Search Results (367)

This article argues that learning a foreign language cannot be understood solely as the acquisition of internal grammatical or lexical rules, but rather as a form of action situated and corporeally embodied in a social, material, and cultural environment from which new linguistic skills emerge. Hence, we propose to describe foreign language learning under an ecological–enactive approach to cognition, that is, a coordination of two simultaneous multilevel processes: (i) at the subpersonal level, as the coordination of sensorimotor loops that adjust phonation, prosody, and auditory discrimination, and (ii) at the personal level, as the organism–environment coupling led by sociomaterial affordances that guide linguistic exploration. We conclude that active and immersive methodologies are more effective because they synchronize sensorimotor plasticity with the detection of affordances, enabling linguistic competence to emerge as a progressive self-organization of the agent–world system. Full article

Background: Embodied artificial intelligence (EAI), integrating advanced AI algorithms with robotic platforms capable of sensing, planning, and acting, has emerged as a transformative approach in healthcare delivery. This systematic review synthesizes evidence on robotic perception, decision-making, and clinical impact of EAI systems in healthcare settings. Methods: Following PRISMA 2020 guidelines, we searched PubMed/MEDLINE, Scopus, Web of Science, IEEE Xplore, and ACM Digital Library for studies published between January 2020 and August 2025. Seventeen studies met eligibility criteria, spanning four domains: surgical assistance, rehabilitation, hospital logistics, and telepresence. The protocol was prospectively registered in PROSPERO under ID: CRD420261285936. Results: Perception architectures predominantly employed multimodal sensor fusion, combining vision with force/torque, depth, and physiological signals. Decision-making approaches included imitation learning, reinforcement learning, and hybrid symbolic-neural control. Key findings indicate that surgical robots demonstrated consistency advantages in specific experimental tasks, rehabilitation robotics produced statistically significant improvements (SMD = 0.29) across 396 randomized controlled trials, and both logistics and telepresence systems achieved very high operational success levels. Nonetheless, important barriers remain, including limited external validation, small sample sizes, and insufficient cost-effectiveness data. Conclusions: Future research should prioritize standardized benchmarks, prospective multicenter trials, and patient-centered outcome measures to facilitate clinical translation of EAI technologies. Full article

Integrating engineering ethics education into the curriculum system of China’s transportation engineering major is crucial for promoting Sustainable Development Goal 11 (SDG 11) in the field of transportation engineering in China. However, the mapping relationship between teaching satisfaction and engineering ethics literacy, which are traditional indicators for evaluating teaching effectiveness in China, is not clear. This study constructed a teaching satisfaction transformation model with Experiential Transformer Engagement (ETE) and Self-Efficacy (SE) as dual mediators. Through experimental testing of undergraduate students majoring in transportation engineering from five universities in Hebei Province, it was found that both ETE and SE significantly moderated the conversion of teaching satisfaction to two key ethical abilities: Ethical Decision-Making Competence (EDMC) and Social Responsibility Strength (SRS). Among them, ETE accounted for about 64% of the total indirect impact on the two outcomes, which was significantly stronger than SE’s 48%. In addition, achievement goal orientation has a significant moderating effect. These findings not only address the teaching framework of engineering ethics that is suitable for the Chinese context, but also expand the theoretical basis and implementation plans of teaching models in the early stages of engineering ethics education in developing countries. Full article

Embodied learning involves the use of the physical embodiment of hands-on experiences, including gestures, body language, and gaze, during the instructional process for facilitation of the learning outcomes of robotics technology. Understanding the embodiment process is however challenging. In this research, large language model-based video understanding was used for the study of the effectiveness of embodied learning of robotics technology. Embodied and conventional videos were randomly selected, and the user comments were correlated with the transcript and summary of the videos. Results showed that there were higher numbers of user comments correlated with video content for the embodied learning-centered robotics instructional videos than the conventional learning-centered approach in terms of user sentiment and logical reasoning. The sentiment analysis of the video comments showed that the use of embodied learning was effective in achieving engagement in learning robotics, yielding fewer numbers of negative comments in comparison to the conventional learning videos. The embodied learning-centered videos were also helpful to enhance the logical reasoning of students. This user study shows that embodied learning is effective in engaging students, granting more positive sentiments toward the videos. Similarly, the logical reasoning of the students was also enhanced through the use of embodied learning for learning robotics technology. Full article

