Search Results (259)

Systems thinking is not a static concept, but rather a dynamic and evolving paradigm that continually adapts to the challenges of its time, becoming more refined and applicable in different areas, such as education. The main objective of the study is to identify the relationship between academic performance and the pedagogical strategies used to promote systems thinking in undergraduate and graduate students in STEM disciplines (science, technology, engineering, and mathematics). The method used is quantitative research with a non-experimental cross-sectional design. For data collection, a 25-item Likert scale called “STEM Pedagogical Strategies” was used, with an overall Cronbach’s alpha coefficient of α = 0.985. The instrument measures students’ perceptions of the application of five key strategies: problem-based learning, thinking routines, system maps and visual diagrams, design thinking, and system dynamics. The sample consisted of 350 undergraduate and graduate students in STEM fields. The main results show that there is a significant correlation between students’ academic performance and the pedagogical strategies of thinking routines and design thinking. Likewise, the skills developed through systems thinking, as shown in the available literature, would be the basis for fostering collaboration, complex problem solving, and students’ ability to become “systems”. Full article

Vision-language models (VLMs) integrate visual and textual information and are increasingly being used as innovative tools in educational applications. However, there is a lack of evidence regarding current practices for integrating VLMs into teaching and learning. To address this research gap and identify the opportunities and challenges associated with the integration of VLMs in education, this paper presents a systematic review of VLM use in formal educational contexts. Peer-reviewed articles published between 2020 and 2025 were retrieved from five major databases: ACM Digital Library, Scopus, Web of Science, Engineering Village, and IEEE Xplore. Following the PRISMA-guided framework, 42 articles were selected for inclusion. Data were extracted and analyzed against six research questions: (1) where VLMs are applied across academic disciplines and educational levels; (2) what types of VLM solutions are deployed and which image–text modalities they infer and generate; (3) the pedagogical roles of VLMs within teaching workflows; (4) reported outcomes and benefits for learners and instructors; (5) challenges and risks identified in practice, together with corresponding mitigation strategies; and (6) reported evaluation methods. The included studies span K-12 through higher education and cover diverse disciplines, with deployments dominated by pre-trained models and a smaller number of domain-adapted approaches. VLM-supported pedagogical functions cluster into five roles: analyst, assessor, content curator, simulator, and tutor. This review concludes by discussing implications for VLM adoption in educational settings and offering recommendations for future research. Full article

Categorical data analysis offers valuable tools for evaluating school-based prevention programs, yet these methods remain rarely applied in cyberbullying research. This literature review examined how categorical approaches, including contingency tables and related techniques, have been used in studies evaluating school-based cyberbullying prevention. A comprehensive search was conducted in Web of Science covering publications from 2020 to 2025, yielding 100 articles. After applying predefined inclusion and exclusion criteria, 24 studies were reviewed in full, of which 8 met all requirements for final analysis. The results revealed a predominant reliance on linear statistical techniques, such as t-tests, ANOVA, and regression models, applied mainly to continuous variables. By contrast, categorical analyses were seldom employed. The chi-square test appeared as the most frequent approach, but its use was generally restricted to descriptive purposes, with little application of complementary methods such as standardized residuals, effect size measures, or logistic models. This restricted application reduced the ability to capture response patterns, subgroup differences, and categorical associations essential for evaluating program outcomes. The findings highlight a methodological gap in cyberbullying prevention research and emphasize the potential of categorical data analysis to enrich interpretation. Incorporating these methods could increase methodological rigor, reveal nuanced behavioral patterns, and provide actionable evidence for educators, policymakers, and program designers seeking to strengthen school-based prevention strategies. Full article

The integration of large language models (LLMs) and fuzzy metrics offers new possibilities for improving automated grading in programming education. While LLMs enable efficient generation and semantic evaluation of programming assignments, traditional crisp grading schemes fail to adequately capture partial correctness and uncertainty. This paper proposes a grading framework in which LLMs assess student solutions according to predefined criteria and output fuzzy grades represented by trapezoidal membership functions. Defuzzification is performed using the centroid method, after which fuzzy distance measures and fuzzy C-means clustering are applied to correct grades based on cluster centroids corresponding to linguistic performance levels (poor, good, excellent). The approach is evaluated on several years of real course data from an introductory programming course with approximately 800 students per year called “Programski jezici i strukture podataka” in the first year of studies of multiple study programs at the Faculty of Technical Sciences, University of Novi Sad, Serbia. Experimental results show that direct fuzzy grading tends to be overly strict compared to human grading, while fuzzy metric correction significantly reduces grading deviation and improves alignment with human assessment, particularly for higher-performing students. Combining LLM-based semantic analysis with fuzzy metrics yields a more nuanced, interpretable, and adaptable grading process, with potential applicability across a wide range of educational assessment scenarios. Full article

