Search Results (145)

Background/Objectives: Mental health challenges are increasingly prevalent among higher education students, with significant implications for academic success and personal development. Emerging research suggests that critical thinking dispositions may support psychological well-being by enhancing resilience and adaptive coping. This study aimed to investigate the relationship between critical thinking dispositions and psychological well-being and to identify key sociodemographic predictors in this context. Methods: A cross-sectional design was employed from December 2024 to May 2025, recruiting 429 students from Portuguese higher education institutions via convenience sampling. Participants completed validated self-report measures: the Critical Thinking Dispositions Scale (CTDS) and the Psychological Well-Being Scale (PWBS), assessing seven critical thinking dispositions and six well-being dimensions, respectively. Sociodemographic data were also collected. Descriptive statistics, independent t-tests, one-way ANOVA, Pearson correlations, and hierarchical multiple regression were used for data analysis. Results: Students demonstrated moderate to high levels of critical thinking and psychological well-being, with higher scores associated with increased age and academic progression. Significant positive correlations were identified between critical thinking dispositions and all well-being dimensions; personal growth, purpose in life, and autonomy exhibited the strongest associations. Regression analysis revealed that confidence in reasoning, cognitive maturity, and open-mindedness were significant predictors of psychological well-being, explaining 28.7% of the variance. Conversely, inquisitiveness showed a negative association with psychological well-being in the multivariate model, an unexpected finding that warrants cautious interpretation and further investigation. Conclusions: Critical thinking dispositions reflect affective tendencies and habitual ways of engaging with thinking. These dispositions appear to protect psychological well-being in higher education students. Integrating the development of emotional awareness and reflective thinking into curricula may therefore foster resilience and academic success. Further longitudinal research is needed to explore causal mechanisms and intervention efficacy in broader academic contexts. Full article

This study explores how immersive e-learning technologies influence entrepreneurial intention among business education students, with a focus on the mediating role of learning satisfaction. Using a quantitative approach, data were collected from 561 final-year undergraduate students enrolled in business and entrepreneurship programs at globally ranked universities. The relationships between immersive learning design features, learning satisfaction, and entrepreneurial intention were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The study draws on Experiential Learning Theory, Self-Determination Theory, and Expectancy–Value Theory to explain how immersive learning experiences shape entrepreneurial motivation. The results show that interactivity, experiential engagement, and personalization positively influence entrepreneurial intention, primarily by enhancing learning satisfaction. Students are more inclined toward entrepreneurial careers when immersive learning environments support autonomy, meaningful engagement, and perceived value. In contrast, high levels of realism and multisensory intensity do not consistently strengthen entrepreneurial intention, suggesting that excessive immersion may create cognitive strain or diminishing motivational returns under certain conditions. These findings highlight the importance of balanced and learner-centered immersive learning design rather than increased technological intensity alone. From a practical perspective, the study suggests that business schools should integrate immersive technologies in ways that emphasize experiential learning, adaptability, and cognitive balance. However, the findings should be interpreted with caution due to the cross-sectional design, reliance on self-reported data, and focus on final-year students. Overall, the study provides a nuanced understanding of how immersive e-learning can support entrepreneurial intention while also identifying important boundary conditions that shape its effectiveness. Full article

The rapid expansion of algorithmic systems has transformed cognition into an increasingly distributed and collective enterprise, giving rise to what can be described as cognitive assemblages, dynamic constellations of humans, institutions, data infrastructures, and artificial agents. This paper traces the historical and conceptual evolution that has led to this shift. First, we show how cognition, once conceived as the property of autonomous individuals, has progressively become embedded in socio-technical networks in which algorithmic processes participate as co-agents. Second, we revisit the progressive awareness of human cognitive limits, from bounded rationality to contemporary theories of extended mind. These frameworks anticipate and help explain today’s hybrid cognitive ecologies. Third, we assess the philosophical implications for Enlightenment ideals of autonomy, rationality, and self-governance, showing how these concepts must be reinterpreted in light of pervasive algorithmic intermediation. Finally, we examine global initiatives that seek to integrate augmented cognitive capacities into large-scale cybernetic forms of societal coordination, ranging from digital platforms and data spaces to AI-driven governance systems. These developments offer new opportunities for steering complex societies under conditions of globalization, environmental disruption, and the rise of autonomous intelligent systems, yet they also raise profound questions regarding control, accountability, and democratic legitimacy. We argue that understanding cognitive assemblages is essential to designing socio-technical systems capable of supporting collective intelligence while preserving human values in an era of accelerating complexity. Full article