Extended Reality (XR) has reshaped how learning can be structured, yet its integration into formal curricula continues to lag behind its technological potential. Established instructional design models such as ADDIE and ASSURE provide stable planning structures, but were not developed to address the spatial, embodied and interactive characteristics of immersive environments. The XR2Learn framework was developed to bridge this gap by combining structured instructional planning with XR-specific pedagogical considerations. This study presents a multi-round Delphi-based expert evaluation of XR2Learn, involving twenty specialists in instructional design and XR-enhanced education. Experts assessed the framework across four dimensions: validity, clarity, usability and suitability. Qualitative feedback was thematically analyzed and subsequently quantified to establish consensus. The findings show strong agreement regarding the framework’s pedagogical grounding, logical structure and alignment with established instructional design practices. At the same time, experts identified limitations related to practical enactment, accessibility and the explicit integration of XR-specific learning constructs such as presence and social interaction. Overall, the results position XR2Learn as a framework at a transitional stage, moving from conceptual formulation toward practical instructional use. The study provides the first systematic expert validation of XR2Learn and outlines targeted directions for its refinement as a robust instructional design framework for XR-based education. Full article

With the rapid evolution of Digital Twins and Embodied AI, achieving fast, dense, and high-precision 3D perception in unknown environments has become paramount. However, existing Visual SLAM paradigms face a critical dilemma: geometry-based methods often fail in texture-less areas due to feature scarcity, while learning-based approaches frequently suffer from scale drift and unphysical deformations. To bridge this gap, we propose VGGT-Geo, a novel SLAM system that synergizes generative priors from Large Foundation Models with multi-modal geometric optimization. Distinguishing itself from simple cascaded architectures, we construct a Probabilistic Geometric Fusion framework, consisting of (1) Generative Warm-start, leveraging the holistic scene understanding capabilities of the VGGT, (2) Confidence-Aware Optimization to extract dense features via DINOv3 and predict their confidence map, and (3) a Multi-Modal Constraint Closure that fuses point-line features and metric depth priors to constrain rotational Degrees of Freedom in Manhattan Worlds. We conducted systematic evaluations on TUM, Replica, Tanks and Temples, and a challenging self-collected dataset featuring extreme lighting and texture-less walls. Experimental results demonstrate that VGGT-Geo exhibits superior robustness and accuracy in unseen environments. On our most challenging dataset, it achieves an Absolute Trajectory Error of 4–5 cm and a Relative Rotation Error of 0.79°, outperforming current state-of-the-art methods by approximately 50% in trajectory accuracy. This study validates that synergizing the intuition of Large Foundation Models with geometric rigor is a viable path toward next-generation robust SLAM. Full article

Industrial heritage embodies the complex interplay between historical continuity, technological development, and social spatial transformation. However, existing assessment methods often rely on qualitative judgments or fragmented criteria, limiting their ability to systematically evaluate the reuse potential in the context of heterogeneous heritage. To overcome this limitation, this study constructs an empirical evaluation framework that defines heritage value through quantifiable indicators and examines how different value dimensions affect reuse potential. Based on a dataset of 124 industrial heritage sites located on saline–alkali soil along the coast of Jiangsu Province, this study integrates multiple data sources such as archival records, field surveys, spatial data, and questionnaire surveys to construct a multidimensional indicator system. This system quantifies and analyzes four value dimensions: historical, architectural, technological, and socio-cultural, and employs machine learning methods for analysis. The study utilizes a Random Forest model to examine the relative impact of each dimension and assess their comprehensive explanatory power in classifying the potential for heritage reuse. The performance of the model is evaluated through cross-validation, yielding robust results (accuracy = 0.833, macro F1 = 0.812). A five-fold cross-validation is conducted to train a Random Forest classifier. The model achieves an accuracy of 0.833, a macro F1 score of 0.812, and an AUC of 0.871, outperforming the baseline classifier and validating the reliability of the analytical framework. The research findings indicate that the impact of architectural integrity and technical characteristics on reuse potential significantly outweighs symbolic or perceptual attributes, unveiling structural biases present in traditional heritage assessment practices. This study transcends descriptive assessments by empirically examining the operational modes of different value dimensions within a unified analytical framework, offering empirical insights into the mechanisms influencing the reuse of industrial heritage. The proposed framework provides a reproducible and transparent approach to support heritage conservation and adaptive reuse strategies in industrial transformation areas. Full article

