Search Results (1,179)

Objective: Informed consent documents often prioritize institutional liability over patient comprehension, and conventional readability metrics are inadequate to assess their ethical and cognitive complexities. This study aims to develop and demonstrate the preliminary feasibility of the Universal Health Communication Index (UHCI), an AI-driven Health Technology Assessment (HTA) framework utilizing large language models (LLMs) to objectively evaluate the multidimensional quality and operational impact of medical consent forms. Methods: The UHCI employs a hybrid natural language processing architecture to evaluate clinical texts across five dimensions: Accessibility, Transparency, Autonomy, System Burden, and Medical Adequacy. The pipeline deploys an ‘AI Persona’ to simulate patient cognitive load and an ‘AI Gold Standard’ utilizing clinical guidelines (UpToDate) to identify omitted risks. The framework was benchmarked in an exploratory proof-of-concept against a fifty-eight-member multidisciplinary human baseline and subsequently tested in a cross-lingual proof-of-concept by comparing standardized lumbar puncture consent forms across three distinct healthcare jurisdictions (Turkey, the UK, and the USA). Results: The algorithm effectively quantified abstract bioethical concepts into an objective HTA metric, demonstrating preliminary numerical agreement with the human baseline across five pilot forms (mean difference: 2.73, SD: 5.94). The framework effectively penalized the ‘illusion of transparency’ and defensive medical terminology. In the cross-lingual case study, although the global scores were comparable, the algorithm successfully discerned between forms prioritizing patient-centered autonomy and those reflecting directive institutional discourse. Conclusions: Transcending conventional readability formulas, the UHCI provides healthcare administrators and policymakers with a scalable, practical methodology to optimize clinical documentation. By identifying institutional bias and communicational barriers, this AI-assisted screening tool promotes equitable patient engagement and supports evidence-based health policies, demonstrating the responsible integration of AI into clinical workflows without replacing human clinical judgment. Full article

Background: Smartphones and generative artificial intelligence (GenAI) are increasingly integrated into everyday life, influencing information access, digital interaction, and cognitive tasks. While problematic smartphone use and cognitive offloading are well studied, GenAI introduces newer forms of cognitive delegation that warrant comparison. Objective: This narrative review examines convergences and divergences between smartphone- and GenAI-mediated cognitive processes, focusing on cognitive offloading, attention, reliance, self-regulation, and human agency. Review approach: Interdisciplinary literature from psychology, artificial intelligence, digital technologies, and education was narratively reviewed and compared. The aim was to integrate relevant evidence rather than provide a systematic or exhaustive synthesis. Synthesis: Smartphones primarily support connectivity, information access, and digitally mediated attention, whereas GenAI extends technological assistance toward reasoning, synthesis, and content generation. Despite important differences, both may redistribute cognitive effort between individuals and digital systems, with potential implications for self-regulation and human agency. Conclusions: Smartphones and GenAI are distinct technologies with partly overlapping but also technology-specific effects. Further empirical research is needed to determine which forms of cognitive offloading and reliance are shared across technologies and their implications for human agency. Full article

Generative artificial intelligence (GenAI) is becoming embedded in adult professional education, yet its capacity to accelerate academic work may also remove cognitive processes that underpin professional judgement. This qualitative study explored how educators working in Dutch academic medical settings perceive GenAI’s implications for students’ critical thinking and how they believe it should be integrated into medical curricula. Seventeen educators from Dutch academic medical settings participated in semi-structured interviews. Data were analysed thematically using a mixed inductive–deductive approach informed by the Technology Acceptance Model and the Paul–Elder critical thinking framework. Three themes were identified: educational value and efficiency; threats to independent critical thinking; and conditions for responsible integration. Educators valued GenAI for personalised learning, writing support, research assistance, accessibility, simulation, and administrative efficiency. However, they associated low-friction use with cognitive offloading, weaker source appraisal, reduced information filtering, possible erosion of foundational knowledge, and difficulties verifying authentic student understanding. Participants proposed AI literacy, transparent use, institutionally governed models, comparative human–AI tasks, simulation, staged access, and AI-free oral or in-class assessment. The findings suggest that adult professional education should design GenAI as co-intelligence rather than cognitive substitution, preserving productive friction while verifying unassisted competence. Full article