The author investigates how digital learning competence (DLC) is bridged to academic achievement (AA) through informal digital learning engagement (IDLE) and how meta-cognitive self-regulation (MSR) shapes these pathways among university students. Grounded in a moderated mediation framework, this research conceptualizes DLC not as a static skill set but as a latent capacity that is channeled into academic outcomes when students autonomously engage in digital environments and regulate their cognition. Survey data were collected from 432 undergraduate students and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results show that DLC significantly predicts AA both directly and indirectly via IDLE, identifying informal digital learning engagement as a central pathway through which digital learning competence is translated into academic gains. Furthermore, MSR moderates the relationship between DLC and IDLE, such that higher levels of metacognitive self-regulation strengthen the conversion of digital learning competence into productive informal digital learning engagement. These findings support a dynamic view of digital learning competence and underscore the roles of learner autonomy and metacognitive awareness in transforming digital skills into meaningful educational outcomes. By integrating perspectives on digital literacy, self-regulated learning, and informal learning, this study offers implications for the design of digital learning ecosystems that effectively bridge students’ digital capacities with their academic achievement. Full article

Intelligent Clinical Decision Support Systems (ICDSS) are increasingly integrated into healthcare settings to enhance clinical decision-making, efficiency, and patient safety. Despite advances in artificial intelligence-enabled decision support, ICDSS adoption remains inconsistent, particularly in complex clinical environments where professional autonomy, workflow alignment, and accountability are critical. This study examines healthcare providers’ perspectives on ICDSS through a grounded theory approach informed by established Information Systems theories, including the Unified Theory of Acceptance and Use of Technology (UTAUT), Technology Acceptance Model (TAM), Diffusion of Innovation (DOI), and the Human-Organization-Technology fit (HOT-fit) framework. Semi-structured interviews were conducted with 11 providers within a large, integrated healthcare organization, and data were analyzed using open, axial, and selective coding. The findings reveal three interrelated dimensions shaping ICDSS use: provider experience, clinical utility, and adaptation. While ICDSS were perceived as valuable for improving efficiency, supporting treatment decisions, and enhancing patient safety, their adoption was constrained by cognitive overload, workflow misalignment, data quality concerns, and perceived threats to professional autonomy. Trust, explainability, and workflow fit emerged as central mechanisms influencing selective use rather than full adoption. By grounding provider perspectives within a sociotechnical lens, this study extends existing IS theories to the context of AI-enabled clinical decision support and offers empirically grounded insights for designing ICDSS that better align with clinical practice. Full article

Contemporary artificial intelligence (AI) achieves remarkable results in data processing, text generation, and the simulation of human cognition. However, it appears to lack key characteristics typically associated with living systems—consciousness, autonomous motivation, and genuine understanding of the world. This article critically examines the possible ontological divide between simulated intelligence and lived experience, using the metaphor of the motorcycle and the horse to illustrate how technological progress may obscure deeper principles of life and mind. Drawing on philosophical concepts such as abduction, tacit knowledge, phenomenal consciousness, and autopoiesis, the paper argues that current approaches to developing Artificial General Intelligence (AGI) may overlook organizational principles whose role in biological systems remains only partially understood. Methodologically, it employs a comparative ontological analysis grounded in philosophy of mind, cognitive science, systems theory, and theoretical biology, supported by contemporary literature on consciousness and biological autonomy. The article calls for a new paradigm that integrates these perspectives—one that asks not only “how to build smarter machines,” but also “what intelligence, life, and consciousness may fundamentally be,” acknowledging that their relation to computability remains an open question. Full article

