Search Results (471)

This study comprehensively analyses how AI tools scaffold and share metacognitive processes, thereby facilitating students’ learning in STEM classrooms through a mixed-method research synthesis combining bibliometric analysis and systematic review. Using a convergent parallel mixed-methods design, the study draws on 135 peer-reviewed articles published between 2005 and 2025 to map publication trends, author and journal productivity, keyword patterns, and theoretical frameworks. Data were retrieved from Scopus and Web of Science using structured Boolean searches and analysed using Biblioshiny and VOSviewer. Guided by PRISMA 2020 protocols, 24 studies were selected for in-depth qualitative review. Findings show that while most research remains grounded in human-centred conceptualisations of metacognition, there are emerging indications of posthumanist framings, where AI systems are positioned as co-regulators of learning. Tools like learning analytics, intelligent tutoring systems, and generative AI platforms have shifted the discourse from individual reflection to system-level regulation and distributed cognition. The study is anchored in Flavell’s theory of metacognition, General Systems Theory, and posthumanist perspectives to interpret this evolution. Educational implications highlight the need to reconceptualise pedagogical roles, integrate AI literacy in teacher preparation, and prioritise ethical, reflective AI design. The review provides a structured synthesis of theoretical, empirical, and conceptual trends, offering insights into how human–machine collaboration is reshaping learning by scaffolding and co-regulating students’ metacognitive development in STEM education. Full article

Young people increasingly express high levels of concern about the climate crisis. Their climate-related emotions and thoughts are often linked to a perceived lack of government action. This study employs a convergent parallel mixed-methods design to explore the perceptions and emotions of college students (N = 16) related to climate change and government climate action. Participants completed semi-structured interviews assessing their thoughts and emotions about climate change as well as a measure of impairment related to climate change anxiety. Through reflexive thematic analysis of interview responses, researchers generated the following themes to describe participants’ responses: negative emotions and dire predictions, influence of participants’ background on perceptions and engagement, coping with climate-related concern, perceptions of government and political influence, and barriers to the U.S. addressing climate change. While results of bivariate correlation analysis indicated low levels of impairment related to climate change anxiety, the strong emotional responses expressed in interviews highlight the need for university mental health interventions, comprehensive climate change education, and inclusion of youth voices in policymaking. Understanding college students’ climate-related emotions and thoughts can inform strategies to support their well-being and engagement in the face of this crisis. Full article

Background/Objectives: Despite its benefits for both nurses and patients, entrepreneurship among nurses remains uncommon. Specifically, impact entrepreneurship has rarely been applied in nursing. Impact entrepreneurship promotes solutions to health, social, and environmental challenges using business models committed to measurable social impact and return on investment. Beginning in 2020, an impact entrepreneurship course was introduced as a mandatory component of the master’s and nurse practitioner programs in geriatrics and palliative care at an Israeli college. This article examines the perceptions of the nurses who completed the course and explores how the course affected them and their professional practice. Methods: This mixed-methods study employed a convergent design and included self-administered questionnaires sent to all graduates of two cohorts, along with a qualitative thematic analysis of students’ reflective essays written during the course. Results: Students reported that the course empowered them to be innovators and contributed to their professional and personal development. They also explained that studying impact entrepreneurship could increase their earning potential and enable them to make a substantial difference for patients. Throughout the course, participants shifted their view of entrepreneurship and realized that, given nurses’ closeness with patients, they can and should be entrepreneurs. Before the course, students were reluctant to engage in entrepreneurship; afterward, students demonstrated marked interest in pursuing entrepreneurial pathways. Conclusions: Exposure to impact entrepreneurship can help nurses identify and implement creative and cost-effective solutions to workplace challenges. The professional characteristics of nurses position them as potential leaders of impact entrepreneurship in healthcare. Educational institutions worldwide should incorporate the subject of impact entrepreneurship into curricula and practice to realize that potential. Full article

