Search Results (915)

Virtual reality offers unique opportunities to personalize learning by adapting instructions to individual learning styles. This study explores the relationships between learning styles, cognitive load, and learning outcomes in a virtual reality environment designed for engineering education. Drawing on Kolb’s experiential learning theory, the research investigates how immersion and flow, in relation to learning styles, influence learning outcomes within the Submarine Simulator, an educational tool for underwater engineering. To enhance instructional design in virtual reality, this study proposes to aggregate existing and validated models, such as Kolb’s framework, to develop new models tailored specifically for learning environments in virtual reality. This research aims to highlight the interplay of these variables in a learning process focused on acquiring knowledge in the Science, Technology, Engineering, and Mathematics fields, specifically hydrodynamics, through designing and operating a simulated submarine model in virtual reality. A cohort of 26 students from MINES Paris—PSL participated in a three-phase testing process to evaluate the effectiveness of original virtual reality software designed to support learning in underwater engineering. The findings enhance our understanding of how learning styles influence learner engagement and performance and how virtual reality environments can be optimized through adaptive instructional design guided by these novel models tailored specifically for such immersive settings. Full article

Background/Objectives: No Arabic-language version of the Observable Social Cognition Rating Scale (OSCARS) is available that allows to properly and specifically assess social cognition (SC) in Arabic-speaking populations. This study aimed to examine the preliminary psychometric characteristics of the Arabic translated version of the OSCARS, including factor structure, reliability, concurrent validity, and measurement invariance across sex. Methods: This cross-sectional study has been conducted during February and March 2024 and included 113 chronic, remitted, and clinically stable patients with schizophrenia. Results: The originally proposed two-factor model (Social Cognitive Bias and Social Cognitive Ability) showed acceptable model fit after removal of two items that yielded low factor loadings (items 2 and 3). Total and factor scores showed good internal consistency, with Cronbach’s alpha of 0.85–0.94. Measurement invariance was established across sex groups at the configural, metric, and scalar levels. No significant differences emerged between male and female patients for latent mean scores of the OSCARS. Finally, concurrent validity was supported by appropriate patterns of correlations with functioning, recovery, and emotional intelligence measures. Conclusions: The Arabic OSCARS stands out as a brief, valid, reliable, and comprehensive assessment tool to evaluate SC in Arabic-speaking patients with schizophrenia based on the perspectives of interviewers. Offering this measure to clinicians and researchers who work in Arab settings may close the existing gap in the assessment of SC in schizophrenia. Due to its easy and fast application, the Arabic OSCARS is believed to be highly valuable in clinical and research practices. Full article

AI-powered full-site web generation tools promise to democratize website creation for novice users. However, their actual usability and accessibility for novice users remain insufficiently studied. This study examines interaction barriers faced by novice users when using Wix ADI to complete three tasks: Task 1 (onboarding), Task 2 (template customization), and Task 3 (product page creation). Twelve participants with no web design background were recruited to perform these tasks while their behavior was recorded via screen capture and eye-tracking (Tobii Glasses 2), supplemented by post-task interviews. Task completion rates declined significantly in Task 2 (66.67%) and 3 (33.33%). Help-seeking behaviors increased significantly, particularly during template customization and product page creation. Eye-tracking data indicated elevated cognitive load in later tasks, with fixation count and saccade count peaking in Task 2 and pupil diameter peaking in Task 3. Qualitative feedback identified core challenges such as interface ambiguity, limited transparency in AI control, and disrupted task logic. These findings reveal a gap between AI tool affordances and novice user needs, underscoring the importance of interface clarity, editable transparency, and adaptive guidance. As full-site generators increasingly target general users, lowering barriers for novice audiences is essential for equitable access to web creation. Full article