Background/Objectives: Healthcare and human service organizations face mounting pressures to adapt to social and public health challenges while maintaining quality care. Innovative work behavior among healthcare and human service professionals is critical to organizational resilience. Prior research suggests that job autonomy fosters innovative work behavior, but the mechanisms underlying this relationship remain unclear. This study examined how cognitive appraisal, work engagement, and job burnout mediate the relationship between job autonomy and innovative work behavior. Methods: A non-experimental, cross-sectional online survey was conducted with 607 healthcare and human service professionals in the United States. Validated measures assessed job autonomy, cognitive appraisal, work engagement, job burnout, and innovative work behavior. Serial mediation analyses were performed using Hayes’ PROCESS macro (Model 6) with bootstrapping (n = 5000). Work innovation was included as a covariate to control for organizational climate effects. Results: Job autonomy was positively related to innovative work behavior, work engagement, and both challenge and hindrance appraisal. The direct relationship between job autonomy and job burnout was mixed, significant in the hindrance appraisal model but not in the challenge appraisal model. Mediation analyses revealed that challenge and hindrance appraisal significantly influenced the pathways from job autonomy to work engagement and job burnout, which in turn mediated the job autonomy—innovative work behavior relationship. Burnout had a significant negative effect on innovative work behavior, whereas engagement strengthened the positive relationship between job autonomy and innovative work behavior. The full model explained 65.12–67.73% of the variance in innovative work behavior. Conclusions: Job autonomy is a critical driver of innovative work behavior among healthcare and human service professionals, operating through appraisal, engagement, and burnout. Building on previous research, this study extends prior evidence by clarifying when autonomy enables professionals to thrive and innovate, and when it risks contributing to burnout. Findings underscore the importance of appraisal-based interventions and autonomy-supportive climates to sustain workforce well-being and organizational innovation. Full article

Background: The rapid diffusion of large language models (LLMs) such as Claude, ChatGPT, Gemini, LLaMA, and Mistral is reshaping logistics and supply chain management by embedding generative intelligence into planning, coordination, and governance processes. While prior studies emphasize algorithmic capability, far less is known about how differences in diffusion pathways shape productivity outcomes, managerial cognition, and institutional control. Methods: This study develops and applies an integrative analytical framework—the AI Diffusion Triad—comprising Productivity, Perspective, and Power. Using comparative qualitative analysis of five leading LLM ecosystems, the study examines how technical architecture, access models, and governance structures influence adoption patterns and operational integration in logistics contexts. Results: The analysis shows that diffusion outcomes depend not only on model performance but on socio-technical alignment between AI systems, human workflows, and institutional governance. Proprietary platforms accelerate productivity through centralized integration but create dependency risks, whereas open-weight ecosystems support localized innovation and broader participation. Differences in interpretability and access significantly shape managerial trust, learning, and decision autonomy across supply chain tiers. Conclusions: Sustainable and inclusive AI adoption in logistics requires balancing operational efficiency with interpretability and equitable governance. The study offers design and policy principles for aligning technological deployment with workforce adaptation and ecosystem resilience and proposes a research agenda focused on diffusion governance rather than algorithmic advancement alone. Full article

The digital era represents a paradigm shift in gifted education, moving at an accelerating pace away from traditional models toward flexible and personalized technology-based pathways. This study investigates the impact of a model implemented via the FutureX platform in Saudi Arabia on the autonomy and self-regulated learning (SRL) of 63 gifted high school students. Using a quasi-experimental design, the study integrated quantitative measures (paired t-tests) with phenomenological analysis of interviews. The quantitative results showed statistically significant improvements (p < 0.001) in the dimensions of autonomy and self-regulated learning, with large Cohen’s d effect sizes for planning (d = 1.05), monitoring (d = 1.05), and cognitive control (d = 1.30). These gains were supported by a pedagogical design intentionally embedded within the platform to scaffold self-regulation. These findings were reinforced by qualitative results, with 88% of gifted students reporting that the platform provided appropriately challenging content and promoted self-learning and goal-setting behaviors. Full article

The present study examines the well-being of workers in an Italian manufacturing plant, focusing on work engagement, emotional exhaustion and work ability. These dimensions have received relatively little attention in manufacturing contexts. Utilising a person-centred approach, the objective is to identify distinct subjective well-being profiles among Italian manufacturing workers and to examine how work-related psychosocial characteristics differentiate these profiles. The research, which collected data from 340 workers (predominantly male at 62.1%) between July and September 2023, focused on work engagement, emotional exhaustion, and work ability—factors that have been previously understudied in manufacturing environments. Through cluster analysis, researchers were able to identify three worker profiles. The largest group, designated “Motivated & Healthy” (45.3%), exhibited the most favourable characteristics: strong work engagement, minimal emotional exhaustion, and adequate work ability. These workers reported experiencing reduced physical demands, greater autonomy in decision-making, and superior rewards compared to their colleagues. The second-largest group, “Motivated & Stressed” (32.5%), demonstrated a mixed profile. While maintaining average work engagement, these workers experienced high levels of emotional exhaustion and diminished work ability. The smallest group, termed “Disillusioned” (22.2%), consisted entirely of blue-collar workers and exhibited the most concerning pattern: low engagement, high exhaustion, and mediocre work ability. This group also reported the most challenging working conditions, including the highest physical and cognitive demands, least decision-making authority, and lowest rewards. The study corroborates earlier research findings by identifying significant relationships between work engagement and work ability (positive correlation) and emotional exhaustion (negative correlation). These results suggest that manufacturing facilities might benefit from tailoring their support strategies to address the specific needs of each worker profile, rather than applying one-size-fits-all solutions. Full article