Background: Artificial intelligence (AI) is increasingly used in intensive care units (ICUs) to enable personalized care, real-time analytics, and decision support. Nutritional therapy—a major determinant of ICU outcomes—often remains delayed or non-individualized. Objective: This study aimed to review current and emerging AI applications in ICU nutrition, highlighting clinical potential, implementation barriers, and ethical considerations. Methods: A narrative review of English-language literature (January 2018–November 2025) searched in PubMed/MEDLINE, Scopus, and Web of Science, complemented by a pragmatic Google Scholar sweep and backward/forward citation tracking, was conducted. We focused on machine learning (ML), deep learning (DL), natural language processing (NLP), and reinforcement learning (RL) applications for energy/protein estimation, feeding tolerance prediction, complication prevention, and adaptive decision support in critical-care nutrition. Results: AI models can estimate energy/protein needs, optimize EN/PN initiation and composition, predict gastrointestinal (GI) intolerance and metabolic complications, and adapt therapy in real time. Reinforcement learning (RL) and multi-omics integration enable precision nutrition by leveraging longitudinal physiology and biomarker trajectories. Key barriers are data quality/standardization, interoperability, model interpretability, staff training, and governance (privacy, fairness, accountability). Conclusions: With high-quality data, robust oversight, and clinician education, AI can complement human expertise to deliver safer, more targeted ICU nutrition. Implementation should prioritize transparency, equity, and workflow integration. Full article

The objective of this study is to assess the extent to which science teachers implement (SERTPs) and to examine whether these practices differ according to selected demographic and professional variables. Using a descriptive–analytical design, data were collected from 225 science teachers enrolled in graduate programs at King Khalid University during the 2025–2026 academic year. The findings reveal a high overall level of SERTPs (M = 2.45; 81.81%). The highest-scoring dimensions were Enhancing Students’ Environmental Awareness (86.59%) and Using Sustainable Resources in Teaching (84.00%), while Encouraging Community Participation showed the lowest application level (77.95%). No significant differences were found across gender, teaching stage, academic qualification, or age; however, a significant difference emerged in favor of teachers with a high level of technology use (p < 0.001). These results underline the vital role of technological integration in strengthening sustainable teaching practices. The study recommends targeted professional development, sustainability-centered curriculum enhancement, and institutional support to align science education with global Education for Sustainable Development (ESD) goals. Full article

Astronomy provides an exceptional context for developing data literacy, critical thinking, and computational skills in education. This paper presents a project-based learning (PBL) framework that integrates exploratory data analysis (EDA), fuzzy logic, and explainable artificial intelligence (XAI) to teach students how to extract and interpret scientific knowledge from real astronomical data. Using open-access resources such as NASA’s JPL Horizons and ESA’s Gaia DR3, together with Python libraries like Astroquery and Plotly, learners retrieve, process, and visualize dynamic datasets of comets, asteroids, and stars. The methodology follows the full data science pipeline, from acquisition and preprocessing to modeling and interpretation, culminating with the application of the FAS-XAI framework (Fuzzy-Adaptive System for Explainable AI) for pattern discovery and interpretability. Through this approach, students can reproduce astronomical analyses and understand how data-driven methods reveal underlying physical relationships, such as orbital structures and stellar classifications. The results demonstrate that combining EDA with fuzzy clustering and explainable models promotes deeper conceptual understanding and analytical reasoning. From an educational perspective, this experience highlights how inquiry-based and computationally rich activities can bridge the gap between theoretical astronomy and data science, empowering students to see the Universe as a laboratory for exploration, reasoning, and discovery. This framework thus provides an effective model for incorporating artificial intelligence, open data, and reproducible research practices into STEM education. Full article

Innovation in the new energy industry serves not only as a key accelerator for the global green and low-carbon energy transition but also as a core driving force of the ongoing energy revolution. This study utilizes data on publications, patents, and the spatial distribution of representative innovation enterprises in the new energy industry of the Yangtze River Delta urban agglomeration from 2009 to 2023 to construct a multilayer science–technology–industry innovation network. Social network analysis is employed to examine its evolutionary dynamics and structural characteristics, and the Quadratic Assignment Procedure (QAP) is used to investigate the factors shaping intercity innovation linkages. The results reveal that the multilayer innovation network has continuously expanded in scale, gradually forming a multi-core radiative structure with Shanghai, Nanjing, and Hangzhou at the center. At the cohesive subgroup level, the scientific and technological layers exhibit clear hierarchical differentiation, where core cities tend to engage in strong mutual collaborations, while the industrial layer shows a hub-and-spoke pattern combining large, medium, and small cities. In terms of layer relationships, the centrality of the scientific layer increasingly surpasses that of the technological and industrial layers. Inter-layer degree correlations and overlaps also display a strengthening trend. Furthermore, differences in regional higher education scale, urban economic density, and geographic proximity are found to exert significant influences on scientific, technological, and industrial innovation linkages among cities. In response, this study recommends enhancing the leadership role of core cities, leveraging the bridging and intermediary functions of peripheral cities, and promoting application-driven cross-regional innovation collaboration, thereby building efficient science–technology–industry networks and enhancing intercity innovation linkages and the flow of innovation resources, and ultimately promoting the high-quality development of the regional new energy industry. Full article

