Frontiers in Education: Digital Learning Innovations

BackgroundEngagement and achievement gaps in higher education remain persistent, especially for students from lower socioeconomic and minority cultural backgrounds in digital learning environments.ObjectiveThis study examined whether participation in LMS-based courses implementing a Culturally Responsive Digital Differentiated Learning (CR-DDL) Framework was associated with greater improvements in student engagement and learning outcomes and with narrower observed engagement disparities compared with standard LMS-based instruction.MethodsAn explanatory sequential mixed-methods design was used with 252 undergraduates in public health and agrotechnology programmes at three Indonesian institutions, comparing CR-DDL courses (n = 129) with standard LMS-based courses (n = 123). Quantitative data included pre- and post-intervention Higher Education Student Engagement Scale scores, LMS-derived behavioural indicators, and final course scores. Outcomes were analysed using linear mixed-effects models with a random intercept for course to account for the clustering of students within the six participating course clusters. Eighteen students also participated in semi-structured interviews to provide contextual insight into their learning experiences.ResultsThe CR-DDL group demonstrated a larger increase in total engagement than the comparison group (mean change, +0.52 vs. + 0.13; group × time estimate = 0.31, SE = 0.07, approximate Wald 95% CI 0.17–0.45, p < 0.001). Greater increases were also observed in LMS-based participation. After adjustment for prior-semester GPA and course-level clustering, CR-DDL participation was associated with a 3.41-point higher final course score on the 0–100 reporting scale (SE = 1.12, approximate Wald 95% CI 1.21–5.61, p = 0.003). Lower-SES students in CR-DDL courses showed larger descriptive engagement gains than their counterparts in comparison courses. Because the formal group × time × SES interaction could not be verified from the currently reported summary results, the SES findings are interpreted as exploratory subgroup patterns rather than definitive evidence of effect modification. Documented interview excerpts provided limited contextual insight into students’ experiences but were not treated as independently reproducible qualitative evidence.ConclusionThe quantitative findings provide evidence that CR-DDL was associated with improved engagement, LMS participation, and course performance and with a narrowing of observed engagement disparities. The available qualitative material provides limited contextual insight into students’ experiences but should be interpreted cautiously because complete original qualitative records are currently unavailable.

IntroductionGenerative artificial intelligence has diffused rapidly among learners, but a question remains whether that reach becomes useful for learning or stalls at access. This distinction matters most where learners already have connectivity, so the barrier is no longer reaching the technology but converting access into learning use.MethodsThis study draws on a survey of 14,653 computer science learners across 166 countries, a population for whom first-level access is broadly shared. A two-part model separates whether an everyday user converts to any learning use from how widely converters then use the tool, estimated across World Bank income tiers.ResultsEveryday adoption is high across income levels and is not lower in poorer settings. The quantity that varies is conversion. Among everyday users, those in low-income countries are markedly less likely to convert to any learning use, at an odds ratio of 0.49 (95% CI 0.27, 0.90), while breadth among converters does not differ by income tier. A latent typology shows that disengaged learners are found across every income tier rather than in one region, and the association between AI use and self-rated proficiency is strongest where other support is weakest.DiscussionThe gap sits at the threshold of first learning use, not in the breadth beyond it. This article names the pattern the access-conversion gap and develops it as a measurable construct at the second and third levels of the digital divide. The findings describe a connected population of engaged learners, so the divide to address is in use, not access.

This mixed-method exploratory study examined the development of AI literacy among (N = 19) master's-level special education graduate students as they explored the integration of generative AI tools to support students with high-incidence disabilities. Participant responses were examined to identify perceptions of AI, intended uses of generative AI, and considerations related to responsible implementation in special education settings. Findings revealed a conceptual shift in participants' perspectives, from viewing generative AI primarily as an administrative tool for increasing efficiency to recognizing its potential as an adaptive instructional scaffold. Participants increasingly emphasized the need for critical human oversight, FERPA compliance, data privacy, and individualized alignment between AI-generated resources and students' educational needs. Intentional AI literacy development can support future special educators in moving beyond basic technical proficiency toward the critical judgment needed to evaluate and implement generative AI responsibly. Preparing special educators to use AI effectively requires attention not only to its potential benefits for instruction and efficiency but also to legal, ethical, and pedagogical considerations, including student confidentiality and individualized educational planning.

