speech rate, mean length of run, and pause reduction, as well as larger improvements in turn-taking, repair strategies, and topic management. Learner autonomy scores increased significantly only for the experimental group [t(25) = 4.12, p < .001, d = 0.82, mean difference = 0.90, 95% CI [0.45, 1.35]], with 71% of participants voluntarily exceeding the required practice time. Qualitative reflective responses with eight experimental participants indicated reduced speaking anxiety, perceived usefulness of immediate feedback, and increased motivation.DiscussionThese findings suggest that AI voice chat, when integrated into learners’ existing academic tools, may support multidimensional speaking development and autonomous practice in EFL contexts.

IntroductionThis study investigated the effectiveness of a GenAI-based intelligent learning environment, “Neuro-Adapter,” as a cognitive scaffold for developing spatial imagination skills and reducing performance gaps based on cognitive processing styles.MethodsA quasi-experimental design was used with 45 ninth-grade students (control group, n = 23; experimental group, n = 22). Data were gathered using a Cognitive Processing Style Inventory and a Pre/Post Spatial Imagination Test over a 4-week timeline.ResultsPre-test results favored holistic-visual styles (0.05 ≥ α). On the post-test, the experimental group significantly outperformed the control group with a large effect size (η² = 0.86). Within the experimental group, a non-significant post-test difference (Sig = 0.067, Cohen's d = 0.58) indicated that the environment successfully leveled the skill gap for sequential-analytical thinkers.DiscussionThe “Neuro-Adapter” functioned effectively as an operational cognitive equalizer. We recommend integrating adaptive GenAI algorithms and tailored software scaffolding strategies into spatial education curricula.