Search Results (3,532)

Clinical practice guidelines require expert synthesis that large language models (LLMs) might partly automate, yet their ability to reproduce clinically actionable recommendations is poorly quantified. We evaluate an LLM (Claude Sonnet 4.6) against the 103 recommendations of the Spanish enhanced-recovery guideline Vía RICA 2026, grouped in 17 bundles. The model used the panel’s own closed corpus (617 documents) in a multilingual retrievalaugmented generation pipeline. Concordance was assessed twice: by optimal 1:1 bipartite matching (Hungarian) on cosine similarity, and by an LLM-as-a-judge clinical adjudicator (Claude Haiku 4.5) validated against a three-clinician panel (Fleiss’ κ = 0.538). The two schemes bracket a micro F₁ of 0.61–0.69 and reveal four findings: (i) a systematic granularity bias, producing 1–8 recommendations per bundle regardless of ground-truth size; (ii) failure of cosine similarity to discriminate within narrow clinical domains; (iii) high reference-concordance precision (0.70–0.81) despite low exhaustiveness; and (iv) no transfer of the GRADE fields, evidence level agreeing no better than chance and strength systematically downgraded. An eight-fold larger retrieval budget left it intact. A corpus audit found 25 documents that formulate recommendations; excluding them lowers judged micro F₁ to 0.602. The results delimit the current utility of generative AI for guideline development. Full article

Gender bias in generative artificial intelligence (GenAI) is both a technical and a social phenomenon: it emerges from historically patterned data, model design, and interactions in institutional use, and it cannot be understood by engineering or by social critique alone. This critical integrative review develops a more differentiated account. It connects feminist epistemology, Science and Technology Studies, critical AI scholarship, natural language processing, and governance research to examine five levels: historical knowledge production, technical representation and generation, benchmark evaluation, institutional deployment, and accountability. The review explains tokenization, next-token prediction, transformers, and the transition from static embeddings to contemporary language models before assessing evidence from standard fairness tests—coreference tests (WinoBias), sentence-pair tests (CrowS-Pairs), and stereotype tests (StereoSet)—as well as open-ended generation, multilingual testing, and text-to-image systems. It shows that measured bias varies with task, prompt, language, model version, and metric. What a test records and what that record means are therefore distinct questions: measurements are situated and depend on the instrument, and their interpretation draws on theory rather than following from the numbers alone. Evidence from employment, education, healthcare, and translation further indicates that the relevant unit of analysis is the model-in-context—the model together with the institution and workflow in which its outputs are used. Technical mitigation can reduce specific harms but does not repair unequal criteria, incomplete evidence bases, or weak institutional accountability. The review proposes a multilevel governance approach combining technical evaluation, documentation, professional and community oversight, appeals, remedies, and public-interest knowledge infrastructure. Its distinctive contribution is to connect three observations usually kept apart—how bias is measured, how generative systems concentrate epistemic authority, and how statistical learning is oriented toward past data—and to show why democratic and feminist governance can keep alternative technological futures open. Full article

Artificial intelligence (AI) is increasingly integrated into healthcare education, clinical decision-making, and future practice. For medical students, AI literacy entails technical understanding, practical competence, ethical awareness, value-based judgment, and responsible engagement. This study examines how culturally embedded value orientations are associated with Chinese medical students’ perceived AI literacy, as assessed using a self-report instrument. In a cross-sectional sample of 1500 medical students enrolled at a comprehensive university in Henan Province, China, AI literacy was assessed using the 12-item Artificial Intelligence Literacy Scale (AILS), a self-report measure whose scores represented perceived AI literacy. Value orientations were measured using the 32-item Chinese Values Questionnaire (CVQ), comprising eight value dimensions. NCA and fsQCA were conducted to examine necessary conditions and configurational associations with membership in the high perceived AI literacy set. No single value dimension met the criterion for set-theoretic necessity with respect to membership in the high self-reported AI literacy set, and no individual condition met the fsQCA necessity consistency threshold of 0.90. Four sufficient configurations associated with high perceived AI literacy were identified, with an overall solution consistency of 0.867 and coverage of 0.383. Moral Self-Discipline and Public Interest repeatedly appeared as core or peripheral conditions. These results suggest that high perceived AI literacy was associated with multiple combinations of value orientations rather than with a single value dimension. High perceived AI literacy was associated with multiple value configurations rather than one dominant value orientation. Empirically, this study applies configurational analysis to understand how value orientations are associated with AI literacy, complementing existing research on knowledge, attitudes, and readiness. These context-bound associations may inform future research on whether medical AI curricula can integrate technical training with ethical reflection and public-oriented professional values. Full article

