Search Results (352)

Mathematical knowledge is essential for adult learners’ advancement in academic and professional settings; however, instructional strategies for adult learners in higher education often emphasize memorizing procedures while neglecting their personal and professional experiences. Such approaches limit opportunities to leverage these experiences for developing meaningful mathematical understanding. Grounded in the Models and Modeling Perspective, this exploratory qualitative case study examines how a Model Development Sequence (MDS) supports the development of mathematical knowledge of adult learners returning to higher education. The participants were a group of seven first-year business adult learners enrolled in the Applied Mathematics in Business course at a higher education institution. Data were analyzed using protocol coding to describe the types of mathematical models the participants constructed. Findings indicate that participants progressed from creating models requiring redirection, grounded in proportional reasoning, to developing more sophisticated models based on linear and exponential functions. The MDS supported learners in refining, extending, and adapting their models, strengthening their conceptual understanding of variation, linear and exponential functions, and covariational reasoning. Moreover, the participants’ personal and professional experiences were central to model development. This study contributes to research on adult mathematics education by demonstrating the potential of MDS to support meaningful mathematical learning. Full article

Background/Objectives: Simulation digital twins (DT) models that integrate patient-specific imaging with artificial intelligence (AI)-based segmentation and extended reality (XR) technologies are rapidly increasing in relevance in personalized medicine. While their clinical applications are expanding, their role as reusable educational tools and the technical pipeline utilized for their development remain incompletely characterized. This narrative review examines current approaches to digital twin creation and XR integration, illustrated by a scoliosis-specific proof-of-concept educational case study. Methods: A narrative technical review was conducted by identifying relevant search keywords within the fields of AI-based image segmentation, extended reality in medicine, and medical education based on the authors’ expertise and familiarity with the subject. PubMed, Google Scholar, and Scopus were searched for English-language studies published primarily between 2015 and 2025 addressing patient-specific three-dimensional modeling, AI-driven segmentation, and XR applications in spine, orthopedic, anesthesiology, and interventional care. A de-identified case of scoliosis is used to present a proof-of-concept example of this process of creating a simulated digital twin for the purpose of medical education in a recorded XR format. Results: Prior studies demonstrated benefits of patient-specific 3D models for anatomical understanding and procedural planning, while highlighting limitations in segmentation accuracy and workflow integration. Nevertheless, while DTs have traditionally served clinical roles in surgical planning or pre-procedural rehearsal, their pedagogical potential remains under-explored. In the proof-of-concept case study, AI-assisted segmentation enabled rapid creation of an anatomically detailed scoliosis digital twin that was incorporated into XR and used to produce a reusable, spatially anchored instructional experience focused on neuraxial access. Conclusions: AI-enabled digital twin models integrated with XR represent a promising approach for personalized, anatomy-driven medical education. Further evaluation is needed to assess educational outcomes, scalability, and integration into clinical training workflows. Full article

To enhance flight safety and operational efficiency, extracting key actions, flight parameters, and status information from civil aviation flight crew instructions generated during pre-flight and in-flight procedures is crucial. However, such texts are highly condensed and involve complex multi-role interactions, easily leading to entity boundary drift and category misclassification. To address this, this paper proposes a joint key information extraction framework based on a lightweight pre-trained language model (TinyBERT) and a Role-Aware Fusion mechanism, abbreviated as TR-BiGRU-CRF. This framework introduces the Role-Aware Fusion mechanism to resolve semantic ambiguity caused by multi-party interactions, utilizes TinyBERT for semantic representation that balances accuracy and computational efficiency, and employs BiGRU-CRF for robust sequence feature modeling and decoding. Experiments on a flight crew instruction dataset show that the proposed method achieves 92.2% precision, 91.8% recall, a 92.0% F1 score, and an overall prediction accuracy of 92.6%. Compared to the BiGRU-CRF baseline, it significantly improves accuracy, precision, and F1 score by 11.4, 13.3, and 13.5 percentage points, respectively. These results prove that the proposed method effectively mitigates boundary drift and category confusion, providing strong support for flight crew instruction understanding and safety decision-making. Full article