IntroductionThe modern higher education classroom is incredibly diverse, with students displaying various strengths, challenges, prior knowledge, and learning preferences. Traditional “one-size-fits-all” teaching methods often fail to meet different learners' needs and hinder student engagement and academic achievement. To address this, Universal Design for Learning (UDL), a research-based, learner-focused approach grounded in cognitive neuroscience, provides a proactive path to design more inclusive and accessible teaching practices.MethodsEmploying a quasi-experimental design, this study examined written lesson-planning performance among 45 Moroccan pre-service English as a Foreign Language (EFL) teachers across three conditions: a control group (CG), a UDL-only training group (TG1), and a UDL training supplemented with AI tools group (TG2).ResultsAnalysis of the 38 participants with matched pretest and posttest scores indicated an adjusted between-group difference in posttest lesson-plan scores. After controlling for pretest scores, TG2 had higher adjusted posttest scores than both CG and TG1, whereas TG1 did not differ significantly from CG. The TG2-versus-TG1 comparison was sensitive to the estimation method and is interpreted cautiously.DiscussionThe findings indicate an adjusted difference associated with the UDL + AI training package as delivered. Because the UDL + AI condition also included additional training time, demonstrations, guided prompts, practice, and feedback, the design cannot isolate the contribution of AI tools alone. The evidence is limited to rubric-scored lesson plans in a single Moroccan institution, and a larger, time-matched study is needed to examine which components of the training account for the observed difference.

The adequate application of artificial intelligence (AI) technology in legal education in the United Arab Emirates (UAE) has been barely covered by scholarly literature, especially, from the perspectives of legal and educational sciences. The current paper attempts to fill in an existing gap in literature about the appropriate usage of AI in legal education. The research is intended to help curriculum designers in UAE universities to improve undergraduate legal education programs using innovative AI systems and, thus, to provide UAE policy makers with the academic and ethical standards for the use of AI in legal education. The current paper critically discusses the possible contribution of AI systems in the improvement of the quality of legal education. Moreover, the research analyzes the probable implications of using AI in law curricula for the learning process and for the development of critical legal skills in UAE law students, stressing on the importance of ensuring academic integrity. Furthermore, the current research uses a qualitative approach to analyze the legal, educational, pedagogical, and ethical aspects of AI integration into legal education in the UAE and suggests a legal-pedagogical model of its responsible integration. The goal of the analysis is to determine what opportunities may be offered by AI systems in improving the quality of legal education in the UAE and to discuss their ability to educate students to perform independent legal practice. The discussion focuses on four main areas: (i) the quality of legal education in the UAE’s legal context; (ii) understanding of the AI-enabled legal education systems; (iii) identifying the educational and ethical risks of using AI in legal education; and (iv) setting the standards for the responsible use of AI in legal education. To conclude, the article argues that the adequate use of AI can improve significantly the quality of legal education, if appropriate ethical and academic standards will be developed to ensure the integrity of the process and to develop legal competencies of the students.

IntroductionGenerative AI creates a curriculum-transformation problem by redistributing activities through which disciplinary expertise is learned, practised, demonstrated, and assessed. This study examines that problem within graphic design education as a bounded empirical case.MethodsA survey of 270 participants was analysed using exploratory factor analysis (N = 265 complete cases), and semi-structured interviews with 12 design academics and practitioners were analysed using a frozen rule-based codebook across 650 meaning units. The quantitative and qualitative strands were analysed independently and integrated through a joint display.ResultsThe quantitative analysis yielded a broad, sample-specific Transformative–Normative response orientation and a narrower Curricular Diagnostic Critique cluster; neither is interpreted as a validated latent construct. The final qualitative dataset contained 504 normative/transformative units (77.5%), 35 diagnostic units (5.4%), and 111 units retained as X0 (17.1%). Both the N-family and D-family occurred in all 12 interviews.DiscussionThe integrated interpretation informed the Bot-Haus Curriculum Architecture, which organises human creative agency, experiential human–AI practice, adaptive curriculum, critical and ethical governance, foundational design principles, and educational outcomes. The architecture is presented as an empirically informed, theory-guided design proposition developed from the graphic design case, not as a validated causal model. Transfer to other practice-based disciplines remains a proposition for future empirical testing.