The rapid diffusion of generative artificial intelligence (GenAI) tools such as ChatGPT has unsettled established academic practices related to assessment, authorship, integrity, and disciplinary knowledge production. This qualitative repeated cross-sectional study examines how faculty and staff at a large public research university understood these changes in 2023 and 2024. The analysis draws on responses to the same open-ended survey question collected from independent respondent groups in 2023 (n = 104) and 2024 (n = 313). Responses were analyzed inductively through thematic analysis and subsequently interpreted using Disruptive Innovation Theory and Complex Adaptive Systems Theory. The analysis identified both continuity and change across the two datasets. Responses in 2023 emphasized uncertainty, threats to academic integrity, and defensive assessment redesign. Responses in 2024 more frequently described pedagogical experimentation, process-oriented assessment, and AI literacy as emerging academic and professional competencies. Concerns about authorship, equity, reliability, and inconsistent institutional guidance persisted across both years. The findings suggest that faculty and staff discourse shifted from primarily containing GenAI-related risks toward selectively integrating the technology into teaching and professional practice. However, because the study used independent cross-sectional samples, it does not establish individual change over time. The study contributes a theoretically informed account of institutional sensemaking during the first two years following ChatGPT’s public release and identifies strategies for balancing innovation, integrity, equity, and the human purposes of higher education. Full article

Artificial intelligence (AI) is transforming organisations by accelerating decision cycles, increasing informational density, and expanding behavioural visibility. While these technologies enhance analytical capability, they do not resolve a persistent leadership challenge: the knowing–doing gap between leaders’ intentions and their enacted behaviour under pressure. This article introduces the Root–Reset–Rise framework, explaining how leaders sustain behavioural alignment and credibility in AI-augmented environments where technological acceleration intensifies integration strain. The article develops a conceptual framework through integrative theory synthesis drawing on identity theory, behavioural integrity, self-regulation, emotional regulation, recovery science, and institutional theory to explain why leaders struggle to translate knowledge into consistent action and how technological acceleration amplifies this challenge. It argues that AI does not correct leadership weakness; it amplifies the conditions under which behavioural misalignment occurs. As decision tempo and informational inputs increase, leaders experience greater cognitive load and integration strain. The Root–Reset–Rise framework explains how leadership stability can be sustained through three reinforcing capabilities: Root, anchoring leadership identity through values, habits, and emotional stability; Reset, interrupting behavioural drift through recalibration practices such as reflection and recovery; and Rise, institutionalising credibility through modelling, norm formation, and structural reinforcement. By positioning AI as a structural amplifier of the knowing–doing gap, the model provides a novel conceptual explanation of leadership effectiveness in AI-accelerated organisations. Full article

Autonomous vehicles increasingly require capabilities that extend beyond perception towards contextual understanding, semantic reasoning, and explainable decision-making. Knowledge graphs (KGs) have emerged as a promising solution by integrating heterogeneous sensor data, traffic regulations, domain knowledge, and contextual information into unified semantic frameworks. This review systematically examines knowledge graph-based intelligent reasoning in autonomous driving through a PRISMA 2020-guided analysis of 47 peer-reviewed studies identified from the literature published from 1 January 2018 to 31 January 2026. The findings reveal that semantic scene understanding and ontology-based representations currently dominate the field, with 66.0% of studies integrating knowledge graphs with deep learning approaches. Neuro-symbolic methods and explainable AI components were identified in 38.3% and 34.0% of publications, respectively, indicating increasing research interest in hybrid and transparent AI architectures. The analysis further demonstrates that 80.9% of studies remain limited to benchmark datasets and simulation environments, whereas only 19.1% provide real-world validation, suggesting relatively low technological maturity and limited industrial readiness. Although KG-enabled approaches substantially improve contextual awareness, hidden hazard anticipation, and explainability compared with conventional perception-centric architectures, major challenges remain regarding scalability, ontology interoperability, semantic error propagation, real-time reasoning, and certification requirements. The review identifies the convergence of knowledge graphs, large language models, and neuro-symbolic AI as a promising direction for next-generation autonomous driving systems. Future research should therefore focus on uncertainty-aware reasoning, adaptive explainability, standardised evaluation methodologies, and certification-oriented real-world deployment strategies. Full article