Background: Global developmental delay (GDD) affects multiple domains of early childhood development, including gross motor, cognitive and communication skills. Early, intensive, family-centered rehabilitation is considered key to optimizing functional outcomes in affected children. Case Presentation: We report a female child with GDD who began rehabilitation at our institution at 15 months of age, presenting with generalized hypotonia with superimposed fluctuating episodes of hypertonia, poor postural control, absent independent sitting, markedly reduced spontaneous motor activity, and associated cognitive and communication delay. Brain MRI at 7 months showed no parenchymal abnormality, with mildly enlarged extracerebral cerebrospinal fluid spaces and ventricular system. The metabolic and genetic evaluation performed so far, including microarray/MLPA-based screening for common microdeletion syndromes and SMN1/SMN2 genotyping, has not identified a specific underlying etiology. Diagnostic work-up is ongoing. Rehabilitation was delivered as a comprehensive, multidomain program; this report focuses specifically on the child’s motor progression. Intervention: The child underwent the Early Intensive Stojčević-Polovina Rehabilitation Method (EIR-SPM), a high-intensity, continuous approach for children with cerebral palsy, at-risk infants, and other developmental disabilities, built on parental education enabling home-based continuity of therapy. Rehabilitation focus is selected according to the child’s optimal developmental stage—the milestone showing the least abnormal movement patterns and muscle tone—rather than chronological age, with positions progressively adjusted following the trajectory of typical motor development described by Vojta. Results: Gross motor function, monitored using the Gross Motor Function Measure–88 (GMFM-88) at four assessment points from 15 months to 6 years 6 months of age, improved progressively from 10.8% to 48.9%, 64.7%, and finally 73.9%. The child achieved independent kneeling, reciprocal crawling, independent sitting in all positions, independent standing and assisted stepping. Conclusions: In this child with GDD of undetermined etiology, more than five years of intensive, family-centered rehabilitation according to the EIR-SPM were accompanied by substantial and sustained gains in gross motor function and functional independence. This report suggests that meaningful progress remains achievable even when rehabilitation begins later than the period considered optimal within the EIR-SPM framework, and that a family-centered structure may be what makes therapy of this intensity and duration sustainable. Full article

Alzheimer’s disease (AD) is the most prevalent progressive neurodegenerative disorder in older adults, marked by insidious cognitive decline and gradual loss of functional independence. Growing evidence suggests that handgrip strength (HGS) assessment may provide a simple, noninvasive approach for early screening of AD-related functional changes. This review summarizes current evidence on the association between HGS and AD, while distinguishing evidence obtained directly in AD/MCI populations from evidence derived from general aging, cognitive decline, dementia, or non-AD disease cohorts. Longitudinal and cohort studies have reported associations between reduced grip strength and a higher risk of MCI and AD; however, HGS is influenced by age, sex, body composition, frailty, physical activity, nutritional status, comorbidities, and measurement procedures, and therefore should not be interpreted as an AD-specific diagnostic biomarker. Beyond maximal grip force, dynamic features such as force variability, temporal instability, fatigue-related change, contraction smoothness, and bilateral asymmetry may provide additional information on motor control. The review further discusses measurement standardization, disease specificity, residual confounding, reverse causality, and the methodological requirements of AI-assisted analysis. Recent advances in machine learning, deep learning, and multimodal analysis are considered as tools for integrating multidimensional functional information within an early-screening framework rather than as established diagnostic solutions. In community and home settings, HGS-based assessment may serve as a first-stage screening approach to identify individuals who warrant further clinical evaluation; definitive assessment should remain based on appropriate cognitive/behavioral and neuropsychological examinations and, when clinically indicated, neuroimaging or established AD biomarkers. Future studies should prioritize standardized protocols, prospective longitudinal designs, direct comparison with conventional clinical variables, external validation, calibration, and clinically meaningful screening outcomes. Full article