IntroductionPromoting scientific argumentation in the context of socio-scientific issues remains a major challenge in science education. Artificial intelligence offers new opportunities to provide adaptive feedback on students’ written arguments; however, little is known about learners’ perceptions of hybrid learning environments that combine AI-supported feedback with peer feedback under authentic classroom conditions. This study presents the development and pilot evaluation of SMOKEChem, a web-based learning environment designed to foster scientific argumentation through AI-supported and collaborative feedback processes.MethodsAn exploratory mixed-methods design was employed to investigate learners’ perceptions of SMOKEChem during regular chemistry lessons. A total of 130 secondary school students (mean age = 15 years) participated in the study. Quantitative data were collected using questionnaires assessing technology acceptance, cognitive load, and chatbot usability, while qualitative responses explored students’ perceptions of peer feedback.ResultsThe findings indicate that SMOKEChem was perceived as useful, user-friendly, and cognitively balanced, with low levels of extraneous cognitive load and high levels of germane cognitive load. Furthermore, students perceived AI-supported and peer feedback as fulfilling complementary functions: acceptance and usability data indicate that AI provided immediate, structured guidance for revising arguments, while qualitative data on peer feedback point to dialogic reflection, perspective-taking, and collaborative knowledge construction.DiscussionThe findings provide initial empirical evidence that hybrid feedback designs combining AI-supported and peer feedback can effectively support scientific argumentation in socio-scientific contexts. The complementary strengths of both feedback sources highlight the potential of integrating AI into collaborative learning environments rather than replacing peer interaction. These results provide practical implications for the design of AI-supported argumentation environments and inform future research on hybrid feedback approaches in science education.

Learning analytics promises early identification of struggling students, but evidence comes mainly from learning-management-system counts rather than authentic laboratory telemetry. We tested the predictive value of fine-grained process traces in a first-year digital electronics laboratory. The open Educational Process Mining dataset contains 115 engineering undergraduates observed across six simulator sessions (230,318 records) and 93 students with examination outcomes. Under a pre-specified, leakage-aware protocol—nested repeated cross-validation, pipeline-internal preprocessing, a 5×3 feature-group-by-model ablation matrix, and bootstrap confidence intervals—we predicted below-median final-examination performance. The pre-specified behavioural logistic model, using eleven features spanning volume, engagement, and interaction intensity, achieved an AUC of 0.617 (95% CI 0.491–0.737), not reliably above chance; the best non-pre-registered behavioural model reached 0.648. Four in-course-grade features achieved an AUC of 0.764 (0.644–0.854), the best matrix cell. Behaviour underperformed grades (ΔAUC −0.147, 95% CI −0.263 to −0.028), and adding telemetry to grades produced no detectable improvement (ΔAUC −0.040, −0.083 to 0.006). With one session, behavioural prediction was below chance and plateaued near 0.62 from session four, while the single grade available at session two already outperformed six sessions of telemetry; its signal concentrated in exercise coverage and workflow and partly reflected a small lower-participation group that often missed the examination. Full telemetry predicted non-participation well (AUC 0.872), but exercise coverage alone matched it (0.879). Continuous-score regression also favoured grades as standalone predictors (best MAE 21.29 vs. 22.38), although nonlinear unions modestly improved error. In this setting, telemetry is primarily a coarse disengagement flag; the course’s assessment loop remains the more trustworthy signal for participating students.

Artificial Intelligence (AI)-generated feedback is increasingly transforming mathematics education by providing timely, personalized, and adaptive learning support. However, limited evidence exists on the behavioural mechanisms through which students’ perceptions of AI-generated feedback influence mathematics learning outcomes, particularly in Sub-Saharan Africa. This study examined the direct and indirect relationships among Perceived AI Feedback Quality (PAQ), AI Dependence (AID), and Mathematics Learning Outcomes (MLO) among 360 undergraduate students in Ghana. A quantitative cross-sectional survey design was adopted, and data were analysed using Structural Equation Modelling (SEM) with IBM SPSS Statistics 23 and AMOS 23. The measurement model demonstrated satisfactory reliability and validity. The structural model showed that PAQ significantly predicted AID (β = 0.673, p < .001) and MLO (β = 0.449, p < .001), while AID also significantly predicted MLO (β = 0.441, p < .001). Bootstrapping analysis further revealed that AID partially mediated the relationship between PAQ and MLO (β = 0.314, p < .001). The findings indicate that students who perceive AI-generated feedback as accurate, relevant, and useful are more likely to depend on AI, and this dependence is in turn associated with stronger perceived mathematics learning outcomes. The study extends Feedback Theory and Social Cognitive Theory by identifying AI Dependence as a key behavioural mechanism linking AI feedback quality to mathematics learning. The findings provide practical implications for educators, instructional designers, and policymakers seeking to promote the effective and responsible integration of AI technologies into mathematics education.