Location-based games offer innovative approaches for health promotion among older adults, but their effectiveness depends on understanding complex contextual factors beyond technological design. In our study, we aimed to adapt a location-based game in the form of a smartphone application which originally targeted younger people. We employed ethnographic observations in a field test under real-world conditions for identifying the needs and preferences of older adults in this regard. Field notes of one co-creative workshop were analyzed using thematic analysis. Four key contextual factor categories emerged that significantly influenced user engagement: (1) temporal/spatial factors including weather conditions, topography, and traffic safety that impacted screen visibility and cognitive function; (2) virtual-physical orientation challenges requiring high cognitive load to transfer abstract digital maps to real environments; (3) individual factors such as technical competence, mobility levels, and prior accessibility experiences that shaped usage patterns; and (4) social dynamics that provided motivation and peer support while potentially creating exclusionary practices. Successful digital health innovations for older adults require a socio-technical systems approach that addresses environmental conditions, reduces cognitive transfer demands between virtual and physical navigation, leverages social elements while preventing exclusion, and accounts for heterogeneity among older adults as contextually interactive factors rather than merely individual differences. Full article

This study examines the influence of artificial intelligence (AI) system transparency, cognitive load, response bias, and individual values on perceived AI decision integrity. Using a quantitative approach, data were collected through surveys and analyzed via SEM-PLS. The findings highlight that AI transparency and familiarity significantly impact users’ trust and perception of decision fairness. Response biases were found to be increased by the cognitive load and decision fatigue, affecting decision integrity. This study identifies mediating effects of sensitivity to errors and response bias in AI-driven decision-making. Practical implications imply that lowering the cognitive load and increasing transparency will help to increase the acceptance of AI, and incorporating ethical considerations into AI system design helps to minimize bias. This study contributes to AI ethics by emphasizing fairness, explainability, and user-centered trust mechanisms. Future research should explore AI decision-making across industries and cultural contexts. The findings of this study offer managerial, theoretical, and practical insights into responsible AI deployment. Full article

Background: In healthcare education, understanding and managing cognitive load is crucial for enhancing learning outcomes for students, healthcare professionals, patients, and the general public. Despite numerous studies developing cognitive load classification models, there is a lack of comprehensive guidelines on how to effectively utilize these models in healthcare education. This study reviews cognitive load classification models using physiological data to provide insights and guidelines for their development in healthcare contexts. Methods: A scoping review was conducted on studies published between 2015 and 2024, identified through databases including Scopus, Web of Science, PubMed, EMBASE, and PsycINFO. The search terms included “cognitive load,” “physiology,” “data,” and “classification.” Inclusion criteria were peer-reviewed journal articles in English, focused on the healthcare context, utilizing experimental physiological data, and developing classification models. After screening 351 articles, ten studies met the inclusion criteria and were analyzed in detail. Results: Task design predominantly focused on measuring intrinsic cognitive load by adjusting task difficulty. Data collection mainly utilized EEG (electroencephalogram) and body movement data. SVM (support vector machine) algorithms were the most frequently used for model development, with cross-validation and feature selection employed to prevent overfitting. This study derived the importance of clearly defining cognitive load types, designing appropriate tasks, establishing reliable ground truths with multiple indicators, and selecting contextually relevant data. Conclusions: This study provides a comprehensive analysis of cognitive load classification models using physiological data in healthcare education, offering valuable guidelines for their development. Despite the study’s limitations, including a small number of analyzed papers and limited diversity in educational contexts, it offers critical insights for using and developing cognitive load classification in healthcare education. Future research should explore the applicability of these models across diverse educational settings and populations, aiming to enhance the effectiveness of healthcare education and ultimately improve learning and healthcare outcomes. Full article