This interdisciplinary study presents a novel questionnaire analysis methodology using Artificial Intelligence (AI) and Machine Learning (ML). The framework is broadly applicable to all areas of research using questionnaire data analysis, including health sciences and physical education. Our predictive modeling was based on the XG-Boost algorithm, which classified individuals into three distinct groups—employees and two cohorts of retirees—based on their demographic profiles and responses to the WHOQOL-BREF survey. In order to ensure the credibility and reliability of the predictions, the model building process used the implementation of cross-validation. This procedure produced a model with a resultant accuracy of 0.8038 ($95\%$ confidence interval: 0.7551–0.8908). To go beyond conventional performance metrics, we implemented the SHapley Additive exPlanations (SHAP) method, providing a transparent and detailed interpretation of the model’s decision-making process. This explainable AI analysis clarifies both the magnitude and direction of the impact of key factors such as age and various predictors of quality of life, providing detailed, data-driven insights into what differentiates groups. Full article

Background/Objectives: Evidence-Based Practice (EBP) positively impacts health safety and quality while also empowering nursing as a discipline. A useful strategy for promoting EBP is to build learning paths adapted to the individuality of nurses. These elements establish the framework for effective learning, determining the availability of specific content at certain times and influencing the design of learning objects to ensure optimal efficacy in the teaching-learning process. It is essential to identify effective strategies in evidence-based nursing education to advance EBP and thereby enhance the quality and safety of nursing care. This review aims to summarize the evidence on the effectiveness of learning paths in the acquisition of EBP competencies by nurses. Methods: A systematic review of the literature will be carried out in accordance with the Joanna Briggs Institute (JBI) methodology for systematic reviews of effectiveness. The results of the review will be reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocols (PRISMA-P). The protocol is registered in the PROSPERO database (CRD4202453155). The search will be performed using the EBSCOhost search engine in the following databases: CINAHL Plus, MedicLatina, MEDLINE, Psychology and Behavioral Sciences Collection, Academic Search Complete, eBook Collection, and Education Resources Information Center. The inclusion of studies, data extraction, and analysis will be carried out by two reviewers independently. Disagreements will be resolved by a third reviewer. All studies involving nurses, learning paths, EBP competencies, regardless of geographical area and context, with no time limit or language constraints, will be included. Results: Not applicable; this is a protocol. Findings will be synthesized as specified in the Methods. Conclusions: This review will provide a better understanding of the effectiveness of a learning path in the acquisition of EBP competencies by nurses. It will also assist in the identification of knowledge gaps in the literature and potential areas for future research and development. Full article

Technologies such as XR (Extended Reality), in the form of VR (Virtual Reality), AR (Augmented Reality) and MR (Mixed-Reality), are being researched for their potential to support higher education. XR offers novel opportunities for improving understanding and engagement of computer science (CS) courses, abstract and algorithmic thinking and the application of knowledge to solve problems with computers. This narrative literature review aims to report the state of XR adoption in the university CS education context by studying pedagogical benefits, representative cases, challenges, and future research work. Recent case studies have demonstrated that VR innovations are supportive of algorithm and data structure visualization, AR in programming and circuit analysis contextualization, and MR in bridging the experimental practice on virtual with real hardware within computer labs. The potential of XR to enhance engagement, motivation, and complex content understanding has already been researched. However, ongoing obstacles remain such as the high cost of hardware, technical issues in practicing scalable content, restricted access for students with disabilities, and ethical considerations over privacy and data protection. This review also presents XR, not as a substitute for traditional pedagogy, but as an additive tool that, in alignment with well-defined curricular objectives, may enhance CS learning. If it overcomes these deficiencies and progresses appropriate inclusive evidence-based practices, XR has the potential to play a powerful role in the future of computer science education as part of the digital learning ecosystem. Full article