Artificial intelligence has emerged as a promising paradigm for advancing the screening, diagnosis support, and monitoring of autism spectrum disorder (ASD) through scalable and data-driven clinical augmentation. Recent advances in machine learning, multimodal sensing, and digital phenotyping have enabled AI systems to analyze behavioral, neurophysiological, speech, and clinical data to identify early markers of ASD. Despite encouraging experimental results, major barriers to clinical translation remain, including limited generalizability, fragmented datasets, insufficient evaluation rigor, lack of semantic interoperability, and unresolved ethical and regulatory concerns. This systematic review provides a comprehensive technical review of AI for ASD, covering data modalities, feature engineering, learning paradigms, evaluation protocols, deployment architectures, and knowledge representation frameworks. Particular emphasis is placed on system-level and translational considerations, including cloud–edge infrastructures, explainable clinical decision-support systems, privacy-aware deployment, and ontology-driven reasoning. Beyond summarizing existing work, this paper critically analyzes challenges related to reproducibility, dataset bias, interpretability, and clinical integration and derives design requirements for next-generation trustworthy ASD AI systems. We argue that meaningful clinical impact will require the integration of multimodal learning, semantic knowledge representation, explainable reasoning, and human-in-the-loop decision processes to support safe, interpretable, and clinically deployable AI systems in pediatric healthcare environments. Full article

While artificial intelligence (AI) is expected to drive paradigm-shifting transformations, many initiatives result in merely incremental optimization. Anchored in strategic human capital theory, this study shifts the analytical focus from the scale of elite technical talent, conceptualized as AI stars, to the configuration of their knowledge structures to unpack this paradox. Using a dataset of 1270 medical AI patents from corporate R&D teams, we employed high-dimensional fixed-effects models to examine these dynamics. The results reveal that while the knowledge diversity of AI stars acts as a potent engine for breakthrough innovation, this generative capacity is attenuated by excessive domain familiarity. Specifically, direct domain familiarity (derived from internal experience) and indirect domain familiarity (absorbed through external collaborative networks) negatively moderate this relationship, a dynamic theorized to operate through internal cognitive entrenchment and external relational conformity, respectively. Extending the efficiency-driven consensus regarding bilingual expertise, these findings demonstrate that excessive domain embeddedness transforms from an informational bridge into a restrictive constraint during paradigm-shifting innovations, particularly within highly institutionalized environments. Full article

Background: Emergency departments (EDs) operate under severe time pressure, diagnostic uncertainty, and resource constraints, making the consistent application of clinical protocols challenging. This scoping review aimed to map the barriers to protocol adherence and the strategies reported to support implementation in emergency care. Methods: PubMed/MEDLINE, Scopus, and Web of Science were searched for publications from January 2000 to June 2026. Following predefined eligibility criteria, 58 publications were included and charted according to clinician-related, guideline-related, patient-related, and organizational determinants, and we reported the implementation strategies. No formal design-specific risk-of-bias or certainty-of-evidence assessment was performed; therefore, the synthesis was intended to map the available evidence rather than establish the comparative effectiveness. Results: Commonly reported barriers included limited guideline knowledge and clinical experience, cognitive overload and occupational fatigue, poor guideline usability and workflow compatibility, patient communication difficulties and clinical complexity, overcrowding, staffing shortages, and limited organizational support. The reported strategies included education and simulation, audit and feedback, clinical decision support, workflow redesign, multidisciplinary collaboration, and leadership engagement. However, the heterogeneity in study designs, clinical settings, definitions of adherence, and reported outcomes precluded ranking these strategies or determining whether particular combinations were superior. Conclusions: Protocol adherence in EDs appears to be shaped by interacting clinician-related, guideline-related, patient-related, and organizational factors. The identified strategies may support implementation, but their relative and comparative effectiveness remains uncertain. Digital health and artificial intelligence should be considered priorities for prospective evaluation rather than established solutions. Full article