Equine-assisted services (EAS) are used for civilian and military trauma survivors to reduce depression and posttraumatic stress symptoms. While early scientific evidence supports the benefits of EAS, the neurophysiological mechanisms underlying these benefits are unknown. The specific aims of this exploratory study were to determine (1) whether functional near-infrared spectroscopy (fNIRS) neuroimaging can be used to explore neural responses of EAS veteran participants and (2) the correlation between neural responses and psychological outcomes of the participants interacting with equines. Fifteen veterans participated in a 2-day EAS program consisting of four randomized activities. An fNIRS sensor cap was used to measure the oxygenated (O₂Hb), deoxygenated (hHb), and total hemoglobin (tHb) of the participants during each activity. The results indicated no significant differences for O₂Hb and tHb across the visits or activities, however, a significant difference in hHb was observed. There was an increase in hHb during the activities that included an equine, which indicated a greater cognitive load and attention. Further, data from pre-/post-psychometric assessments showed a significant improvement in participants’ trait anxiety, psychological flexibility, and positive and negative affect after interacting with the horse. Preliminary data revealed a potential association between the cognitive attention and psychological health of participants during an EAS session. Full article

The tunnel construction environment is complex, and workers’ cognitive load directly affects safety and efficiency, making a dynamic assessment urgently needed. This study aims to explore the cognitive load of tunnel construction workers under different working environments using EEG technology. In the experimental design, subjects adapted to the virtual reality (VR) environment and received instructions before wearing a wireless EEG system and VR equipment to begin the formal experiment. Each subject underwent four rounds of experiments, corresponding to four different scenarios: control, night shift, noise, and confined space. Each round included three tasks of low, medium, and high difficulty. Analysis of EEG data showed that tunnel construction tasks in different environments significantly affected cognitive load, especially during night shifts and in confined spaces, with cognitive load increasing significantly with task difficulty. The results provide a theoretical basis for optimizing the management of tunnel construction environments and task design. Full article

Background: Effective doctor–patient communication is essential for high-quality care, especially for patients with chronic conditions requiring hemodialysis. However, there is a lack of validated tools in the Malay language to measure this communication. This study aimed to translate and validate the Doctor–Patient Communication Questionnaire (DPCQ) into Malay (MyD-PCQ) for use among patients receiving hemodialysis in Kelantan, Malaysia. Methods: A cross-sectional study was conducted with 300 patients receiving hemodialysis at Hospital Universiti Sains Malaysia. The original English DPCQ was translated and culturally adapted into Malay following international guidelines, including forward and backward translation, expert review, and cognitive debriefing. Data were collected using the Malay version of the questionnaire. Confirmatory factor analysis (CFA) assessed the construct validity, while Raykov’s rho measured internal consistency. Results: The Malay version of the DPCQ demonstrated excellent model fit in CFA (χ²/df = 1.25, p = 0.053; SRMR = 0.037; RMSEA = 0.029; CFI = 0.982; and TLI = 0.979). Factor loadings ranged from 0.493 to 0.640. The internal consistency was high, with Raykov’s rho of 0.887. The average total score among participants was 37.31 out of 60, indicating moderate perceived communication quality. Conclusions: The Malay Doctor–Patient Communication Questionnaire (MyD-PCQ) is a valid and reliable tool for assessing communication between doctors and patients receiving hemodialysis in Malaysia. Its use can help identify communication gaps, support training initiatives, and improve patient-centered care in clinical practice. Future research should evaluate its use in other settings and patient populations. Full article

Industry 4.0 has intensified the skills gap in industrial automation education, with graduates requiring extended on boarding periods and supplementary training investments averaging USD 11,500 per engineer. This paper introduces VisFactory, a multimedia learning system that extends the cognitive theory of multimedia learning by incorporating haptic feedback as a third processing channel alongside visual and auditory modalities. The system integrates a digital twin architecture with ultra-low latency synchronization (12.3 ms) across all sensory channels, a dynamic feedback orchestration algorithm that distributes information optimally across modalities, and a tripartite student model that continuously calibrates instruction parameters. We evaluated the system through a controlled experiment with 127 engineering students randomly assigned to experimental and control groups, with assessments conducted immediately and at three-month and six-month intervals. VisFactory significantly enhanced learning outcomes across multiple dimensions: 37% reduction in time to mastery (t(125) = 11.83, p < 0.001, d = 2.11), skill acquisition increased from 28% to 85% (${\eta }_p^2=0.54$), and 28% higher knowledge retention after six months. The multimodal approach demonstrated differential effectiveness across learning tasks, with haptic feedback providing the most significant benefit for procedural skills (52% error reduction) and visual–auditory integration proving most effective for conceptual understanding (49% improvement). The adaptive modality orchestration reduced cognitive load by 43% compared to unimodal interfaces. This research advances multimedia learning theory by validating tri-modal integration effectiveness and establishing quantitative benchmarks for sensory channel synchronization. The findings provide a theoretical framework and implementation guidelines for optimizing multimedia learning environments for complex skill development in technical domains. Full article