Background/Objectives: Adolescence is pivotal for establishing dietary habits; however, school-based nutritional education remains focused on information dissemination, with minimal effects on behavior modification. Evidence from neuroscience, education, and food literacy indicates that attention, engagement, sensory experiences, and social contexts are integral to effective learning in nutrition education. This article conceptualizes a framework for adolescent food education beyond knowledge transmission, aiming to cultivate taste competence using the Mediterranean Diet as a pedagogical ecosystem. Methods: This study employed a conceptual methodology, utilizing interdisciplinary literature from food literacy, sensory education, developmental neuroscience, educational theory, and public health nutrition. It synthesizes empirical findings and theoretical models to develop the Teaching Autonomous Sensory Taste in the Mediterranean Diet (TASTE–MED) framework. Results: This study introduces taste competence as a multifaceted educational outcome, encompassing sensory, relational, cultural, and reflective dimensions. The TASTE–MED framework outlines how experiential, multisensory, and socially embedded learning processes can be implemented in schools, facilitated by the Mediterranean Diet, which provides a sensory-rich and culturally significant context. The educational implications are discussed in terms of curriculum design, teacher training, family involvement and digital tools. Conclusions: The TASTE–MED framework redefines food literacy as an embodied and socially situated competence rather than a cognitive construct. This framework provides a theoretical foundation for informing the design, evaluation, and research of future interventions, advocating for the transition from information-based nutrition education to competence-oriented food education during adolescence. Full article

The relationship between sound and visual representation has long intrigued artists and educators, with historical explorations ranging from colour–music correspondence to alternative notations and graphic visualisations of music. Recent advances in digital technologies have significantly expanded the pedagogical potential of visual information in music education. However, there is still no comprehensive review mapping how visual information is applied in digital music education tools. This scoping review maps the application of visual modalities in original digital tools for music teaching and learning, drawing on 63 studies published between 2014 and 2024. Following Arksey and O’Malley’s five-stage framework and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) reporting guidelines, this review analyses the methodological characteristics, pedagogical foundations, and design features of these tools. Findings reveal a dominant focus on performance skills and individual learning, often supported by visual feedback and interactivity. However, other aspects of learning such as creativity, responsiveness, and collaboration remain underexplored. While references to concepts such as multimodality and embodied learning are common, a robust theoretical grounding is frequently lacking or implicit. This review calls for a shift from technology-driven innovation toward pedagogy-led design, advocating for a more holistic educational approach and more rigorous empirical research. Implications highlight the potential of visual information not only to support performance skill acquisition but also to foster creative, expressive, and collaborative dimensions of music learning. Full article