This article formalizes AI-assisted assessment as a discrete-time policy-level design for iterative feedback and evaluates it in a digitally transformed higher-education setting. We integrate an agentic retrieval-augmented generation (RAG) feedback engine—operationalized through planning (rubric-aligned task decomposition), tool use beyond retrieval (tests, static/dynamic analyzers, rubric checker), and self-critique (checklist-based verification)—into a six-iteration dynamic evaluation cycle. Learning trajectories are modeled with three complementary formulations: (i) an interpretable update rule with explicit parameters $\eta$ and $\lambda$ that links next-step gains to feedback quality and the gap-to-target and yields iteration-complexity and stability conditions; (ii) a logistic-convergence model capturing diminishing returns near ceiling; and (iii) a relative-gain regression quantifying the marginal effect of feedback quality on the fraction of the gap closed per iteration. In a Concurrent Programming course ($n=35$), the cohort mean increased from 58.4 to 91.2 (0–100), while dispersion decreased from 9.7 to 5.8 across six iterations; a Greenhouse–Geisser corrected repeated-measures ANOVA indicated significant within-student change. Parameter estimates show that higher-quality, evidence-grounded feedback is associated with larger next-step gains and faster convergence. Beyond performance, we engage the broader pedagogical question of what to value and how to assess in AI-rich settings: we elevate process and provenance—planning artifacts, tool-usage traces, test outcomes, and evidence citations—to first-class assessment signals, and outline defensible formats (trace-based walkthroughs and oral/code defenses) that our controller can instrument. We position this as a design model for feedback policy, complementary to state-estimation approaches such as knowledge tracing. We discuss implications for instrumentation, equity-aware metrics, reproducibility, and epistemically aligned rubrics. Limitations include the observational, single-course design; future work should test causal variants (e.g., stepped-wedge trials) and cross-domain generalization. Full article

Introduction: The integration of Immersive Virtual Reality (IVR) into teacher education is a significant innovation that can enhance the learning and practical training of future teachers. IVR enables highly interactive, immersive experiences in simulated educational environments where student teachers confront realistic classroom challenges. The objective was to synthesize how IVR is implemented in the training of future teachers and its level of effectiveness, in order to develop recommendations for practice and identify potential barriers to implementation. Method: A systematic review was carried out following the PRISMA model. A total of 1677 articles published in the Web of Science, Scopus, and SciELO databases were reviewed between 2021 and 2025, with 13 articles selected for analysis. Results: The reviewed articles highlight Immersive Virtual Reality (IVR) as a virtual tool that facilitates the training of future teachers. Among its most common applications are the use of virtual and augmented reality for conflict resolution, classroom management, and teacher adaptation. However, its implementation is limited by access to equipment, scenario development, and integration into university institutions. Conclusions: There is converging evidence that supports the strengths of using IVR as an emerging technology in teacher training, offering facilitating elements for the development of pedagogical competencies through the simulation of practical situations in a safe environment. Thus, this review summarizes recommendations for practice and warnings about implementation barriers, identifying the most potential uses and proposing actionable steps for its phased adoption in initial teacher training. Full article

Artificial intelligence (AI) has rapidly permeated education since 2014, propelled by technological innovation and global investment, yet scholarly discourse on contemporary AI-Education intersections remains largely fragmented. The present study addresses this notable gap through a bibliometric-driven and inductive content analysis to inform future research and practice. A total of 317 articles published between 2014 and October 2024 were retrieved from WOSCC and Scopus following the PRISMA protocol. Keyword co-occurrence and co-citation analyses with VOSviewer (version 1.6.20) were employed to visualize the intellectual structures shaping the field, while qualitative inductive content analysis was conducted to address the limitations of bibliometric methods in revealing deeper thematic insights. This dual-method approach identified four thematic clusters and eleven prevailing research trends. Subsequently, through interpretive synthesis, five interrelated research issues were identified: limited congruence between technological and pedagogical affordances, insufficient bottom-up perspectives in AI literacy frameworks, an ambiguous relationship between computational thinking and AI, a lack of explicit interpretation of AI ethics, and limitations of existing professional development frameworks. To address these gaps pragmatically, thirty issue-specific recommendations were consolidated into five overarching themes, culminating in the Integrated AI-Education Convergence Framework. This framework advocates for pedagogy-centric, ethically grounded, and contextually responsive AI integration within interdisciplinary educational research and practice. Full article