This study presents Care-MOVE, a smartphone-based application designed for continuous, passive, and unobtrusive monitoring of mobility, environmental exposure, and cognitive status in older adults within a telemedicine framework. The system integrates movement-related data collected through smartphone sensors (GPS, activity recognition, and caloric expenditure estimation) with contextual air quality information and standardized neuropsychological assessments, resulting in a comprehensive multimodal dataset (Care-MOVE Dataset). An exploratory proof-of-concept study was conducted on a subsample of 53 participants aged over 65, each monitored continuously for five days, contributing on average more than 30,000 longitudinal records. To investigate whether daily motor behavior can serve as a digital biomarker of cognitive functioning, several Machine Learning and Deep Learning models were evaluated using a Leave-One-User-Out (LOUO) cross-validation strategy. The comparative analysis included traditional classifiers (Logistic Regression, Random Forest, Gradient Boosting, K-Nearest Neighbors, and Support Vector Machines) as well as temporal deep learning architectures (1D CNN, LSTM, GRU, and Transformer). Among all of the evaluated approaches, the Support Vector Machine with RBF kernel achieved the best performance, reaching an accuracy of 98.1%, a balanced accuracy of 0.988, and an F1-score of 0.981, demonstrating robust generalization across unseen subjects. For this reason, the study was designed and presented as an exploratory proof-of-concept rather than a definitive clinical validation. This integrated approach not only enables the collection of detailed and contextualized data but also opens new perspectives for proactive digital healthcare, focused on risk prevention, improving quality of life, and promoting autonomy in elderly patients. Full article

Financial education is critical for fostering inclusive, resilient societies by reducing inequalities and promoting sustainable development. Despite growing interest, there is a notable gap in research on effective pedagogical approaches for financial literacy in primary education. Early intervention is essential to shape financially literate, active citizens capable of contributing to sustainable communities. This study addresses this gap by comparing the effectiveness of two distinct implementation packages involving active pedagogies—cooperative learning (CL) with high autonomy and stable groups, and game-based learning (GBL) featuring stronger teacher orchestration and dynamic grouping—in enhancing financial literacy among primary school pupils. Using a multiple case study design, two fifth-grade classes in Northern Italy participated in six 2 h financial education sessions, each employing a different instructional implementation package. Quantitative data from pre- and post-tests revealed significant improvements in financial literacy in both groups, confirmed by Wilcoxon signed-rank tests, with CL-based implementation showing a larger observed effect size (Hedges’g = 1.84) than GBL-based implementation (g = 1.20). Qualitative analysis of focus groups showed that CL-based implementation, characterized by high autonomy and group stability, fostered deeper learning through collaboration, shared responsibility, and relational skills vital for social sustainability. In contrast, GBL-based implementation, with structured teacher facilitation, promoted context-specific knowledge and relied more on extrinsic motivation and competition. These findings suggest that the observed benefits may be associated with the combined features of the cooperative learning-based implementation package, particularly high autonomy and stable group structures, which appear to support more socially embedded financial literacy. They also highlight both the potential and the limitations of game-based approaches. The study offers evidence-based insights for designing effective financial education programs that support both cognitive and social competencies in primary education. Full article

