Search Results (2,799)

Artificial intelligence (AI) has reshaped contemporary approaches to teaching, assessment, and feedback. Most AI systems provide reactive feedback, offering instant answers that reduce learners’ cognitive engagement and sense of agency. In contrast, Mili was developed as a proactive pedagogical intelligence that asks guiding questions and encourages learners to construct their own responses. Through this design, feedback becomes a process of learning rather than an evaluative mechanism. Mili is a Hebrew-language educational chatbot grounded in principles of dialogic feedback, pedagogical mediation, and literacy resilience. Its goal is to create a metacognitive literacy dialogue in which questions replace answers and learning becomes an act of reflection and self-inquiry. The development followed a Design-Based Research approach involving iterative cycles of design, training, and testing. At each stage, pedagogical prompts were crafted to simulate authentic teacher–learner dialogue, including clarifying questions, pedagogical delay, and emotional reinforcement. This process enabled an exploration of how AI can mediate feedback that stimulates deeper cognitive engagement. The resulting model demonstrates proactive dialogic feedback in which AI does not simply respond but initiates reflective dialogue. Simulated interactions with Mili reveal how such feedback supports the three dimensions of literacy resilience: linguistic-cognitive, metacognitive, and emotional. Mili represents a conceptual shift in AI-based feedback, moving from response to process, from outcome to mediation, and from reactive AI to learning-generative AI. The study makes a theoretical contribution by articulating a model of pedagogically mediated AI and a practical contribution by developing a feedback tool that fosters inquiry, reflection, and literacy resilience in learners and teachers. Full article

Modern interaction with smart devices is hindered by the “Midas Touch” problem, where sensors frequently misinterpret incidental physical movements as intentional commands due to a lack of human context. This research addresses this conflict by introducing the Multimodal Haptic Informatics (MHI) index within a novel Scene–Action–Trigger (SAT) framework. The goal is to contextualize mechanical movements as human intent by integrating physical, spatial, and cognitive data locally at the edge. The methodology employs an “Action-as-primary indexing” mechanism where the Action channel (IMU) serves as a temporal anchor t, triggering high-resolution Scene (computer vision) and Trigger (audio) processing only during critical haptic events. Validated through a complex origami crane task generating 29,408 data frames, the framework utilizes a three-stage informatics derivation process: single-modal scoring, score weighting, and hand state mapping. Results demonstrate that applying an adaptive “Speedometer” logic successfully reclassifies the “Transitional State”. While this state constitutes over half of the behavioral dataset (54.76% on average), it is effectively disambiguated into meaningful intent using a self-trained local Large Language Model (LLM) for semantic verification. Furthermore, the event-driven sampling of 93 keyframes reduces the processing overhead by 99.68% compared to linear annotation. This study contributes a low-latency, privacy-preserving “Protocol of Assent” that maintains user agency by providing intelligent system suggestions based on confirmed haptic intensity. 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

Self-regulated learning (SRL) is a critical competency for learners in increasingly technology-enhanced educational environments, yet little is known about how SRL is fostered within video-based interventions in K-12 settings. While existing reviews and meta-analyses focus on the effectiveness of SRL interventions, this study aims to address current gaps by specifically examining the implementation processes, instructional tools, and the role of video. Addressing this, the present study conducted a systematic literature review of peer-reviewed K-12 intervention studies published since 2010, guided by PRISMA standards and other methodological frameworks in the field of SRL. 30 quantitative or mixed-methods studies focusing on K-12 SRL interventions were selected from Web of Science and ERIC, with the requirement that video served as an instructional component rather than a research tool. These studies were then systematically coded by eight researchers for SRL strategies, instructional methods, video roles, and pedagogical settings. Findings show that most video interventions targeted multiple SRL strategies across different phases of the SRL cycle, offering a comprehensive approach to fostering regulation. However, while cognitive and metacognitive strategies were frequently addressed, motivational and resource-management strategies were seldom included within explicit instruction, which focused primarily on cognitive and metacognitive training. Video played multiple pedagogical roles, including delivering disciplinary content, teaching SRL strategies, or combining both. A thematic analysis identified four pedagogical settings that characterized successful interventions: Teacher-guided, Active, Social, and Knowledge-based (TASK) learning. These settings appear to mitigate common challenges of video-based learning, such as cognitive load and learner passivity. The review contributes a novel synthesis of SRL-video integration and proposes TASK learning as a framework for designing SRL interventions. Full article