The proliferation of Artificial General Intelligence (AGI) presents a systemic paradox within complex socio-technical systems: while enhancing efficiency, AGI may subvert human autonomy through comfort-based alignment rather than overt coercion. Although algorithmic management research has theorized surveillance-based control—the “Iron Cage”—it has largely overlooked the voluntary erosion of autonomy driven by algorithmic nurturing and its recursive feedback loops. This study proposes the Algorithmic Nurturing Perspective (ANP) by synthesizing five foundational behavioral theories—bounded rationality, expectancy theory, prospect theory, goal-setting theory, and social information processing theory—reinterpreted within the AGI context. ANP elucidates how AGI systemically reconfigures socio-technical systems through three self-reinforcing causal feedback loops: the automation of choice, the externalization of emotional regulation, and isolation from social reality. We derive eight propositions illustrating how these mechanisms trigger individual-level cognitive dependence and systemic regression, which subsequently emerge as organizational-level pathologies, including learning myopia, leadership degeneration, and declining strategic decision quality. By introducing the “Golden Cage” as a novel conceptual lens, the ANP shifts the AI governance discourse from an “efficiency-vs-coercion” framework to an “autonomy-vs-compliance” paradigm. We further propose Cognitive Friction Design as a multilevel balancing intervention to mitigate these risks. As a self-contained theoretical contribution, this framework stands independently of any single empirical test while remaining falsifiable: a mechanism-differentiated system dynamics simulation demonstrates the internal coherence of the proposed feedback structure, and proposition-by-proposition falsification conditions specify the empirical pathway for causal verification in future research. We further identify balancing-dominant conditions under which algorithmic assistance may enhance, rather than erode, human autonomy. Full article

This study examines whether perceived interactivity is associated with tourists’ continuance intention toward generative AI travel assistants through two parallel post-use evaluations—perceived usefulness and flow experience—while accounting for individual technology-related dispositions. Drawing on the Stimulus–Organism–Response (S–O–R) framework, the model conceptualizes perceived usefulness as a cognitive–instrumental evaluation and flow experience as an experiential–absorptive evaluation, while examining personal innovativeness in information technology and technology anxiety as focal moderators, selected on theoretical grounds, of the interactivity–flow and interactivity–usefulness associations, respectively. Data were obtained through a purposive online survey of 853 tourists with prior experience using generative AI for travel-related purposes, and the proposed relationships were tested using partial least squares structural equation modeling (PLS-SEM). The findings show that perceived interactivity is positively associated with continuance intention, perceived usefulness, and flow experience. Both perceived usefulness and flow experience are positively associated with continuance intention and exhibit significant indirect associations between perceived interactivity and continuance intention when estimated simultaneously. The indirect association through perceived usefulness is numerically larger than that through flow experience, while the remaining direct association is consistent with the two evaluations providing a complementary but non-exhaustive account of continuance. Personal innovativeness is positively associated with flow experience, and the positive interactivity–flow association is estimated to be stronger at higher levels of personal innovativeness. Technology anxiety is negatively associated with perceived usefulness, and the positive interactivity–usefulness association is estimated to be weaker at higher levels of technology anxiety. Although statistically significant, both interaction magnitudes are small and therefore provide limited rather than strong evidence of association-specific heterogeneity across tourists. The model demonstrates moderate explanatory performance and positive but limited benchmark-relative out-of-sample predictive relevance. The study contributes to research on generative AI-enabled tourism by showing that perceived interactivity is concurrently associated with distinct and nonredundant instrumental and experiential forms of post-use value, each of which retains a separate association with continuance intention. This dual pattern is particularly relevant to travel decision-making, in which tourists frequently navigate complex and interdependent choices under experiential uncertainty while remaining engaged in an evolving planning process. Within the S–O–R framework, the study specifies a context-bound dual-evaluation account of generative AI post-adoption rather than claiming that the inclusion of additional mediators or moderators constitutes a general extension of the framework. The findings offer general practical considerations for designing generative AI travel assistants that combine responsive interaction with functional value and engaging user experiences. However, disposition-specific design and segmentation implications should be treated as tentative given the small interaction magnitudes. Full article