IntroductionArtificial intelligence (AI) is rapidly transforming research practices in higher education, yet empirical evidence on how AI supports research productivity among students remains limited, particularly in developing contexts. This study examined the influence of AI tools used during the research preparation, analysis, and dissemination stages on the research productivity of postgraduate students in public universities in Nigeria. The study was guided by the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) and Experiential Learning Theory.MethodsThe study adopted a descriptive survey design involving 312 respondents. Data were collected using the AI Tools Utilisation and Research Productivity Questionnaire (AITURPQ) and analysed using simple linear regression. The analysis examined the predictive effects of AI-assisted research preparation, analytical, and dissemination tools on research productivity.ResultsThe findings reveal that AI-assisted research preparation tools significantly predicted research productivity (R² = .241, p < .05). AI-supported analytical tools also exerted a significant positive influence on research productivity (R² = .137, p < .05), while AI-driven dissemination tools demonstrated the strongest predictive effect (R² = .329, p < .05).DiscussionThe findings underscore the role of AI across the research lifecycle in supporting knowledge creation, analytical competence, and scholarly dissemination. They highlight the need to strengthen AI literacy and institutional support and promote responsible AI integration to enhance postgraduate research productivity.

Generative artificial intelligence (GenAI) and artificial intelligence (AI) are transforming STEAM education by reshaping instructional design, learning facilitation, assessment, creativity, and interdisciplinary learning. Their rapid evolution presents both opportunities and challenges for educators and educational institutions. This article synthesized contemporary scholarship on GenAI, AI, pedagogy, ethics, and digital innovation. Structured searches of Scopus, Web of Science, ERIC, and Google Scholar, supplemented by the UNESCO Digital Library and the OECD iLibrary and by citation chaining, identified 107 records, of which 15 met the eligibility criteria and were included in the reviewed corpus; additional literature cited in the findings and discussion attaches the pedagogical argument and contextualizes the synthesis. The primary search window covered 2016–2025, with foundational theoretical works purposively retained. Records were screened using explicit inclusion and exclusion criteria in two stages. The synthesis examined the conceptual foundations of Gen AI and AI in STEAM education, pedagogical theories supporting responsible integration of Gen AI and AI, and Gen AI and AI tools applicable to STEAM classrooms. Findings highlight applications in lesson planning, personalized learning, inquiry- and project-based learning, formative assessment, feedback, academic integrity, coding, media generation, and interdisciplinary learning. GenAI and AI can enhance teacher productivity, creativity, collaboration, adaptive instruction, and learner engagement when supported by sound pedagogy, AI literacy, and ethical governance. The article proposes an integrated framework for responsible human–AI collaboration linking pedagogical principles, ethical considerations, and discipline-specific applications. It offers practical guidance for fostering innovative, equitable, and contextually responsive STEAM education in an increasingly AI-enabled educational environment.

BackgroundLarge language models (LLMs) are increasingly being considered for assessment support in health professions education; however, evidence of their performance in essay-style examinations remains limited. In particular, little is known about the reproducibility and operational stability of LLM-based grading under different conditions.ObjectiveThis study compared the grading performance of several LLMs with that of human examiners in a master's-level public health course and assessed the consistency of LLM-based grading across repeated sessions and different file upload volumes.MethodsWe conducted a method comparison and validation study using anonymized student submissions from a 5-hour essay-style examination in a master's-level course in public health, empowerment, and health promotion. Four LLMs—ChatGPT, Gemini, LeChat, and Kimi—were prompted to assign final grades on an A-F scale using the same grading guidance as human examiners. The agreement between the LLM- and human-assigned grades in a single-file upload setting was assessed using weighted Cohen's kappa. Internal consistency across file upload volumes was assessed using Krippendorff alpha. Repeated grading across multiple sessions was performed for the best-aligned model.ResultsAgreement with human examiners was limited in fast mode and improved in reasoning or thinking modes; however, reproducibility across sessions and implementation conditions remained limited. In the single-file upload setting, ChatGPT showed the strongest agreement with human examiners (weighted kappa 0.718, 95% CI 0.578–0.859), followed by Kimi (weighted kappa 0.571, 95% CI 0.373–0.768). ChatGPT achieved 50.0% exact agreement and 90.6% agreement within ±1 grade, with corresponding values of 28.1% and 78.1% for Kimi. Gemini and Gemini Pro showed the highest internal consistency across the file upload conditions. Repeated grading by ChatGPT across 5 days showed moderate variation. LLMs used fewer extreme grades than did human examiners.ConclusionsSome LLMs, particularly ChatGPT and Kimi, showed moderate alignment with human examiners, but the alignemnet was not consistently stable across repeated sessions or operational settings. LLMs may currently be better suited as supervised grading assistants than as autonomous graders.