While most of the Alzheimer’s disease (AD) cases are sporadic and manifest after age 65 (late-onset AD, LOAD), a subset of patients develop symptoms earlier in life (early-onset, EOAD) due to mutations in the PSEN1, PSEN2, or APP genes with an autosomal-dominant inheritance pattern (AD-EOAD). In this study, we examined the association between age of onset (AoO) and first clinical manifestation (FCM) with the APOE and DAOA genotypes, previously described as modifiers of clinical phenotypes in LOAD and EOAD in 88 individuals clinically diagnosed with AD-EOAD due to the PSEN1 A431E variant (39 females, 49 males). We classified the population according to their genotype (APOEε2, APOEε3, and APOEε4 and DAOA G/G, G/A, and A/A) and FCM (cognitive, behavioral, motor, and memory impaired). Memory impairment was the most frequent symptom (51%), followed by motor disturbances (31.8%), cognitive symptoms other than memory (10.4%), and behavioral changes (6.8%). We found a significant association between APOE genotype and AoO (p < 0.001), with the APOEε4 allele being linked to a delayed onset (β = 4.04, SE = 1.11, p = 0.0003). Similarly, individuals with the DAOA rs2391191 A/A genotype showed a significantly later AoO compared to G/G carriers (β = 2.13, SE = 0.96, p = 0.0301). No significant association was found between APOE or DAOA genotypes and FCM. The findings suggest that both the APOEε4 allele and DAOA rs2391191 A/A genotype may act as genetic modifiers of AoO, delaying symptom onset in individuals with AD-EOAD. Further research is needed to elucidate the molecular pathways through which APOE and DAOA influence AD-EOAD progression. Full article

While electroencephalography is extremely useful for studying brain activity, EEG data is always contaminated by a wide range of artefacts. Many techniques exist to identify and remove such artefacts, primarily offline, with and without human supervision and intervention. This research presents a novel, fully automated online wavelet-based learning adaptive denoiser for artefact identification and mitigation in EEG signals. It contributes to knowledge by offering a framework that can be instantiated with artefact-specific and context-dependent parameters. In detail, this framework is instantiated for block online muscle artefact identification and mitigation. It is based on the discrete wavelet transformation (DWT) for time–frequency enrichment and the Isolation Forest algorithm for linearly learning data characteristics and identifying anomalous activity in a sliding moving buffer. It is built upon a denoising strategy that operates in the domain of DWT coefficients before reverting characteristics to the time domain. The findings demonstrate that such instantiation is promising in its goal of successfully identifying myogenic muscle movements and transforming them into cleaner EEG signals. They also emphasise the difficulties in tackling the known problem of the cone of influence associated with wavelet transformation and the tradeoff between the length of consecutive EEG windows and the problem’s real-time nature. Full article

Undergraduate design education faces a structural contradiction characterized by high cognitive load (CL) and relatively low innovation output. Meanwhile, existing generative AI tools predominantly emphasize the generation of visual outcomes, often overlooking the logical guidance mechanisms inherent in design thinking. This study proposes a Dual-Path teaching model integrating critical reconstruction behaviors to examine how AI enhances design thinking. It adopts structured interactions with the DeepSeek large language model, CL theory, and Structural Equation Modeling for analysis. Quantitative results indicate that AI-assisted paths significantly enhance design quality (72.43 vs. 65.60 in traditional paths). This improvement is attributed to a “direct effect + multiple mediators” model: specifically, AI reduced the mediating role of Extraneous Cognitive Load from 0.907 to 0.017, while simultaneously enhancing its investment in Germane Cognitive Load to support deep, innovative thinking. Theoretically, this study is among the first to integrate AI-driven critical reconstruction behaviors (e.g., iteration count, cross-domain terms) into CL theory, validating the “logical chain externalization → load optimization” mechanism in design education contexts. Practically, it provides actionable strategies for the digital transformation of design education, fostering interdisciplinary thinking and advancing a teaching paradigm where low-order cognition is outsourced to reinforce high-order creative thinking. Full article