Background and Clinical Significance: The optimization of soft tissue healing following oral surgical procedures remains a key factor for achieving long-term functional and esthetic success. This article aims to explore the clinical application and healing potential of zinc-containing stents in the management of various oral soft tissue conditions. Case Presentation: Four clinical cases involving different etiologies of soft tissue lesions were included: (1) persistent pregnancy-associated gingival enlargement, (2) prosthesis-related gingival inflammation, (3) plaque-induced gingivitis, and (4) palatal thermal injury.Zinc-containing stents were fabricated from preheated granulate and applied following initial or supportive plaque control. Patients were instructed to wear the stents for a prescribed period. Clinical parameters, including the full mouth plaque score (FMPS), full mouth bleeding score (FMBS), tissue appearance, and patient comfort, were evaluated during follow-up. All four patients demonstrated complete resolution of clinical signs, including reduced inflammation, improved gingival contour, and accelerated tissue healing, without reported discomfort or adverse effects. In inflammatory cases, FMPS and FMBS values decreased markedly after stent use, while the palatal burn lesion showed complete re-epithelialization within five days. No adverse effects or complications were observed during follow-up periods ranging from one week to one year for the different cases. Conclusions: Zinc-containing stents show promising clinical potential as adjunctive tools in the management of periodontal and oral mucosal conditions. Their bioactive properties—anti-inflammatory, antimicrobial, and regenerative—may enhance soft tissue healing and patient comfort. Further controlled clinical studies are needed to establish standardized treatment protocols and optimize zinc formulations for wider adoption in clinical practice. Full article

Background/Objectives: Oral health is an essential component of general health, particularly in hospitalized pediatric patients undergoing surgery. Hospitalization may disrupt oral hygiene routines and dietary habits, increasing the risk of oral health deterioration. This prospective observational study aims to develop a standardized oral care protocol for pediatric patients hospitalized for surgical procedures by evaluating changes in oral health status, oral hygiene practices, and dietary habits between hospital admission and discharge. Methods: Children aged 0–17 years undergoing surgery and hospitalized for at least three nights were enrolled. Clinical oral examinations and caregiver-administered questionnaires were performed at admission and at discharge. Oral health status, plaque accumulation, gingival condition, oral pain, hygiene behaviors, and dietary habits were assessed. Results: In total, 118 patients were included. During hospitalization, plaque accumulation significantly increased and oral hygiene practices worsened. Dietary habits changed, with fewer daily meals and a slight reduction in cariogenic food and beverage intake. Oral hygiene instructions or dental examinations were documented in only 2.5% of patients. Based on these observations, a protocol was developed targeting hospitalized patients, their families, and healthcare staff, with the aim of improving oral health conditions during hospitalization. Conclusions: Pediatric surgical hospitalization is associated with a deterioration in oral hygiene behaviors and increased plaque accumulation. The implementation of standardized protocols and the dissemination of preventive oral health knowledge may transform hospitalization into an opportunity to improve oral health in children and adolescents. Full article

Information visualization is a crucial yet often underdeveloped research skill in graduate education. This study examined how practice-based visualization instruction enhances graduate students’ conceptual understanding and procedural competence in scientific graph construction. Forty first-year graduate students participated in a ten-week instructional program combining diagnostic assessment, guided exercises, and a complex graph replication task. Conceptual and procedural competence were evaluated using validated analytic rubrics to ensure reliability and depth of analysis. Results showed substantial improvement in students’ ability to select suitable chart types, label axes accurately, and apply coherent color schemes. Consistent with the study’s hypotheses, significant gains were observed in conceptual understanding (H1) and technical execution (H2), and a moderate positive correlation between the two domains (H3) confirmed that stronger conceptual grasp aligned with higher visualization proficiency. Iterative feedback and guided reflection supported the integration of theory and practice. However, challenges in detailed annotation and multivariable coordination persisted. Overall, structured, practice-based visualization training enhanced methodological competence and communication clarity. Embedding such experiential learning within graduate curricula can strengthen visualization literacy and support the development of research independence. Full article