As chemical exposures are increasingly emphasised as public health concerns, understanding how people perceive chemical risks is vital for shaping responsive and inclusive human biomonitoring (HBM) programmes. Public awareness not only influences individual behaviours but can also inform national policy priorities and scientific focus. This study reports findings from the Human Biomonitoring for Ireland (HBM4IRE) feasibility study, which conducted a social survey adapted from the HBM4EU framework. The survey assessed awareness and perceived harmfulness of 24 chemical groups among 218 Irish residents, distinguishing between experts (involved in chemical management) and non-experts. Lead, arsenic, mercury, pesticides, tobacco alkaloids, volatile organic compounds (VOCs), solvents, cadmium, polycyclic aromatic hydrocarbons (PAHs), and persistent organic pollutants (POPs) received the highest perceived harmfulness scores. Non-experts reported lower perceived harmfulness for substances such as phthalates, parabens, and Per- and polyfluoroalkyl substances (PFASs), indicating significant awareness gaps. These findings demonstrate convergence between public and expert views for well-recognised substances but also highlight gaps for certain emerging chemicals. This study highlights the importance of targeted, country-specific education campaigns and shows the added value of integrating public perceptions into HBM design and priority setting. Full article

Flumequine is an antibiotic that is used to treat bacterial diseases in aquaculture. Fish express drug-metabolizing genes in response to antibiotic exposure. However, studies on the effects of high flumequine concentrations on drug metabolism genes and histopathology of the olive flounder are limited. To investigate the response of olive flounder to flumequine, we administered it at different concentrations. We analyzed the expression of drug metabolism genes (CYP) in the liver and histopathological lesions in the liver, spleen, and kidneys. The gene expression levels of CYP were higher at the highest flumequine concentration tested (4×) than at the lowest flumequine concentration (1×). The highest CYP gene expression level was observed for CYP2B4 (46.6-fold) at 4× flumequine compared to that in the control group. Hepatic atrophy, lymphocytic infiltration, and hematopoiesis were observed in the liver, spleen, and kidney at 4× flumequine between 3 and 24 h compared to 1× flumequine, respectively. These results contribute to a better understanding of drug metabolism and the general toxicity of pharmaceutical exposure in olive flounder. Full article

Background: Chronic Brain Injury (CBI) is a lifelong condition requiring continuous adaptation by patients, families, and healthcare professionals. Transitioning rehabilitation toward patient-centered and self-management approaches is essential, yet remains limited in Spain. Methods: We conducted a two-phase consensus study in collaboration with the Spanish Society of Physical Medicine and Rehabilitation (SERMEF) and the Spanish Federation of Brain Injury (FEDACE). In Phase 1, surveys were distributed to patients (214 invited; 95 complete responses, 44.4%) and physiatrists (256 invited; 106 valid responses, 41.4%) to capture perceptions of current rehabilitation practices, including tele-rehabilitation. Differences and convergences between groups were analyzed using a Synthetic Factor (F). In Phase 2, a panel of 21 experts applied a real-time eDelphi process (SmartDelphi) to assess the feasibility of proposed innovations, rated on a six-point Likert scale. Results: Patients and professionals showed both alignment and divergence in their views. Patients reported lower involvement of rehabilitation teams and expressed more reluctance toward replacing in-person care with telemedicine. However, both groups endorsed hybrid models and emphasized the importance of improved communication tools. Expert consensus prioritized feasible interventions such as online orthopedic renewal services, hybrid care models, and educational video resources, while less feasible options included informal communication platforms (e.g., WhatsApp) and bidirectional teleconsultations. Recommendations were consolidated into five domains: (R1) systemic involvement of rehabilitation teams in chronic care, (R2) patient and caregiver education, (R3) self-management support, (R4) communication tools, and (R5) socialization strategies. Conclusions: This study demonstrates the value of combining patient and professional perspectives through digital Delphi methods to co-design innovation strategies in CBI rehabilitation. Findings highlight the need to strengthen communication, provide structured education, and implement hybrid care models to advance patient-centered rehabilitation. The methodology itself fostered engagement and consensus, underscoring its potential as a tool for participatory healthcare planning. Full article