With the growth of large language models (LLMs), there are increasing calls to interpret their behavior through the prism of analogies to human cognitive mechanisms. At the same time, scientific literature points to the fundamental limitations of these systems, describing them, among other things, as models that generate a superficial simulation of reasoning without real access to semantic meanings (“stochastic parrots” or “illusion of reasoning”). This paper proposes an innovative, modular benchmark for assessing the cognitive competence of LLMs, integrating three complementary dimensions of language processing: factual, syntactic, and logical. Eight language models (LLama 3.2, Mistral 7B, LLama 3:8B, Gemini 2.5 Flash, ChatGPT-3, ChatGPT-4o mini, ChatGPT-4, and ChatGPT-5) were tested using a uniform procedure with context reset after each interaction and a three-point scoring scheme (0/0.5/1). The results obtained showed a clear advantage for the largest models in tasks based on general knowledge and formal transformations known from training, with a significant decrease in effectiveness, regardless of model size, in tasks requiring conjunctive reasoning based solely on new, local premises. Importantly, unstable but measurable corrective abilities of some models were also observed after feedback, suggesting the presence of reactive mechanisms, but were insufficient to consider them systems capable of cognitive self-reflection. The combined analysis indicates that LLMs effectively simulate syntax and logic rules when the task corresponds to recognizable formal patterns, but fail in situations requiring the construction of new, coherent chains of beliefs and symbolic inferences, which undermines the thesis of their cognitive “understanding”. The results justify the need to create more complex and semantically restrictive evaluation frameworks that will allow distinguishing statistical fit from systemic, multi-stage formal reasoning. The proposed benchmark is a step towards a more multidimensional and diagnostic evaluation of LLMs, shifting the focus from “will the model respond correctly?” to “why and under what conditions is the model able to reason?” Full article

Herding dogs play an important role in the management of herds of sheep and cattle, and their effectiveness depends on various genetic and environmental factors, and the choice of training method. The aim of this study is to explain how these factors determine the effectiveness of herding work, the level of stress and psychological comfort experienced by the dogs, as well as their physical health. The study also examines the choice of training method, the influence of socialisation and relationship with the handler, as well as the living conditions. Information on the welfare of herding dogs and the factors that influence this welfare were obtained based on a search of PubMed, Web of Science, Google Scholar and Scopus using defined keywords. Research indicates that positive reinforcement, early socialisation and trust-building with the handler increase performance, reduce behaviour indicative of any stress the dogs may be experiencing, and improve psychological wellbeing. Selective breeding has developed herding predispositions, including herding instinct and cognitive abilities, at the expense of predatory instinct. Understanding the genetic and environmental factors associated with wellbeing, and using ethical training methods benefits both dogs and livestock by allowing herding dogs to fully realise their natural behaviours. Full article

Motorcyclists face a disproportionately high risk of severe injury or death compared to other road users, highlighting the need for intelligent rider assistance technologies. This paper presents an initial, modular, and interpretable AI pipeline that generates context-aware safety advice from first-person motorcycle videos with practical inference latency suitable for on-device deployment, framing large language models as interpretable cognitive support agents for motorcycle safety. The system integrates lightweight perception and reasoning components to emulate the function of an Advanced Rider Assistance System (ARAS). Video frames are processed at 1 FPS using Pixtral, a Mistral-based multimodal large language model (MLLM), to produce descriptive scene captions, while YOLOv8 identifies key objects such as vehicles, pedestrians, and road hazards. A Mistral-small language model then fuses this information to generate concise, imperative safety tips. Preliminary evaluations on publicly available motorcycle POV datasets demonstrate promising performance in terms of contextual accuracy, interpretability, and scalability, suggesting potential for real-world deployment in low-resource or embedded environments. The proposed framework offers interpretable, context-aware safety assistance that is particularly valuable for young and newly licensed riders during the transition from supervised training to independent riding, where real-time hazard interpretation support is most needed. Full article