Sustainability education has produced four major traditions, Environmental Education, Climate Education, Education for Sustainable Development, and Climate Justice Education, that have developed largely in parallel. This fragmentation prevents cross-tradition comparison and leaves the most transformative dimensions of the field systematically underdeveloped. This article proposes a four-dimensional Taxonomy of Sustainability Education Design, organized around Educational Approach (D1), Cognitive Depth (D2), Disciplinary Integration (D3), and Real-World Connection (D4), for diagnosing dimensional gaps across educational programs and traditions. The taxonomy was constructed inductively from a critical literature review and interpreted through Edgar Morin’s philosophy of complex thought, whose seven principles explain why dimensional underdevelopment is paradigmatic rather than merely programmatic. The framework is illustrated through two contrasting cases: the UNESCO Associated Schools Network and the Brazilian National Environmental Education program. The mapping suggests a structural asymmetry: the dimensions most consistently underdeveloped, D1 Criticality and Advocacy, D3.5 Community and Indigenous Knowledge, and D4 Systemic and Political, are those most closely associated with transformative and epistemically plural education. The normative and policy implications of these gaps are examined in relation to the geopolitics of epistemological authority, climate-displaced communities, the historical production of colonial vulnerability, and the risk of epistemicide at algorithmic scale in AI-mediated education. Full article

This study leverages both Kolb’s experiential learning theory and technological pedagogical content knowledge (TPACK) to explore elementary school pre-service teachers’ experiences using generative artificial intelligence (GenAI) to provide ideas to differentiate instruction. Findings from this exploratory study indicate that pre-service teachers (PSTs) found GenAI tools helpful and aligned to research-based approaches taught in the course. Additionally, PSTs who had more robust background knowledge made more connections to differentiating mathematics strategies with reference to a progression of supports from concrete to pictorial to abstract activities. Implications from this study include the need for teacher educators to thoughtfully consider the use of GenAI and for scholars to systematically examine the potential benefits and drawbacks of these tools. Full article

Accurate air quality prediction is essential for environmental monitoring and public health protection. Among atmospheric pollutants, ground-level ozone remains particularly difficult to predict because of the complex and nonlinear interactions governing its formation. Although recent advances have achieved promising predictive performance using machine learning and deep learning, most existing approaches rely on black-box models whose explanations are provided only through post hoc explainability techniques. This work presents an alternative symbolic artificial intelligence framework based on association rule mining for intrinsically explainable ozone prediction. Hourly atmospheric observations collected from ground-level monitoring stations in the Hauts-de-France region (France) are preprocessed through cleaning, discretization, and class balancing before rule extraction. Two complementary symbolic AI approaches, Formal Concept Analysis (FCA) and a Genetic Algorithm (GA), are employed to automatically discover human-readable association rules linking meteorological and atmospheric variables to ozone concentration classes. The extracted rules provide transparent and directly interpretable decision mechanisms that can be readily validated by air quality experts. The experimental results show that both rule-mining approaches produce substantially more precise rule sets than decision trees, with average rule precisions of 0.76 for FCA and 0.79 for GA. Furthermore, the resulting rule-based classifier achieves an accuracy of approximately 0.79, outperforming the evaluated machine learning baselines while preserving intrinsic interpretability. These results demonstrate that symbolic AI constitutes a promising alternative for trustworthy air quality prediction and knowledge discovery. Full article