To ensure the success of early childhood education and further studies and to formulate interventions, the early diagnosis of childhood socioemotional and behavioral problems is essential. In this study, we analyzed socioemotional and behavioral difficulties in a sample of 501 Hungarian minority children (primary school children from Grades 1, 2, 3, and 4) residing in five administrative regions of Slovakia. Teachers’ and parents’ ratings on the Strengths and Difficulties Questionnaire (SDQ) were used for the assessment, and teacher–parent agreement (ICCs) from a total of 501 parents and 107 teachers were calculated to validate cross-informant convergence. The SDQ assessment tool includes a total of 25 items, which are divided into five subscales: emotional symptoms, conduct problems, hyperactivity, peer problems, and prosocial behavior. Hyperactivity was the problem most frequently reported by teachers, showing abnormal scores, while emotional symptoms were most frequently reported by parents. Demographic information, such as school grade, GPA (grade point average), and maternal education, was significantly related to the children’s SDQ scores. GPA showed a significant negative correlation with difficulties and a positive correlation with prosocial behavior. Significant differences in socioemotional and behavioral problems were also found across different grades. Inter-rater reliability analysis using intraclass correlation coefficients (ICCs) revealed fair-to-good agreement between teachers and parents (ICC range: 0.45–0.61). These findings reveal the importance of incorporating multiple informants in behavioral assessments, highlighting the need for early diagnosis and family-informed interventions to address socioemotional and behavioral problems within culturally diverse educational settings. Full article

Adolescent psychological well-being is a key component of social and educational sustainability. Strengthening well-being during the school years supports healthy development, academic engagement, and resilience to contemporary challenges. Within the framework of the United Nations 2030 Agenda for Sustainable Development, fostering mental health and equity in education contributes to more inclusive, cohesive, and peaceful communities. This study aimed to develop and validate the Psychological Well-Being Scale for Adolescents in a Chilean school population. Drawing on Ryff’s multidimensional model, the scale included 32 items and was administered to 8638 high school students from all sixteen regions of Chile. Exploratory Factor Analysis identified a seven-factor structure in which self-acceptance was divided into two dimensions: one associated with self-esteem and achievement, and another with self-appreciation and body image. Confirmatory Factor Analysis demonstrated that the seven-factor model achieved the best fit. The scale showed high internal consistency, adequate convergent validity, and measurement invariance. The Psychological Well-Being Scale for Adolescents is a psychometrically sound and culturally adapted instrument validated with Chilean adolescents in school contexts. Current evidence supports its use for research and applied assessment within Chilean educational settings. Full article

The Educational Competition Optimizer (ECO) formulates search as a three-stage didactic process—primary, secondary and tertiary learning—but the original framework suffers from scarce information exchange, sluggish late-stage convergence and an unstable exploration–exploitation ratio. We present EECO, which introduces three synergistic mechanisms: a regenerative population strategy that uses the covariance matrix of elite solutions to maintain diversity, a Powell mechanism that accelerates exploitation within promising regions, and a trend-driven update that adaptively balances exploration and exploitation. EECO was evaluated on the 29 benchmark functions of CEC-2017 and nine real-world constrained engineering problems. Results show that EECO delivers higher solution accuracy and markedly smaller standard deviations than eight recent algorithms, including EDECO, ISGTOA, APSM-jSO, LSHADE-SPACMA, EOSMA, GLSRIME, EPSCA, and ESLPSO. Across the entire experimental battery, EECO consistently occupied the first place in the Friedman hierarchy: it attained average ranks of 2.138 in 10-D, 1.438 in 30-D, 1.207 in 50-D, and 1.345 in 100-D CEC-2017 benchmarks, together with 1.722 on the nine real-world engineering problems, corroborating its superior and dimension-scalable performance. The Wilcoxon rank sum test confirms the statistical significance of these improvements. With its remarkable convergence accuracy and reliable stability, EECO emerges as a promising variant of the ECO algorithm. Full article