The study aims to develop a conceptual model for immersive chemical hazard response training that explicitly addresses four core operational constraints: time pressure, uncertainty, teamwork, and procedural/psychomotor precision. The model responds to the need for collaborative and mistake-driven training approaches in high-risk contexts. A design-oriented, theory-informed approach is applied, combining the identification of training requirements characteristic of chemical hazard response and the formulation of core operational constraints shaping the training design with the specification of CAVE affordances, a four-dimensional instructional design framework (cognitive, emotional, social, and psychomotor), conceptual alignment of scenario components with selected Sustainable Development Goals (SDGs 3, 4, 11, and 16), and a preliminary expert-based content appraisal. Results are presented as a design-oriented outcome in the form of a conceptual framework, accompanied by an illustrative scenario-based instantiation and an expert-based content appraisal demonstrating internal coherence and practical plausibility (I-CVI = 0.80–1.00; S-CVI/Ave = 0.93). Conclusions indicate that the proposed model serves as a structured instructional and scenario-design reference for immersive chemical hazard response training, positioning CAVEs as pedagogically organised learning spaces rather than as standalone simulation technologies. Further implications relate to the transferability of the model to sustainability-oriented response training across other high-risk domains. Empirical evaluation of learning processes, performance outcomes, and transfer to operational practice is identified as a necessary next step for future research. Full article

Background/Objectives: Therapeutic exercise (TE) has been shown to be an effective tool for slowing physical and cognitive decline in patients with dementia. However, its true impact on physical and functional variables, as well as the duration of its effects once therapy is discontinued, remains unclear. The aim was to analyze the short- and medium-term effects of a structured and monitored TE program on motor function in patients with dementia. Methods: A pre–post clinical trial was conducted in individuals with a medical diagnosis of mild-to-moderate cognitive impairment (Mini-Mental State Examination scores between 10 and 23) who had not engaged in regular exercise during the previous 6 months. The study variables and their measurement tools included general motor function (Short Physical Performance Battery), trunk control (Trunk Control Test), balance (Berg Balance Scale), overall mobility and gait (Timed Up and Go Test), and degree of independence in activities of daily living (ADLs) (Barthel Index). Participants completed a 12-week TE intervention at moderate intensity, 3 days per week for 45 min sessions. The program included aerobic training and strength, coordination, flexibility, and balance exercises. TE intensity was monitored through heart rate and dynamic maximal resistance. Assessments were conducted at baseline (t0), immediately after the program (t1), and 6 months after completion (t2). Results: Significant global longitudinal effects of time were observed for general motor function, balance, trunk control, and mobility and gait, whereas no significant global effect was detected for independence in activities of daily living. Post-intervention changes were non-significant; however, several pairwise comparisons showed moderate-to-large effect sizes. Follow-up assessments revealed shifts in performance distributions consistent with functional decline. Conclusions: A structured TE program performed at moderate intensity may help slow or attenuate the physical decline experienced by individuals with dementia. Full article

Traumatic brain injury (TBI) is a leading cause of long-term disability worldwide, and each person’s recovery looks different. Artificial intelligence (AI) offers promising tools to project individual outcomes. However, these models are impacted by the quality and inclusiveness of the dataset on which they are trained, having major implications for clinical value. This scoping review evaluated publicly available datasets that use AI modeling to predict outcomes from TBI. It examined how the literature derived from these datasets captures functional and social variables. Following PRISMA guidelines, 24 studies were identified, yielding 19 distinct datasets. While most datasets emphasized biomedical and injury severity metrics, few incorporated communication, cognition, and relevant social determinants of health. Nearly all studies included age and sex, but fewer than half reported race or ethnicity, and only a small subset integrated broader contextual indicators. Results suggest that outcome modeling continues to rely heavily on global scales, with limited use of domain-specific measurements. Another limiting factor is poor use of longitudinal measures, often not extending follow-up past the six-month post-injury time. These findings point to a need for inclusive, functionally rich, and ethically transparent data practices to aid AI systems in promoting equitable and clinically meaningful care. Full article