Academic functioning in university students emerges from the interplay of motivational, self-regulatory, emotional, and contextual processes. The present study examined the network structure linking academic motivation, self-regulated learning, academic engagement, academic burnout, generalized anxiety, self-esteem, and students’ ratings of instruction. Participants were 530 university students from Western Romania (Mage = 28.86, SD = 9.75; 87.5% women). Data were collected through an online cross-sectional survey using validated self-report instruments. A Gaussian Graphical Model was estimated using the EBICglasso procedure to examine the unique associations among the study variables and their relative structural importance within the network. The results indicated a moderately dense psychological network, with academic burnout emerging as the most structurally central node. Intrinsic motivation toward achievement, identified regulation, and performance control were positioned within the adaptive core of the network, whereas burnout, anxiety, amotivation, and low self-esteem clustered within the maladaptive region. Academic engagement occupied an intermediary position linking motivational and self-regulatory processes. Overall, the findings support a systems-oriented interpretation of academic functioning, suggesting that burnout represents a key convergence point in students’ psychological functioning, while self-determined motivation and self-regulated learning may serve as protective processes. These results highlight the value of network analysis for identifying psychologically meaningful intervention targets in higher education. Full article

Modern machine tools are safety-critical mechatronic systems, yet shop floor maintenance from abnormal events still relies heavily on scarce expert know-how and time-consuming manual searches across heterogeneous controller documentation. This paper presents an on-premise multimodal AI assistant. It integrates large language models with retrieval augmented generation and real-time machine signals to support operator-in-the-loop fault diagnosis. The proposed system provides three tightly coupled functions: (1) alarm-grounded guidance, which answers controller alarms and recommends corrective actions by grounding generation on manuals, maintenance procedures, and historical alarm cases; (2) parameter-aware reasoning, which injects live process and health indicators (e.g., spindle temperature, vibration, and axis states) into the reasoning context through an industrial data pipeline, enabling context specific troubleshooting; and (3) vision enabled support, which retrieves similar visual cases and generates concise visual instructions when text alone is insufficient. The assistant is deployed within an intranet environment to satisfy industrial security and privacy requirements and is orchestrated via lightweight tool calling for seamless integration with existing shop floor systems. Experiments on real machine tool alarm scenarios demonstrate that the proposed system achieves 82% answer correctness for alarm Q&A and improves response consistency and time-to-resolution compared with baseline keyword search and template-based guidance. The results suggest that grounded, multimodal chatbot assistants can act as practical AI-based feedback and decision support mechanisms for mechatronic production equipment, bridging human skill gaps while enhancing reliability and maintainability. Full article

The growing complexity and interdependence of critical infrastructures (CIs), increasingly exposed to natural and technological hazards, call for educational approaches to enhance resilience and risk management. This study examines trends, patterns, and challenges in integrating digital and immersive technologies into education and training for stakeholders in critical infrastructure management. A systematic review of peer-reviewed literature was conducted using Scopus as the primary source, covering the last decade and analyzing the corpus across six dimensions: technological approach, pedagogical model, hazard typology, infrastructure domain, stakeholder category, and implementation phase. Following the PRISMA framework, 5635 records were identified and screened through a multistage process combining rule-based filtering and manual review, resulting in 105 papers meeting the inclusion criteria. The analysis reveals a shift from classroom instruction and physical drills toward immersive, simulation-based, and data-informed learning ecosystems that strengthen situational awareness, procedural accuracy, and decision-making under stress. However, the review identifies persistent gaps in evaluation metrics, cross-sector frameworks, and collaborative learning environments that limit adoption. The findings underscore that digital and immersive technologies can reconfigure education and training frameworks, enabling the formation of Resilient Operators endowed with adaptive cognition, continuous learning capacities, and responsiveness to natural hazard-induced technological risks. Full article

Traditional craft education relies heavily on hands-on practice; however, novice learners often struggle with procedural complexity, material behavior, and the tacit knowledge typically transmitted through prolonged apprenticeship. This paper presents an integrated framework that combines semantic Knowledge Graphs (KGs), real-time Finite Element Method (FEM) simulation, and high-fidelity physically based rendering (PBR) to support the teaching, understanding, and preservation of traditional crafts. Craft processes are modelled as ontologically grounded KGs that capture tools, materials, actions, decision points, and common procedural errors through an extensible representation aligned with CIDOC-CRM. These semantic structures drive an interactive FEM-based simulation that enables learners to enact craft actions in a virtual environment while receiving predictive feedback and corrective guidance derived from expert-defined execution parameters. The resulting workpiece states are visualized using PBR techniques, providing perceptually accurate cues essential for assessing surface changes, deformation patterns, and material conditions. The methodology is embedded within an eLearning ecosystem that supports the generation of structured courses, multimodal exemplars, and instructional design informed by Cognitive Load Theory. A use case involving wood and aluminum carving demonstrates the system’s ability to simulate realistic tool–material interactions and produce visually interpretable outcomes. The results indicate that coupling executable semantic knowledge modelling with physically grounded simulation offers a viable pathway toward scalable, safe, and contextually rich craft training while supporting the long-term preservation of domain expertise. Full article