International postgraduate students enrich higher education institutions and host societies, contributing economically, socially, and culturally. However, much less is known about how to improve their academic outcomes compared with their undergraduate counterparts. This study explores the impact of a non-traditional form of learning, a six-week immersive block model underpinned by guided, active learning pedagogy, on the academic success, satisfaction, and experiences of international postgraduate students at an Australian university. A convergent mix-methods design was used. Chi square tests and generalised estimating equations were used to compare the students’ success rates (N = 14,340) and unit satisfaction (N = 4903) in traditional semester and immersive block learning over five years. Qualitative insights were gathered via student focus groups (N = 9). Significant positive changes in success were observed after controlling for gender, age, discipline, and home region, with particularly strong positive effects for male and information technology students. Despite some challenges with depth of learning and placement organisation, focus group participants valued the clear timelines and flexible delivery, reporting that this supported effective time management and study-work–life-balance. Immersive block learning appears to be an effective strategy for transforming the experiences and outcomes of international postgraduate students in higher education. Full article

Social trust is a crucial factor influencing adolescents’ mental health and serves as a cornerstone for social stability. However, there is a lack of a reliable measurement tool specifically designed to assess the psychological characteristics of adolescents’ social trust. This study aimed to develop and validate the Chinese Adolescent Social Trust Scale (STS-CA). Semi-structured interviews were conducted with 45 adolescents (aged 12 to17 years) to generate an initial pool of scale items. Subsequently, eighteen psychological experts evaluated the content validity, and the scale was revised based on their feedback, resulting in a preliminary version. A total of 2036 secondary school students were randomly divided into Sample 1 and Sample 2. Sample 1 (N1 = 1018) was used in item analysis and exploratory factor analysis (EFA). Sample 2 (N2 = 1018) was utilized for confirmatory factor analysis (CFA). Sample 3 (N3 = 1214) was recruited to assess the scale’s reliability, validity, and measurement equivalence. Two months later, test–retest reliability analysis was assessed using Sample 4 (N4 = 303). The final STS-CA consists of 27 items covering four factors, namely trust in relatives, trust in friends, trust in strangers, and trust in organizations. The scale demonstrated good internal consistency reliability, test–retest reliability, convergent validity, and discriminant validity. Correlations between the STS-CA subscale scores and external criterion variables—interpersonal trust, trust propensity, and life satisfaction—supported criterion validity. Additionally, the scale exhibited good measurement equivalence across gender and educational stages. Overall, our findings demonstrate that the STS-CA is a reliable and valid instrument for assessing social trust levels among Chinese adolescents. Full article

Financial sustainability in higher education is increasingly fragile due to policy shifts, rising costs, and funding volatility. Legacy early-warning systems based on static thresholds or rules struggle to adapt to these dynamics and often overlook fairness and interpretability—two essentials in public-sector governance. We propose a university financial risk early-warning framework that couples a causal-attention Transformer with Multi-Objective Bayesian Optimization (MBO). The optimizer searches a constrained Pareto frontier to jointly improve predictive accuracy (AUC↑), fairness (demographic parity gap, DP_Gap↓), and computational efficiency (time↓). A sparse kernel surrogate (SKO) accelerates convergence in high-dimensional tuning; a dual-head output (risk probability and health score) and SHAP-based attribution enhance transparency and regulatory alignment. On multi-year, multi-institution data, the approach surpasses mainstream baselines in AUC, reduces DP_Gap, and yields expert-consistent explanations. Methodologically, the design aligns with LLM-style time-series forecasting by exploiting causal masking and long-range dependencies while providing governance-oriented explainability. The framework delivers earlier, data-driven signals of financial stress, supporting proactive resource allocation, funding restructuring, and long-term planning in higher education finance. Full article