This study investigates how two widely used language-modelled generative AI tools, ChatGPT-5.1 (with DALL·E 3) and Gemini 3 (with Imagen), perform as early-stage co-design partners for biophilic interior design. Focusing on real-world use rather than theoretical capability, the research asks to what extent these systems can generate conceptually robust, visually coherent and practically feasible proposals when designers explicitly request biophilic strategies. A multiple-case design was employed across three scenarios: (1) an empty “tabula rasa” room, (2) a damaged rustic room requiring contextual renovation, and (3) a hospital staff break room to be transformed into a “cognitive restoration sanctuary.” For each case, both tools were prompted to produce a step-by-step biophilic design plan and a corresponding photorealistic image. Textual outputs were coded against the 14 Patterns of Biophilic Design and related restorative concepts, while images were evaluated by an expert panel of 15 architects with formal training in biophilic design using a structured Likert-scale instrument. Exterior and building-scale applications were not assessed. Results show that both systems can articulate broadly plausible biophilic strategies but differ in emphasis: ChatGPT tends to produce more spatially coherent, pattern-rich and functionally grounded plans, whereas Gemini excels more in visual realism and atmospheric rendering. Expert ratings indicate a consistent, though not overwhelming, preference for ChatGPT in spatial composition, human-spatial responses, contextual fit, and strategic support for cognitive restoration, with a slight advantage for Gemini in visual realism. Across all cases, however, plan-to-image fidelity is limited, particularly for non-visual and operational patterns (e.g., sound, scent, thermal variability, circadian systems, infrastructure access). The findings suggest that current generative AI tools are best positioned as fast, co-creative aides for early exploration of biophilic ideas, rather than as reliable autonomous consultants for evidence-based, cognitively targeted biophilic design. Full article

Curiosity supports adaptive cognitive and psychological functioning across the lifespan, yet prior research suggests that some dimensions of curiosity decline with age, and little is known about the psychosocial and lifestyle factors that are associated with higher curiosity in later life. This study investigated age-related differences in joyous exploration (JE) and identified key correlates of high JE among older adults. Data were drawn from the 2018 Ageing as Future study (N = 435; age 19–89). JE levels were compared between younger (<60 years) and older (≥60 years) adults using both linear regression and independent samples t-tests. To identify correlates of high JE in later life, Random Forest (RF) classification models were applied within the ≥60 cohort using stratified train-test splits and repeated cross-validation. Older adults reported significantly less JE than younger adults (p < 0.001, d = 0.52). Across multiple model specifications and sensitivity analysis, high JE in older adults was consistently associated with leisure-time hobbies, engagement in interests outside work, meaning- and purpose-related factors, generativity, select future-oriented beliefs, and social embeddedness. These findings suggest that JE in later life tends to co-exist with emotionally meaningful, socially connected activities and offers valuable insights for geriatric interventions that promote healthy aging. Full article

Parkinson’s disease (PD) is a heterogeneous clinical syndrome representing the final stage of a complex and long-lasting neurodegenerative process that involves not only dysfunction of the dopaminergic system but also impairments in other neurotransmitter systems. The diversity of the clinical presentation of PD, together with the existence of Parkinsonian syndromes and atypical Parkinsonism—such as multiple system atrophy (MSA), progressive supranuclear palsy (PSP), and dementia with Lewy bodies (DLB)—has important implications for rehabilitation outcomes and underscores the need for individualized, stage-dependent therapeutic approaches. Juggling is a complex motor activity that integrates cognitive, visuomotor, and balance processes, requiring a high level of concentration, precision, and motor adaptation. In recent years, there has been growing interest in this form of activity as a potential tool for supporting neuroplasticity, cognitive functions, and neurological rehabilitation. The aim of this review was to summarize current scientific evidence on the effects of juggling training on cognitive functions, visuomotor coordination, and balance, as well as to discuss the potential benefits of combining it with controlled hypoxia in patients with Parkinson’s disease (PD). This narrative review additionally considers how disease heterogeneity and stage of progression may influence the effectiveness of such multimodal interventions. This paper reviews the literature concerning the neurophysiological basis of learning to juggle and the mechanisms of brain plasticity, including increases in gray matter volume, improvements in white matter integrity, and reorganization of neuronal networks in motor and associative regions. Attention is drawn to the synergistic potential of combining juggling training with exposure to moderate, controlled hypoxia, which may induce an adaptive response involving the transcription factor HIF-1α, enhance the expression of brain-derived neurotrophic factor (BDNF), and promote angiogenesis and mitochondrial biogenesis. Although juggling and hypoxia are not directly related to training stimuli, both interventions activate overlapping and complementary neuroplastic pathways, providing a conceptual rationale for their parallel consideration and potential integration within future rehabilitation protocols. Juggling delivers task-specific motor–cognitive learning, whereas hypoxia may amplify molecular plasticity signaling, potentially enhancing responsiveness to motor interventions, particularly in patients at early stages of PD when compensatory mechanisms and neuroplastic capacity are relatively preserved. Findings from existing studies suggest that juggling under controlled hypoxic conditions may represent an innovative, safe, and multimodal form of training that supports both cognitive and motor components. Such effects may be particularly relevant in patients at early stages of PD, when compensatory mechanisms and neuroplastic potential are relatively preserved. Such an intervention may contribute to improvements in balance, attention, executive functions, and cognitive flexibility, which is particularly relevant in the context of rehabilitation for patients with neurodegenerative diseases. Importantly, to date, no randomized clinical trials have directly examined juggling performed under controlled hypoxic conditions in PD. Therefore, the present concept should be regarded as translational and exploratory, integrating evidence from juggling-induced neuroplasticity and hypoxia-related physiological adaptations. In this context, the proposed approach represents a proof-of-concept framework for future multimodal interventions rather than an established therapeutic strategy. Available evidence suggests that combining complex sensorimotor skill training with physiological modulation of the internal environment may constitute a novel direction in PD rehabilitation, extending beyond conventional exercise-based models. Despite promising reports, further well-designed clinical studies are needed to determine the optimal training parameters (frequency, intensity, duration, and degree of hypoxia), to evaluate the long-term sustainability of therapeutic effects, and to account for the heterogeneity of PD and related Parkinsonian disorders. Full article