Deploying Large Language Models (LLMs) on Maritime Autonomous Surface Ships (MASS) entails a critical trade-off between reasoning depth, inference latency, and hardware constraints. To fill the existing gap, we introduce MARTIAN (Maritime Agent for Real-time Tactical Inference And Navigation), a 14B-parameter decision support agent engineered for edge deployment on standard vessel hardware (e.g., the NVIDIA Jetson AGX Orin). Central to our approach is the Cognitive Core architecture, which utilizes a verified dataset of 21,800 Chain-of-Thought (CoT) instruction–response pairs to align general linguistic capabilities with maritime procedural logic. Empirical evaluations demonstrate that MARTIAN achieves an overall accuracy of 73.23% (SFT only) and 81.16% (SFT + RAG) on the Bilingual Maritime Multiple-Choice Questionnaire (BM-MCQ), a standardized assessment dataset constructed based on Officer of the Watch (OOW) competencies. Notably, the SFT-only configuration attains 78.53% on pure-logic-intensive COLREG tasks—surpassing the 72B-parameter Qwen-2.5 foundation model in this domain—while maintaining a real-time inference latency of 22.4 ms/token. Crucially, our ablation studies support a nuanced Interference Hypothesis: while RAG significantly enhances factual recall in knowledge-intensive domains (boosting total accuracy from 73.23% to 81.16%), it concurrently introduces semantic noise that degrades performance in pure logic reasoning tasks (e.g., COLREG maneuvering accuracy decreases from 78.53% to 77.36%). On the basis of this finding, we identify and empirically motivate a decoupled cognitive design principle that separates procedural reflexes (via SFT) from declarative knowledge (via RAG). While the full implementation of an adaptive routing mechanism is deferred to future work, the ablation results presented herein offer a validated, cost-effective reference architecture for deploying transparent and regulation-compliant AI on resource-constrained merchant vessels. Full article

The challenge of designing generative AI (GenAI) tutors that are both pedagogically sound and effective for guided inquiry remains significant. This paper introduces and evaluates a replicable design framework-termed a Pedagogy-to-Prompt Engineering Framework-that systematically translates established pedagogical models into structured AI interactions. We engineered a 5E-structured GenAI coach by integrating the 5E Learning Cycle as the instructional architecture and the 5S Prompting Principles to govern the AI’s dialogue. The coach was evaluated in a middle school chemistry context (N = 60) focusing on procedural skill acquisition for balancing chemical equations. A quasi-experimental study showed the GenAI group achieved significantly higher learning gains than a control group receiving traditional instruction (t(58) = 2.646, p = 0.011, Cohen’s d = 0.68). Crucially, a Johnson-Neyman analysis revealed that the coach was particularly beneficial for students with lower prior knowledge (pre-test scores < 39.39), effectively narrowing the achievement gap. Furthermore, Lag Sequential Analysis of the interaction logs confirmed that the student-AI dialogue successfully adhered to the intended 5E pedagogical sequence (e.g., Engage → Explore transition, z = 11.157). This study demonstrates that the proposed framework is a viable method for creating effective, scalable AI-driven learning environments. Beyond chemistry, this approach is readily adaptable to other STEM disciplines requiring guided inquiry, such as physics and mathematics. By validating a low-code, pedagogy-first methodology, this work offers a scalable blueprint for instructional designers to bridge the gap between generative AI capabilities and rigorous educational standards. Full article