Teaching Virtual Reality (VR) design software to students with disabilities remains challenging in Ecuadorian higher education, where tool complexity, accessibility constraints, and diverse learning needs can limit participation. This study examined Hercules VR, a pedagogical framework integrating gamification, co-design, and biomimicry to support Unreal Engine learning among university students with cognitive, sensory, and motor disabilities. Using a single-group, mixed-methods pre-post pilot design (quasi-experimental, no control group), 26 students participated in a 16-week semester including 12 structured instructional sessions. The framework combined a co-design stage adapting activities to participants’ needs, a gamified progression system, and a biomimetic approach inspired by the life cycle of the Dynastes hercules beetle to make complex software concepts more accessible. Pre- and post-test instruments assessed VR design comprehension, collaborative participation, and intrinsic motivation. Results showed statistically significant within-group improvements across all self-reported dimensions (Wilcoxon signed-rank tests, all exact p < .001), with very large effect sizes (Rosenthal’s r = 0.86–0.87), computed from a dataset independently verified during revision. Post-test scores across the three outcomes were not significantly correlated, suggesting they changed relatively independently rather than as a single cohesive process. Given the absence of a control group and the combined delivery of the three intervention components, findings are interpreted as descriptive evidence of within-group change rather than confirmation of a causal effect or of any individual component's specific contribution. Qualitative findings indicated that participants perceived the biomimetic model as helpful for understanding abstract concepts and experienced the learning process as more accessible, while the co-design component was associated, in participants’ accounts, with greater autonomy and active participation. As an exploratory pilot study, these results suggest Hercules VR may offer a promising pedagogical approach for inclusive digital design education in similar higher-education contexts, though controlled, multi-institutional research is needed before broader replicability claims can be made. The framework aligns with Sustainable Development Goal 4 (Quality Education).

BackgroundHandwriting is central to children's written-language development, but conventional assessment usually emphasizes the completed product and provides limited information about the processes that produced it. Digital technologies can capture handwriting as an unfolding motor and orthographic activity.ObjectiveThis scoping review mapped technologies for capturing children's handwriting process data, the raw signals and derived features obtained, the assessment constructs supported by process measures, and their educational, developmental, and clinical applications.MethodsFollowing scoping-review guidance and PRISMA-ScR, we searched Scopus, PubMed, ERIC, Web of Science Core Collection, and IEEE Xplore and conducted backward citation searching of 11 relevant review or framework articles. Of 198 reports sought for full-text assessment, one could not be retrieved, 197 were assessed, 39 were excluded, and 158 were included. All included reports were charted with the same 16-field framework. Independent verification by a second researcher covered randomly selected samples of 40 of 197 full-text eligibility decisions (20.3%; 97.5% agreement; Cohen's κ = 0.918) and 32 of 158 extractions (20.3%).ResultsDigitizing or graphics tablets were used in 136 reports (86.1%), and 117 (74.1%) used Wacom hardware. Position or trajectory (145, 91.8%) and time or timestamps (124, 78.5%) were the most common raw signals, followed by pen pressure or normal force (82, 51.9%) and pen-state or in-air information (71, 44.9%). Common derived feature families included speed or velocity (97, 61.4%), temporal or duration measures (84, 53.2%), spatial or geometric measures (67, 42.4%), and pressure- or force-derived measures (59, 37.3%). Developmental or normative characterization (75, 47.5%) and clinical characterization or differential assessment (74, 46.8%) were the most frequent applications. Latin-alphabetic handwriting appeared in 114 reports (72.2%), followed by Hebrew (23, 14.6%), Chinese (19, 12.0%), and Arabic (5, 3.2%).ConclusionsDigital handwriting assessment is well established in process capture but less consistently translated into applied educational or clinical decisions. Classification performance should not be equated with diagnostic validity. A theory-informed and review-refined four-level framework organizes raw signals, derived features, assessment constructs, and empirical interpretation or action without implying a validated developmental or psychometric hierarchy.