This study presents Robo-ICARE (Robotics-Inclusive Child-Centered Approach for Robotics in Education), a Child–Robot Interaction (cHRI) framework for designing inclusive educational robotics in under-resourced and multigrade classroom contexts. The framework addresses gaps in existing educational robotics approaches by integrating user-centered design, affective expressiveness, pedagogical alignment and inclusivity, with particular attention to learner variability and contextual constraints. Robo-ICARE comprises six interrelated design dimensions—embodiment, interactivity, intelligence, pedagogical alignment, inclusivity, and user-centered process—intended to guide the design implementation, and evaluation of educational robots in authentic classroom environments. The framework is operationalized and empirically examined through a case study involving COODI, a low-cost and emotionally expressive educational robot co-designed with students and teachers in rural Chile. The study follows a primarily qualitative, exploratory design-based research approach, supported by descriptive quantitative measures. Empirical evaluation with 49 participants focuses on usability, user experience, engagement, and emotional acceptance, rather than on causal measurement of learning gains. The findings indicate that the proposed framework supports the design of emotionally engaging and context-sensitive educational robotics experiences that are feasible in low-resource settings. Overall, this work contributes a design-guiding, empirically grounded framework that bridges child–robot interaction theory and educational practice, offering a replicable approach for inclusive educational robotics in under-resourced classroom contexts. Full article

This paper presents a physics-consistent hybrid surrogate framework for simulating the mechanical behavior of reinforced concrete beams that utilize waste fired clay (WFC) as a partial substitute for cement. The main contribution is the integration of empirically observed deformation behavior with physics-informed learning to produce an interpretable, mechanically valid surrogate model. Full-field surface deformation fields were measured using Digital Image Correlation (DIC) under monotonic loading and processed through a convolutional neural network (CNN) to extract deformation- and crack-sensitive features. These features were integrated with experimentally measured stress–strain data within a Physics-Informed Neural Network (PINN) in which equilibrium and conditional constitutive monotonicity constraints were enforced through the loss function. A Non-Dominated Sorting Genetic Algorithm II (NSGA-II) was utilized as a downstream parametric exploration tool to examine trade-offs among maximum load capacity, material cost, and embodied CO₂ inside a constrained mixture-design space. Model interpretability was assessed by SHapley Additive exPlanations (SHAP), indicating that deformation-driven kinematic factors predominantly influence stress prediction, whereas WFC content and reinforcement parameters have a secondary, mixture-level impact. The resulting framework achieves enhanced predictive accuracy (R² = 0.969) relative to its individual components and operates as an offline, physics-calibrated surrogate rather than a real-time digital twin, providing a reliable and interpretable basis for structural assessment and sustainability-oriented design evaluation of WFC-modified reinforced concrete beams. Full article

As intelligent vehicle technologies evolve, reliable driver monitoring systems have become increasingly critical for ensuring the safety of human drivers and operational reliability. This paper proposes a novel visual computing framework for Driver Fatigue Monitoring Systems (DFMSs) based on hierarchical decoupling and scenario element analysis, specifically designed for intelligent transportation environments. By treating the monitoring system as an engineering-level embodied perception–decision system deployed within the vehicle, rather than a purely disembodied vision module, the framework decouples low-level algorithmic perception from application-layer decision logic, enabling a more granular evaluation of visual computing performance in real-world scenarios. We leverage Python 3.9-driven automated test case generation to simulate diverse environmental variables, improving testing efficiency by 50% over traditional manual methods. The system utilizes deep learning-based visual computing to achieve high-fidelity monitoring of eye closure (PERCLOS, EAR), yawning (MAR), and head pose dynamics, enabling real-time assessment of the driver’s state within the embodied system loop. Comparative benchmarking reveals that our framework significantly outperforms existing models in visual understanding accuracy, achieving perfect confidence scores (1.000) for eye closure and smoking behavior detection, while drastically reducing false positives in mobile phone usage detection (misidentification rate: 0.016 vs. 0.805). These results demonstrate that an embodied approach to visual perception enhances the robustness and reliability of driver monitoring systems deployed in real vehicles, providing a scalable pathway for the development of next-generation intelligent transportation safety standards. Full article