Artificial intelligence (AI) soft skills have become increasingly vital in today’s technology-driven world, as they support decision-making systems, strengthen collaboration among stakeholders, and enable individuals to adapt to rapidly changing environments—factors that are fundamental for achieving the sustainability goals of smart cities. Even though AI soft skills are becoming more important, no scale specifically designed to identify and evaluate individuals’ AI soft skills has been found in the existing literature. Therefore, this paper aimed to develop a reliable and valid scale to identify the AI soft skills of individuals. A sample of 685 individuals who were employed in AI-active sectors, with a minimum of a bachelor’s degree, and at least one year of AI-related work experience, participated in the study. A sequential exploratory mixed-methods research design was utilized. Exploratory factor analysis (EFA) identified a five-factor structure that accounted for 67.37% of the total variation, including persuasion, collaboration, adaptability, emotional intelligence, and creativity. Factor loadings ranged from 0.621 to 0.893, and communalities ranged from 0.587 to 0.875. Confirmatory factor analysis (CFA) supported this structure, with strong model fit indices (GFI = 0.940, AGFI = 0.947, NFI = 0.949, PNFI = 0.833, PGFI = 0.823, TLI = 0.972, IFI = 0.975, CFI = 0.975, RMSEA = 0.052, SRMR = 0.035). Internal consistency for each factor was high, with Cronbach’s alpha values of dimensions ranging from 0.804 to 0.875, with a value of 0.921 for the overall scale. Convergent and discriminant validity analyses further confirmed the construct’s robustness. The finalized AI soft skills (AISS) scale, consisting of 24 items, offers a psychometrically valid and reliable tool for assessing essential AI soft skills in professional contexts. Ultimately, this developed scale enables the determination of the social and cognitive skills needed in the human-centered and participatory governance structures of smart cities, supporting the achievement of specific Sustainable Development Goals such as SDG 4, SDG 8, and SDG 11, and contributes to the design of policies and training programs to eliminate the deficiencies of individuals in these areas. Thus, it becomes possible to create qualified human resources that support sustainable development in smart cities, and for these individuals to take an active part in the labor market. Full article

Next-generation pilot suits are evolving into intelligent, adaptive platforms that integrate advanced polymeric materials, smart textiles, and on-body artificial intelligence. High-performance polymers have advanced in mechanical strength, thermal regulation, and environmental resilience, with fabrication methods like electrospinning, weaving, and 3D/4D printing enabling structural versatility and sensor integration. In particular, functional nanomaterials and hierarchical nanostructures contribute critical properties such as conductivity, flexibility, and responsiveness, forming the foundation for miniaturized sensing and integrated electronics. The integration of flexible fiber-based electronics such as biosensors, strain sensors, and energy systems enables real-time monitoring of physiological and environmental conditions. Coupled with on-body AI for multimodal data processing, autonomous decision-making, and adaptive feedback, these systems enhance pilot safety while reducing cognitive load during flight. This review places a special focus on system-level integration, where polymers and nanomaterials serve as both structural and functional components in wearable technologies. By highlighting the role of nanostructured and functional materials within intelligent textiles, we underline a potential shift toward active human–machine interfaces in aerospace applications. Future trends and advancements in self-healing materials, neuromorphic computing, and dynamic textile systems will further elevate the capabilities of intelligent pilot suits. This review discusses interdisciplinary strategies for developing pilot wearables capable of responding to real-time physiological and operational needs. Full article