As artificial intelligence (AI) continues to expand across clinical environments, healthcare is transitioning from static decision-support tools to dynamic, autonomous agents capable of reasoning, coordination, and continuous interaction. In the context of geriatric medicine, a field characterized by multimorbidity, cognitive decline, and the need for long-term personalized care, this evolution opens new frontiers for delivering adaptive, assistive, and trustworthy digital support. However, the autonomy and interconnectivity of these systems introduce heightened cybersecurity and ethical challenges. This paper presents a Secure Agentic AI Architecture (SAAA) tailored to the unique demands of geriatric healthcare. The architecture is designed around seven layers, grouped into five functional domains (cognitive, coordination, security, oversight, governance) to ensure modularity, interoperability, explainability, and robust protection of sensitive health data. A review of current AI agent implementations highlights limitations in security, transparency, and regulatory alignment, especially in multi-agent clinical settings. The proposed framework is illustrated through a practical use case involving home-based care for elderly patients with chronic conditions, where AI agents manage medication adherence, monitor vital signs, and support clinician communication. The architecture’s flexibility is further demonstrated through its application in perioperative care coordination, underscoring its potential across diverse clinical domains. By embedding trust, accountability, and security into the design of agentic systems, this approach aims to advance the safe and ethical integration of AI into aging-focused healthcare environments. Full article

Background: Attention Deficit Hyperactivity Disorder (ADHD) is associated with executive function deficits—such as planning, organization, and prospective memory—that impair autonomy and daily functioning, increase family stress, and create challenges in educational contexts. These consequences underscore the need for accessible and ecologically valid interventions addressing the cognitive, familial, and educational dimensions. Traditional approaches often lack ecological validity, and pharmacological treatment shows a limited impact on functional cognition. Objectives: This protocol outlines a feasibility study of DiverAcción, a web-based telerehabilitation system designed to enhance functional cognition through interactive and gamified tasks integrated into a comprehensive healthcare programme. Methods: A quasi-experimental feasibility study before and after the study will recruit 30 participants aged 9 to 17 years with ADHD. The study comprises an initial face-to-face session for instructions and baseline assessment (T0), followed by twelve supervised online sessions over six weeks. Therapeutic support is provided via integrated chat, email, and two scheduled videoconference check-ins. Feasibility Outcomes: include recruitment, adherence, retention, usability (SUS), acceptability (TAM), satisfaction, user-friendly design, therapeutic alliance (WAI-I), and professionals’ attitudes toward technology (e-TAP-T). Exploratory Measures: include parental self-efficacy (BPSES), parenting stress (PSI-4-SF), ADHD symptomatology (SNAP-IV), executive functioning (BRIEF-2), time management (Time-S), emotional regulation (ERQ-CA), prospective memory (PRMQ-C), and health-related quality of life (KIDSCREEN-52). Analyses emphasize descriptive statistics for feasibility metrics (recruitment, adherence, retention, dropout and fidelity). Assessments are conducted post-intervention (T1) and at three-month follow-up (T2) and analyzed relative to baseline using repeated-measures ANOVA or Friedman tests, depending on data distribution. Conclusions: This feasibility protocol will provide preliminary evidence on the usability, acceptability, and implementation of DiverAcción. Findings will guide refinements and inform the design of a subsequent randomized controlled trial. Full article

Background: Low and middle-income countries (LMICs) such as Mexico are experiencing rapid population aging, accompanied by increasing levels of functional dependency and growing long-term care (LTC) needs. Objectives: We aimed to identify the factors associated with varying levels of functional dependency in order to assist population health planning and LTC policy in aging populations in Mexico. Methods: This cross-sectional study analyzed data from the 2021 wave of the Mexican Health and Aging Study (MHAS). Functional dependency was assessed through a modified Autonomie Gérontologie Groupes Iso-Ressources (AGGIR) scale, adapted to incorporate cognitive and physical assessments suitable for the Mexican context. Socioeconomic, health-related, and psychological variables were examined using ordinal logistic regression models. Results: Among 8049 participants included in the analysis, 87.08% were classified with non-to-mild dependency, 9.13% with moderate dependency, and 3.79% with severe dependency. More severe levels of functional dependency were associated with older age, lower educational attainment, not having a partner (being single, widowed, separated or divorced), and the presence of chronic conditions such as hypertension and cardiovascular disease. Conclusions: In contrast, higher educational attainment and regular physical activity were associated with less severe levels of dependency. These associations highlight the multifactorial nature of dependency in later life. The application of a graded, multidimensional dependency classification provides a more comprehensive and differentiated understanding of care needs than binary functional measures. This population-level perspective may support the prioritization of healthy aging strategies and long-term care planning in rapidly aging middle-income settings such as Mexico. Full article