Accurate early-stage assessment of building energy and carbon performance is essential for informed sustainable design yet remains challenging due to limited design detail and simulation effort. This study presents a Building Information Modeling–Machine Learning (BIM-ML) framework for predicting office building energy and carbon performance at early design stages using simulation-based datasets. A reduced-factorial Design of Experiments (DOE) generated 210 parametric office building models for Orlando, Florida (ASHRAE Climate Zone 2A), complemented by additional climate scenarios. Systematic variations in geometry, envelope, building systems, and operational schedules produced a dataset with 14 independent variables and five performance indicators: Energy Use Intensity, Operational Energy, Operational Carbon, Embodied Carbon, and Total Carbon. Four regression methods—Linear Regression, Model Tree (M5P), Sequential Minimal Optimization Regression, and Random Forest—were trained and evaluated using 10-fold cross-validation. Random Forest showed the strongest overall predictive performance. Feature-importance analysis identified HVAC system type, Window-to-Wall Ratio, and operational schedule as the most influential parameters, while geometric factors had lower impact. Cross-climate analysis and validation with measured data from two university office buildings indicate that the framework is adaptable and generalizable, supporting reliable early-stage evaluation of energy and carbon performance. Full article

Museums are cultural spaces that should promote accessibility and inclusion for all. However, accessibility is often interpreted as removing physical barriers, overlooking less visible obstacles—such as cognitive, sensory, and communicative challenges—that can profoundly shape the museum experience for people with intellectual disabilities. This paper presents an ethnographic case study conducted in the Veneto region of Italy, in collaboration with a group of individuals with Down Syndrome (DS), aiming to explore their lived experiences of a museum visit. Drawing on participant observation and in-depth interviews, the study examines how visitors with DS engage with the museum environment on behavioural and sensory levels. Findings reveal the impact of environmental stimuli, difficulties in navigating abstract or densely layered visual content, and the importance of embodied interaction with objects and spatial cues. Positive experiences emerged from relational engagement, guided facilitation, and the use of multi-sensory supports. The study underscores the need for museums to move beyond compensatory or charity-based models of accessibility, and instead adopt inclusive design principles that value neurodiversity and participatory co-creation. In doing so, this research contributes to the emerging discourse on how museums can become safe spaces for learning, dialogue, and self-expression for people with intellectual disabilities. Full article

To address the challenges of characterizing subway passenger flow fluctuations and overcoming the slow convergence and significant errors of existing intelligent optimization algorithms in tuning deep learning parameters for flow prediction, this study proposes a novel subway passenger flow fluctuation interval prediction model based on a Symmetry-Enhanced FIG-ICPO-XGBoost model. The core innovation is an Improved Cheetah Optimization Algorithm (ICPO), which incorporates enhancements including Circle mapping for population initialization, a hybrid strategy of dimension-by-dimension pinhole imaging opposition-based learning and Cauchy mutation to escape local optima, and adaptive variable spiral search with inertia weight to balance exploration and exploitation. The construction of this methodology embodies the concept of symmetry in algorithm design. For instance, Circle mapping achieves uniformity and ergodicity in the initial distribution of the population within the solution space, reflecting the symmetric principle of spatial coverage. Dimension-by-dimension pinhole imaging opposition-based learning generates opposite solutions through the principle of mirror symmetry, effectively expanding the search space. The adaptive variable spiral search strategy dynamically adjusts the spiral shape, simulating the symmetric relationship of dynamic balance between exploration and exploitation. Utilizing fuzzy-granulated passenger flow data (LOW, R, UP) from Harbin, the ICPO was employed to optimize XGBoost hyperparameters. Experimental results demonstrate the superior performance of the ICPO on 12 benchmark functions. The ICPO-XGBoost model achieves mean MAE, RMSE, and MAPE values of 10,291, 10,612, and 5.8%, respectively, for the predictions of the LOW, R, and UP datasets. Compared to existing models such as CPO-XGBoost, PSO-BiLSTM, GA-BP, and CNN-LSTM, these values represent improvements ranging from 4541 to 13,161 for MAE, 5258 to 14,613 for RMSE, and 2.6% to 7.2% for MAPE. The proposed model provides a reliable theoretical and data-driven foundation for optimizing subway train schedules and station passenger flow management. Full article