Following PRISMA-ScR guidelines, this scoping review systematically maps the landscape of Large Language Models (LLMs) in mechanical engineering. A search of four major databases (Scopus, IEEE Xplore, ACM Digital Library, Web of Science) and a rigorous screening process yielded 66 studies for final analysis. The findings reveal a nascent, rapidly accelerating field, with over 68% of publications from 2024 (representing a year-on-year growth of 150% from 2023 to 2024), and applications concentrated on front-end design processes like conceptual design and Computer-Aided Design (CAD) generation. The technological landscape is dominated by OpenAI’s GPT-4 variants. A persistent challenge identified is weak spatial and geometric reasoning, shifting the primary research bottleneck from traditional data scarcity to inherent model limitations. This, alongside reliability concerns, forms the main barrier to deeper integration into engineering workflows. A consensus on future directions points to the need for specialized datasets, multimodal inputs to ground models in engineering realities, and robust, engineering-specific benchmarks. This review concludes that LLMs are currently best positioned as powerful ‘co-pilots’ for engineers rather than autonomous designers, providing an evidence-based roadmap for researchers, practitioners, and educators. Full article

The primary objective of this research is to identify the most influential factors in the digital platforms used by cancer patients and their environments for accessing oncopaediatric information. This is a PRISMA-guided systematic review that synthesises studies published between 2004 and January 2023 and does not report new primary data. We explore the drivers and barriers of web-based platforms, health apps and social media. We conducted a literature review guided by the PICOS strategy: (P) children and adolescents; (I) factors affecting the use of health apps and social media; (C) without a specific comparison; (O) measuring impact, understanding and success factors; (S) using a conceptual approach. Our study reveals a dual dynamic in paediatric oncology science communication, in which drivers (information, collaborative efforts, comprehensive education) and barriers (age-appropriate content, misinformation) shape the complex communication landscape. The reality is that a healthcare application is needed that focuses on extensive education and the paediatric patient’s involvement in understanding and improving their well-being. It requires adapting communication strategies. Additionally, we explore the theory of online health communication and identify several promising avenues for research. Full article

Background: Artificial intelligence (AI) and machine learning (ML) have rapidly expanded across the continuum of cardiopulmonary resuscitation (CPR), with growing evidence of their contribution to improving early recognition, intervention quality, and post-cardiac arrest outcomes. This narrative review synthesizes the current advancements and challenges in AI/ML-enhanced resuscitation science. Methods: A targeted literature search was conducted in Web of Science for the period 2018–2025 using the keywords “artificial intelligence” and “cardiopulmonary resuscitation”. The search identified studies addressing AI/ML applications across the resuscitation pathway, which were reviewed and categorized according to the American Heart Association’s Chain of Survival—prevention and preparedness, activation of the emergency response system, high-quality CPR including early defibrillation, advanced resuscitation interventions, post-cardiac arrest care, and recovery. Results: The literature demonstrates substantial promise for AI/ML in several domains: (1) early recognition and timely activation of emergency medical services through real-time detection algorithms; (2) optimization of high-quality CPR, including feedback systems, automated assessment of chest compressions, and prediction of defibrillation success; (3) support for advanced resuscitation interventions, such as rhythm classification, prognostication, and intra-arrest decision support; (4) post-cardiac arrest care, including outcome prediction and neuroprognostication; and (5) integrative and cross-domain approaches that link multiple phases of resuscitation into end-to-end AI-supported systems. Emerging work also highlights the role of AI in education and training, with applications in simulation, assessment, and skill reinforcement. Conclusions: AI/ML technologies hold significant potential to augment clinical performance across all links of the Chain of Survival. Their effective implementation requires attention to ethical considerations, data representativeness, and real-world validation. Future research should prioritize multicenter datasets, transparency, bias mitigation, and clinically embedded evaluation frameworks to ensure that AI/ML systems support safe, equitable, and high-impact resuscitation care. Full article

This review systematically examines electroencephalography (EEG) studies on gifted children, focusing on the signal processing pipelines across acquisition, preprocessing, feature extraction, and analysis, and identifying opportunities for methodological standardisation relevant to educational research. Following PRISMA 2020 guidelines, a comprehensive search was carried out in PubMed, Scopus, Web of Science, IEEE Xplore, and PsycINFO. From 197 records, 14 studies met the inclusion criteria and were analysed for EEG setup, preprocessing strategies, and analytical approaches, including event-related potentials, spectral and connectivity measures, and applications of machine learning. Substantial heterogeneity was observed in device configurations, preprocessing practices, and analytical choices, limiting cross-study comparability and the transfer of findings to educational contexts. Nevertheless, recurring neurophysiological markers were identified, such as P300, frontoparietal $\gamma$ synchronisation, and $\theta$–$\alpha$ modulations during cognitive tasks. Only a minority of studies implemented supervised classification methods, suggesting an underexplored potential for advanced data-driven approaches in paediatric EEG. Transparent and standardised EEG pipelines, with explicit reporting of filters, artefact thresholds, and rejection rates, are essential to enhance reproducibility and translational value. By framing EEG signal processing within an educational perspective, this review provides methodological guidance to support early identification, inform classroom practice, and strengthen the bridge between neuroscience and education. Full article