Aim: Brain training games offer a promising avenue for promoting cognitive engagement and healthy aging among older adults. However, little is known about how design features align with the specific needs of this demographic to promote sustained usage and thereby cognitive intervention. The aim of this study was to characterize how all aspects of the game design and player experience might influence adherence mechanisms, and assess the feasibility and acceptability of a wearable brain-activity measuring device. Methods: We use an exploratory mixed-methods approach with n = 6 community-dwelling older adults (mean age 68 ± 3.94) within a smart-home-style Living-Lab. Participants played two commercially available brain-training games. One of the games uses a wearable brain-activity measuring device. We collected System Usability Scale (SUS) and User Experience Questionnaire (UEQ) scores and conducted focus-group interviews and structured observations. We performed a qualitative theory-informed analysis through the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework. Results: Participants reported high motivation to play brain-training games for dementia prevention. They preferred large, high-contrast text, intuitive navigation, touch-based controls, and a relaxed pacing. The wearable device was acceptable and comfortable for home use. There were requests for a clearer meaning of brain activity scores and the integration of personalized brain data with other health apps and broader health metrics. Quantitative scales (SUS and UEQ) showed similar ratings for both games, with both meeting the threshold for acceptability. Conclusions: In this formative study, concrete design features that plausibly increase engagement, persistence and adherence were identified, alongside evidence for the feasibility of integrating a wearable brain-sensor. Our findings motivate a follow-on trial testing whether an adherence-optimized design increases the training dose and downstream cognitive outcomes. Full article

Background: Emergency department (ED) overcrowding strains clinicians and potentially compromises urgent care quality. Unscheduled return visits (URVs), also known as readmissions, contribute to this cycle, motivating tools that identify high-risk patients at discharge. Methods: This study performed a retrospective study using ED electronic health records (EHRs) from Kaohsiung Veterans General Hospital from January 2018 to December 2022 (n = 184,653). The model integrates structured variables, such as vital signs, medication and laboratory counts, and ICD-10–based comorbidity measures, with unstructured physician notes. Key physiologic measurements were transformed into binary form using clinical reference intervals, and random under-sampling addressed class imbalance. A multimodal, CNN was proposed and evaluated with an 8:2 train–test split and 10-fold Monte Carlo cross-validation. Results: The proposed model achieved a sensitivity of 0.717 (CI: [0.695, 0.738]), accuracy of 0.846 (CI: [0.842, 0.850]), and AUROC of 0.853. Binary transformation improved recall and AUROC relative to the original numeric representations. SHAP analysis showed that unstructured features dominated prediction, while structured variables added complementary value. In a small-scale pilot evaluation using the SHAP-enabled interface, participating physicians reported the system helped surface high-risk cohorts and reduced cognitive workload by consolidating relevant patient information for rapid cross-checking. Conclusions: An interpretable CNN-based clinical decision support system can predict ED revisit risk from multimodal EHR data and demonstrates practical usability in a real-world clinical setting, supporting targeted discharge planning and follow-up as a near-term approach to mitigate overcrowding. Full article