Artificial intelligence is becoming deeply embedded in higher education, yet how the characteristics of AI-based e-learning systems relate to students’ perceived learning effectiveness and satisfaction remains insufficiently understood. This study examines the relationships of AI Functionality Compatibility, AI Instructional Process Coverage, and AI-Assisted Learning Cognitive Usability with Perceived Online Learning Effectiveness and Online Learning Satisfaction. Data from 384 students at Chinese universities were analyzed using a two-stage approach combining partial least squares structural equation modeling and artificial neural networks (PLS-SEM-ANN). The results showed that all three system characteristics were positively associated with perceived learning effectiveness, with instructional process coverage showing the strongest relationship. Cognitive usability also had a significant direct association with learning satisfaction, whereas functionality compatibility and instructional process coverage showed significant indirect effects through perceived learning effectiveness. The findings reveal an outcome-oriented pattern in which perceived learning effectiveness occupies a central position between system characteristics and satisfaction. This study extends understanding of AI-supported learning systems by emphasizing the alignment of technical functions with pedagogical processes and learners’ cognitive needs. It also provides practical guidance for universities and developers seeking to better align the design and evaluation of AI-based e-learning systems with learners’ instructional and cognitive needs. Full article

Against the backdrop of sustainable education and the digital transformation of higher education, AI assistants on online learning platforms have become important tools for facilitating students’ access to learning resources, providing immediate feedback, and supporting self-directed learning, thereby creating new opportunities for more effective and sustainable learning experiences. However, the psychological mechanisms linking AI assistant intelligence to students’ innovative behavior remain unclear. Based on Social Cognitive Theory (SCT), this study examines how AI assistant intelligence on online learning platforms relates to higher education students’ innovative behavior through academic self-efficacy and academic procrastination and further explores the moderating role of AI literacy. Using a two-phase time-lagged design, the study collected data through online questionnaires from students at Chinese universities, yielding a final matched sample of 503 students. The findings show that AI assistant intelligence is positively associated with both academic self-efficacy and academic procrastination, but it has no significant direct effect on innovative behavior. Importantly, these two mediators operate in opposing directions: academic self-efficacy transmits a positive indirect effect, whereas academic procrastination transmits a negative indirect effect on innovative behavior. In addition, AI literacy strengthens the positive relationship between AI assistant intelligence and academic self-efficacy while weakening its positive relationship with academic procrastination. These findings reveal the dual psychological pathways through which AI assistant intelligence shapes students’ innovative behavior, extend Social Cognitive Theory in intelligent learning environments, and offer practical implications for leveraging AI-assisted learning to foster students’ innovative capacity and inform the sustainable integration of AI in higher education. Full article

To investigate the potential of generative artificial intelligence (Gen AI) in sustainable engineering education and laboratory instruction, this study used the direct shear test in soil mechanics as a case study. A total of 112 third-year undergraduate students majoring in hydraulic engineering were assigned to traditional and AI-assisted groups. Their performance in experimental operation, data processing, report writing, presentation of results and problem solving was compared using grade statistics, classroom observations and interview data. The results showed that the AI group generally outperformed the traditional group in Experimental operation, data analysis, report completeness, presentation and defense, and overall scores. The analysis showed that Gen AI can function as a form of cognitive scaffolding in conceptual explanation, data processing, report structuring, and error analysis. Specifically, it can provide stage-specific prompts for understanding, procedural support, and feedback during the learning process, thereby helping students complete experimental learning tasks. However, the qualitative corpus included documented cases of overreliance on Gen AI, including uncritical acceptance of generated outputs and alteration of discrepant data without adequate verification; these behaviours were not tabulated at the pair level, so their prevalence cannot be estimated. These findings suggest that the integration of Gen AI into soil mechanics laboratory instruction should be grounded in teacher guidance, disciplinary knowledge support, data verification awareness, and standardized tool-use practices. These findings also offer implications for the responsible use of Gen AI in sustainable engineering education and for the development of students’ AI literacy, awareness of data authenticity, and responsible technology use competencies. Full article