Background: This in vitro study evaluated the effects of two different adhesive application approaches (total-etch and self-etch) and gallic acid (GA) pretreatment on the dentin microshear bond strength (μSBS) of a universal adhesive system. Bond strength was assessed both before thermal aging and following aging procedures simulating approximately 1 and 5 years of clinical service. Materials and Methods: One hundred twenty intact human incisors were allocated to experimental groups according to the adhesive strategy, presence or absence of gallic acid (GA) pretreatment, and thermocycling regimen (0, 10,000, or 50,000 cycles). A universal adhesive system (G-Premio BOND) in combination with a nanohybrid composite resin was applied in accordance with the manufacturers’ instructions. Microshear bond strength (µSBS) was determined using a universal testing device. The obtained data were analyzed by three-way ANOVA and subsequently compared using Tukey’s post hoc test at a significance level of 0.05. Results: In the total-etch approach, pretreatment with gallic acid (GA) resulted in significantly greater µSBS values than those observed in the corresponding untreated specimens under all aging conditions (no thermocycling: 18.53 ± 0.99 vs. 11.33 ± 0.81 MPa; 1-year: 19.86 ± 0.82 vs. 11.60 ± 0.58 MPa; 5-year: 19.04 ± 0.62 vs. 10.28 ± 0.83 MPa; p = 0.001). A comparable trend was noted for the self-etch strategy, where GA application significantly enhanced bond strength compared with the non-treated groups (no thermocycling: 21.70 ± 0.98 vs. 14.19 ± 1.17 MPa; 1-year: 22.60 ± 0.50 vs. 14.94 ± 0.85 MPa; 5-year: 22.32 ± 0.59 vs. 12.94 ± 0.84 MPa; p = 0.001). Across all thermocycling conditions, the self-etch mode consistently produced higher bond strength values than the total-etch mode. Thermal aging did not significantly influence µSBS in the GA-treated groups. In contrast, in the absence of GA pretreatment, thermocycling led to a reduction in bond strength, particularly after the 5-year aging protocol. Conclusions: Gallic acid pretreatment significantly improved dentin bond strength and contributed to the preservation of bond durability after thermal aging. The highest µSBS values were obtained when the self-etch approach was combined with gallic acid (GA) pretreatment, suggesting that GA may serve as a beneficial adjunct for improving the durability and long-term performance of resin–dentin bonds. Full article

Vocational education systems face increasing pressure to deliver high-quality skills training while ensuring resource efficiency, safety, and scalability. In machining programs, traditional hands-on training relies heavily on physical equipment, consumables, and close supervision, posing challenges for sustainable management. This study employs a quasi-experimental design with pretest–posttest measures and a comparison group to examine the effects of VR-based pre-training with 50 first-year vocational students. The findings indicate that VR-based preparation supports learners’ cognitive and experiential readiness and contributes to perceived preparedness for subsequent hands-on activities. No statistically significant differences in posttest performance were observed between groups. VR-based preparatory training supports risk mitigation in learning contexts by enabling cognitive rehearsal and structured procedural familiarization before physical practice. At the system level, VR-based pre-training transforms early-stage trial-and-error learning into a guided virtual environment that incorporates predefined operational sequences, procedural cues, and embedded safety prompts. This approach helps reduce safety risks for inexperienced learners and supports the more strategic use of instructional resources. Rather than establishing generalized or causal effects, the findings provide exploratory, empirically grounded insights derived from a single institutional context, offering a structured reference framework to inform the design, scaling, and validation of future multi-site or longitudinal research in vocational education management. Furthermore, the study explicitly aligns with Sustainable Development Goals 4 (Quality Education) and 8 (Decent Work and Economic Growth). This alignment underscores the study’s relevance to sustainability-focused vocational training initiatives. Full article

Augmented reality (AR) is increasingly adopted in industrial environments as a tool for improving employee training and supporting complex assembly operations. The purpose of this study was to investigate the design, implementation, and strategic potential of AR-based work instructions using Microsoft HoloLens 2 in a real manufacturing environment. The study proposes and applies an integrated evaluation framework combining direct observation, performance evaluation, semi-structured interviews, quantitative SWOT analysis, PDCA-based process assessment, and economic cost analysis to assess AR-based training in a real manufacturing environment. AR training was implemented through Microsoft Dynamics 365 Guides for a standardized assembly procedure and evaluated with respect to training efficiency, user interaction, and feasibility of deployment. The results indicate improved task guidance consistency and descriptive performance indicators, suggesting enhanced training support under real production conditions. The SWOT analysis identified a favorable SO strategic position, highlighting strong internal capabilities and promising external opportunities for further deployment. The cost analysis shows that AR-based training becomes economically advantageous when applied to a larger number of trainees, despite high initial investment costs. Overall, the study demonstrates that AR-based training, when evaluated through a structured strategic and economic framework, represents a promising and strategically advantageous approach for industrial education, provided that ergonomic challenges, user adaptation, and financial constraints are systematically addressed. Full article