Project management plays a critical role in ensuring project success and organizational competitiveness, particularly in Saudi Arabia’s rapidly evolving construction sector. Artificial intelligence (AI) has emerged as a transformative technology with the potential to improve project performance through enhanced operational processes and managerial decision-making. This study examines the impact of AI integration on construction project outcomes (PO), with operational efficiency (OE) and managerial decision-making (MDM) acting as mediating variables. A quantitative research approach was adopted, and data were collected from 216 professionals working in Saudi Arabia’s private construction sector. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings supported all proposed hypotheses, demonstrating that AI integration has a significant positive effect on PO. Furthermore, OE and MDM were found to significantly mediate the relationship between AI integration and PO. The structural model explained 65% of the variance in PO, indicating substantial explanatory power. The findings contribute to the growing body of knowledge on AI adoption in construction project management and provide practical insights for organizations seeking to enhance PO through AI-driven digital transformation initiatives in support of Saudi Vision 2030. Full article

With the acceleration of global urbanization and the large-scale development of underground spaces, underground engineering faces extremely complex and variable geological environments and high-risk construction disturbances. The widespread application of the Internet of Things and novel sensing technologies has given rise to structural health monitoring data increasingly characterized by massive volume, high dimensionality, multi-source heterogeneity, and strong spatiotemporal coupling. Traditional data processing methods based on mechanical analysis, empirical formulas, or numerical simulation have increasingly exposed limitations of insufficient accuracy, lengthy computation times, and weak generalization capability when confronted with such engineering big data. Machine learning and deep learning technologies, by virtue of their superior nonlinear mapping capability, advantages in feature extraction from massive data, and flexible architectural design, provide solutions for efficient knowledge extraction and intelligent assessment of underground engineering monitoring data. This paper reviews the current application status and frontier advances of machine learning technologies in the field of underground engineering monitoring data processing in recent years. First, the development trajectory of analytical algorithms evolving from classical shallow machine learning, through temporal and spatial deep learning, to physics-data dual-driven approaches is delineated. Second, targeting the critical challenges of missing field data and sparse sensor deployment, spatiotemporal fusion imputation techniques and spatial reconstruction methods incorporating mechanical prior knowledge are thoroughly evaluated, elucidating the paradigm shift in monitoring philosophy from discrete point-based alarming to inference-augmented sparse sensing that approximates full-field state awareness through model-dependent estimation rather than direct measurement. Third, the applications of machine learning in underground structural deformation mechanism interpretation, key influencing factor identification based on explainable artificial intelligence (AI), and rapid back-analysis of geomechanical parameters are summarized. Finally, composite network architectures and physics-constrained guidance strategies for non-stationary deformation time series prediction under complex and variable working conditions are discussed. A methodological audit of the 73 included studies—of which 33 enter the quantitative comparison tables—reveals that 26 of the 33 audited studies (78.8%) validate exclusively on single-project data, only 1 study conducts rigorous out-of-distribution generalization testing, and none of the 33 studies (0%) provides uncertainty quantification. These findings highlight cross-project generalization and probabilistic prediction as important methodological challenges. This paper aims to provide theoretical references and methodological guidance for safety early warning, intelligent construction, and full life-cycle health management of underground engineering. Full article

The exponential growth of the Internet of Things (IoT) has generated massive data streams traditionally processed by centralized cloud architectures, which increasingly face latency, bandwidth, and privacy limitations. Shifting artificial intelligence to resource-constrained edge nodes, known as TinyML, offers a robust decentralized alternative, though it introduces severe memory, compute, and energy bottlenecks. To map this transition, a systematic literature review was conducted following PRISMA guidelines, analyzing peer-reviewed studies published between 2021 and 2026 across major databases. The analysis identifies primary architectural paradigms and evaluates the efficacy of state-of-the-art model compression techniques, such as quantization, pruning, and knowledge distillation. Furthermore, the findings reveal that hardware–software co-design and custom neural accelerators are crucial for overcoming operational bottlenecks, while also highlighting persistent security and privacy challenges in on-device learning. Ultimately, while deploying complex models on microcontrollers is increasingly viable, achieving optimal performance demands holistic optimization strategies. This review synthesizes current research gaps and provides a strategic roadmap to guide future interdisciplinary efforts toward resilient, energy-efficient, and secure next-generation intelligent edge systems. Full article