Background/Objectives: Cognitive stimulation and cognitive training are used as non-pharmacological approaches to support people living with Alzheimer’s disease, but Alzheimer-specific evidence is dispersed across heterogeneous intervention formats. This scoping review mapped structured cognitive stimulation and training interventions evaluated in people with mild-to-moderate Alzheimer’s disease and summarized the cognitive, emotional, functional, and follow-up outcomes reported. Methods: The review was conducted using the Joanna Briggs Institute methodology and reported according to PRISMA-ScR. PubMed, SciELO, PEDro, LILACS, and Google Scholar were searched on 24 February 2025 for studies published between January 2015 and 24 February 2025 in English, Portuguese, or Spanish. Intervention studies were eligible. Two reviewers independently screened records and charted data, with disagreements resolved by a third reviewer. Results: Of 352 records identified, five studies involving 245 participants were included. Interventions comprised virtual-reality cognitive stimulation, conventional cognitive training, group reminiscence therapy, a multicomponent music–reminiscence–reality-orientation intervention, and computerized cognitive training. In the limited technology-assisted evidence, statistically significant changes were reported in global cognition or selected memory, language, attention, and executive outcomes. Conventional cognitive training showed signals of improvement in initiative and temporary stabilization of memory, whereas reminiscence-based interventions primarily reported changes in depressive and neuropsychiatric symptoms. Where longer follow-up was available, benefits diminished over time. Conclusions: The mapped evidence suggests that structured cognitive stimulation and training may produce short-term, outcome-specific benefits in mild-to-moderate Alzheimer’s disease. Given the small and heterogeneous evidence base, these findings represent modality-related patterns within the included studies and should not be interpreted as evidence of comparative effectiveness. Full article

Robotics serves as a key enabler in disability support by offering new pathways to augment social work practice and enhance autonomy, safety, and inclusion for persons with disabilities (PWDs). In this study, an interdisciplinary team executed a socio-technical investigation that integrated robotics engineering, artificial intelligence, rehabilitation sciences, and social work. It was examined how assistive and socially interactive robots, including mobility robots, cognitive-assistive systems, exoskeletons, telepresence units, and socially assistive humanoids, must be embedded within disability services to improve functional independence, strengthen care continuity, and address increasing workforce demands. A comprehensive research design was adopted by combining a systematic literature review and technical benchmarking of robot capabilities with qualitative inputs gathered from co-codesign workshops involving PWDs, caregivers, and social workers. To test these applications, the team evaluated three pilot domains: home-based independent living support, community-based rehabilitation, and social-work-led remote engagement utilizing telepresence robotics. The results demonstrate that these robotic interventions improved independent task completion by 22–41% and reduced caregiver burden by 18–34%. Furthermore, the data revealed significant gains in communication and emotional engagement for individuals with cognitive or speech impairments. While robotics cannot replace social workers, these technologies meaningfully complement care delivery when practitioners develop them through ethical, participatory, and contextually sensitive frameworks. Ultimately, this paper highlights clear pathways toward scalable, inclusive robotic support systems that align engineering innovation with person-centered social work values. Full article