IntroductionDigital technologies and artificial intelligence (AI) are reshaping teacher education faster than many programmes can pedagogically respond. This scoping review examined how digital technologies are incorporated into teacher education, the advantages and barriers associated with these approaches, and how recent AI-focused research may help reinterpret persistent challenges.MethodsTwo analytically distinct Scopus-derived subsets were analysed. The RQ1/RQ2 subset comprised 155 studies on digitally supported teacher education, while the RQ3 subset included 40 AI-focused studies, yielding an overall evidence base of 195 publications. Data charting combined primary-category coding, descriptive frequency analysis, cross-category synthesis, and multi-label coding for the AI-focused evidence.ResultsWithin the RQ1/RQ2 subset, competence and ICT-integration frameworks were the most frequent strategy (46.5%). Digital competence development was the most frequently reported advantage (76.1%), whereas infrastructure and access represented the main barrier (45.2%). In the AI subset, professional development and teacher accompaniment (35.0%) and instructional design and assessment (32.5%) were the predominant approaches.DiscussionAcross both evidence streams, technological affordances became educationally meaningful when mediated by pedagogical design, disciplinary knowledge, critical and ethical judgement, human oversight, professional agency, and institutional support. The findings support an AI-responsive approach to teacher education that emphasises pedagogically justified and professionally accountable AI use rather than tool adoption alone.

BackgroundDigital tools are increasingly integrated into higher education, yet evidence from Peruvian higher technological education remains limited.MethodsWe conducted an observational, analytical, cross-sectional study among 153 students from a public technological higher education institute in Callao, Peru. Data were collected in March–April 2025 using simple random sampling. Digital tools were assessed using an overall score and three domains: asynchronous interactive tools, synchronous interactive tools, and didactic resources. Meaningful learning was analyzed as a continuous score. Spearman correlations and linear regression models with HC3 robust standard errors were used. Multivariable models were adjusted a priori for sex and age group.ResultsMedian digital tools and meaningful learning scores were 94 (IQR: 85–103) and 85 (IQR: 75–96), respectively. Overall digital tools use correlated with meaningful learning (ρ=0.54; p < 0.001) and remained associated after adjustment (aβ=0.52; 95% CI: 0.41–0.64). Synchronous interactive tools showed the strongest independent association (aβ=0.81; 95% CI: 0.50–1.11), followed by didactic resources (aβ=0.46; 95% CI: 0.18–0.75). The association for asynchronous tools was attenuated after adjustment (aβ=0.21; 95% CI: −0.12 to 0.55; p = 0.213).ConclusionGreater digital tool use was associated with higher meaningful learning scores. Synchronous tools and didactic resources showed independent associations, whereas asynchronous tools did not retain an adjusted association. Longitudinal multicenter studies are needed to confirm these findings.

Digital entrepreneurship has emerged as one of the most transformative forces reshaping economies, labour markets, and educational expectations globally, yet initial teacher education (ITE) programmes have been conspicuously slow to respond. This article reports a systematic review of peer-reviewed literature published between 2015 and 2025, examining the extent to which digital entrepreneurship has been integrated into ITE curricula and what implications that integration, or its absence, carries for curriculum design. Searches were conducted across ERIC, Scopus, Web of Science, Google Scholar, and SciELO, yielding 312 initial results. Following rigorous inclusion and exclusion screening, 37 sources were retained and thematically analysed. Four interrelated themes emerged: the conceptual ambiguity surrounding digital entrepreneurship as an educational construct; the persistent gap between policy ambition and ITE curriculum reality; the inadequacy of pre-service teacher preparation for teaching digital entrepreneurship across conceptual, pedagogical, and technological dimensions; and the structural, cultural, and resource-related barriers that impede meaningful curriculum reform. Drawing on the EntreComp and DigCompEdu frameworks, the review concludes that ITE has not yet arrived at the destination its own policy documents have signposted. Fundamental redesign is required, not the incremental addition of technology modules, but a rethinking of what it means to prepare teachers for a digitally entrepreneurial world. The urgency of this task is compounded by the rise of generative artificial intelligence, acute youth unemployment across the Global South, and new evidence that pre-service teachers consistently underperform on precisely the digital competence dimensions most relevant to entrepreneurial practice. Five evidence-grounded curriculum implications are advanced: conceptual clarity before content delivery; integration rather than addition; the modelling imperative; assessment alignment; and structural partnership with the digital economy.