This exploratory study examined a dual-role artificial intelligence-supported programming environment that integrated human-in-the-loop item generation and on-demand AI debugging within a flipped cognitive apprenticeship framework. Programming tasks were initially generated by GPT-4o and were subsequently reviewed, revised, and approved by the instructor before release. Both instructional conditions completed the same instructor-approved tasks and received the same compiler and automated test-case feedback. Students in the AI Debug-enabled condition could additionally request diagnostic guidance after an unsuccessful submission, whereas students in the comparison condition did not have access to AI-generated debugging assistance. The analytic sample comprised 45 students in the AI Debug-enabled condition and 16 students in the comparison condition. Prior-semester programming achievement, operationalized as the total grade obtained in the preceding programming course, was used as an archival baseline covariate. The observed baseline difference was not statistically significant. Programming-task performance was recorded across 13 instructional weeks. A generalized estimating equation model controlling for prior-semester programming achievement showed no significant overall condition effect, but revealed significant effects of instructional week and the Condition × Week interaction. Prior-semester programming achievement significantly predicted weekly task performance. The comparison condition also obtained higher midterm and final examination scores after adjustment for prior achievement. The findings do not demonstrate a consistent programming-performance advantage associated with access to AI Debug. Instead, adjusted differences varied across instructional weeks and assessment contexts. Because the study used naturally occurring nonequivalent groups and did not retain AI Debug activation records, the results should be interpreted as exploratory associations concerning feature availability rather than causal effects of actual AI use. The primary contribution lies in documenting a human-supervised architecture that integrates instructor-facing AI item generation and student-facing debugging assistance while distinguishing AI-supported task performance from independent programming achievement. Full article

This study presents the design, implementation, and exploratory classroom evaluation of a course-constrained educational assistant combining Retrieval-Augmented Generation (RAG) with large language models (LLMs) in Vocational Education and Training (VET). The platform retrieves teacher-provided materials, generates course-aligned responses, displays source references, and returns a predefined abstention message when evidence appears insufficient. It was deployed during a practical session in the Spanish Higher Vocational Training programme Administración de Sistemas Informáticos en Red (ASIR; Network and Information Systems Administration). Nineteen students and one instructor completed a post-session questionnaire. Students reported positive perceptions of usability, clarity, perceived reliability, and learning support, while restricted corpus coverage and the absence of conversational memory emerged as limitations. To complement the perception-based classroom study, a separate post hoc technical assessment used 30 predefined queries with the same prototype and corpus. All 16 fully answerable prompts received substantive responses; 13 were rated fully correct and three partially correct by the first author. Seven of eight deliberately out-of-corpus prompts triggered abstention, whereas one OpenShift Route query produced an unsupported answer from semantically adjacent but non-supporting Kubernetes evidence. The contribution is empirical and design-oriented rather than algorithmic. The findings are context-specific and show both the potential and limitations of bounded cognitive support grounded in instructor-selected materials. Full article

The rapid integration of generative artificial intelligence (GenAI) into educational settings has raised growing concerns regarding its potential influence on students’ higher-order thinking skills. However, existing findings remain inconclusive, and the role of individual factors in shaping these effects has received limited attention. Drawing on self-regulated learning theory, this study examined the relationships between GenAI usage and three higher-order thinking skills, critical thinking, creative thinking, and computational thinking, and investigated the moderating role of self-regulated learning. The results indicated that the associations between GenAI use and higher-order thinking skills varied across levels of self-regulated learning. Specifically, among students with lower levels of self-regulated learning, GenAI use was negatively associated with critical thinking, creative thinking, and computational thinking. In contrast, among students with higher levels of self-regulated learning, GenAI use was positively associated with critical thinking and computational thinking, while its association with creative thinking was not significant. Furthermore, self-regulated learning was positively associated with all three higher-order thinking skills and significantly moderated the relationships between GenAI use and critical thinking, creative thinking, and computational thinking. These findings highlight the complex cognitive consequences of GenAI usage and underscore the importance of self-regulated learning in shaping students’ cognitive outcomes in AI-assisted learning environments. The study contributes to a more nuanced understanding of how GenAI influences higher-order thinking and provides implications for promoting effective and responsible AI use in education. Full article