Generative artificial intelligence is increasingly used to produce formative feedback in programming education; however, whether students find such feedback useful, actionable, and worth using remains underexamined. This study presents a user-centred evaluation of feedback produced by an automated, rubric-based large language model assessment of programming responses, using a two-layer instrument, namely, response-level feedback on individual answers rated on five dimensions (clarity, specificity, accuracy, actionability, and usefulness) and a consolidated performance report rated on six dimensions (overall satisfaction, relevance, personalisation, cognitive load, intention to use, and motivation). Using a cross-sectional design, 144 students across secondary, short-cycle higher education (CTeSP—professional higher technical courses), and undergraduate programming rated 893 response-level feedback instances, and 140 of them rated 237 consolidated reports. Ratings were favourable across all dimensions, with student-level means of 4.24–4.43 (response level) and 4.11–4.38 (report level). At the response level, clarity and perceived accuracy were rated highest, and actionability and usefulness lowest. Cognitive load was the lowest at the report level. Only the between-context comparison for perceived accuracy reached statistical significance, with lower ratings in CTeSP (ε2 = 0.076); the small secondary sample limits conclusions about similarity. Perceived usefulness was most strongly associated with actionability and perceived accuracy (R2 = 0.76), and intention to use with motivation and personalisation (R2 = 0.53). These findings characterise the correlates of perceived usefulness and intention to use within each feedback layer, supporting human-centred designs that preserve student judgement and instructor oversight.

This study addresses the challenge of providing scalable and immersive environments for spoken English training in foreign language education. To this end, we designed and implemented a virtual reality (VR)-based instructional system that integrates immersive scenarios, avatar-based interaction, and task-based learning activities to support oral communication practice. A convergent parallel mixed-method design was employed to evaluate the effectiveness of the proposed instructional framework. Using convenience sampling, 60 university students from two intact English classes voluntarily participated in the study. One class (n = 30) received VR-supported instruction, whereas the other class (n = 30) received traditional instruction. Quantitative data were collected through pre- and post-intervention IELTS speaking assessments, while qualitative data were obtained from questionnaires and interviews to examine learners’ perceptions and experiences. Quantitative analyses, including independent-samples t-tests and ANCOVA, revealed no statistically significant difference in speaking proficiency between the two groups. However, a small effect size (Cohen's d = 0.2085) suggested a modest advantage for the VR group. Qualitative findings indicated high levels of learner engagement and perceived improvement, with over 90% of participants reporting increased confidence, vocabulary use, and opportunities for authentic communication. Learners particularly valued the immersive environments, avatar-based interaction, and immediate feedback provided through the VR platform. Rather than developing a new VR engine, this study contributed a structured instructional framework that demonstrates how existing immersive technologies can be systematically integrated into spoken English instruction. The findings provide practical insights into the pedagogical design, implementation, and classroom integration of VR technologies for language learning.

Generative artificial intelligence (GenAI) is rapidly transforming pedagogical practices in higher education by generating explanations, feedback, simulations, learning resources, and dialogic prompts. Existing AI frameworks in education predominantly conceptualize AI through functional roles, such as tutoring, assessment-centric models, institutional governance principles, or learner literacy perspectives. However, higher education institutions often regard GenAI as a complementary tool while simultaneously framing it as a threat to academic integrity, triggering reactive responses such as prohibition, surveillance, and detection. These framings leave a theoretical gap, offering limited insight into how GenAI redefines pedagogical agency, responsibility, and knowledge work in everyday interactions among instructors, students, and institutional structures. To address this gap, the present study proposes a nested instructor-student-GenAI triadic conceptual model for higher education. The model is derived through a focused integrative interdisciplinary synthesis that brings together literature from higher education, educational technology, learning sciences, instructional design, human-computer interaction, cognitive psychology, policy, ethics, and institutional governance. The model positions GenAI as a bounded didactic-pedagogical mediator operating within a shared didactic mediation space. Higher education institutions are conceptualized as the governance layer that enables, constrains, and legitimizes triadic practice through policies, infrastructure, regulations, and accountability mechanisms, while wider stakeholders shape external expectations. The study further formulates researchable propositions and discipline-sensitive implications to support future empirical validation and responsible GenAI integration in higher education.