Search Results (528)

Background: Artificial intelligence (AI) is increasingly integrated into healthcare systems, with important implications for nursing practice and clinical workflows. However, evidence regarding nurses’ digital skills, perceptions, and readiness to adopt AI-enabled technologies remains limited, particularly in national healthcare contexts such as Greece. Objectives: This study examined nurses’ digital skills, perceptions of AI, and readiness for AI adoption in clinical practice, and explored demographic and professional factors associated with these outcomes. Methods: A cross-sectional survey was conducted among 166 nurses working in two public hospitals in Greece. Results: Nurses reported moderate digital skills, with 59.1% indicating competence in email/video communication and 27.2% reporting adequate use of digital security tools, while exposure to AI remained limited (18.0% reported using AI products/services in daily life). Perceived professional impact of AI was moderate, whereas readiness for AI adoption was comparatively lower, with only 7.8% considering health professionals adequately prepared and 7.2% reporting adequate AI training. Statistical analyses indicated that educational level and computer literacy certification were positively associated with digital skills, whereas longer professional experience was negatively associated with readiness for AI adoption. Conclusions: These findings highlight a gap between general digital competence and preparedness for AI-driven healthcare applications and underline the need for targeted education and implementation strategies to support effective and ethical integration of AI in nursing practice. From a nursing workforce perspective, the results underscore the importance of integrating AI literacy into continuing professional education and aligning digital health implementation strategies with clinical nursing practice. Full article

Background: Digitalization and artificial intelligence-supported clinical decision support systems (AI-DSS), defined here as tools that generate patient-specific alerts, risk estimates, prioritization prompts, documentation suggestions, or related recommendation outputs intended to support rather than replace professional nursing judgment, can improve clinical decision-making, yet they may also amplify technostress and burnout, with downstream effects on missed nursing care and implementation readiness. Methods: We surveyed 239 registered nurses from a tertiary-care hospital in Timișoara, Romania (January–March 2025), including critical care (n = 60) and general wards (n = 179). Measures included a 15-item technostress scale, eHEALS, Maslach Burnout Inventory–Human Services Survey (MBI-HSS), Safety Attitudes Questionnaire (SAQ) teamwork and safety climate subscales, a 10-item missed nursing care inventory, and a six-item AI-DSS acceptance scale reflecting perceived usefulness, trust, and stated willingness to use such tools if available as an attitudinal readiness outcome rather than as routine observed use. Multivariable regression, exploratory mediation models, cluster analysis, and exploratory ROC analysis were performed. Results: Higher technostress was associated with higher emotional exhaustion (r = 0.52) and more missed care (r = 0.41), whereas eHealth literacy correlated with higher AI-DSS acceptance (r = 0.35) and lower technostress (r = −0.34). In adjusted models, technostress (per 10 points) was associated with higher missed care (β = 0.28, p < 0.001) (equivalent to 0.14 points per 5-point increase) and higher odds of low AI-DSS acceptance (OR = 1.38, p = 0.001), while eHealth literacy was associated with lower odds of low acceptance (OR = 0.71 per 5 points, p < 0.001). Burnout and the safety climate statistically accounted for approximately 35% of the technostress–missed care association. Three workflow phenotypes were identified, with the high-strain/low-literacy cluster showing the most missed care (3.5 ± 1.8) and the lowest AI acceptance (19.7 ± 5.2). An exploratory in-sample ROC model for intention to leave achieved an AUC of 0.82. Conclusions: Higher technostress clustered with worse nurse well-being, more care omissions, and lower AI-DSS acceptance, whereas eHealth literacy appeared protective. Interventions combining digital skills support, usability-focused redesign, and a stronger safety climate may reduce missed care and support safer AI implementation. Full article

The rapid diffusion of generative artificial intelligence (GenAI) is reshaping organisational systems and digital transformation strategies, yet it remains unclear which organisational conditions are associated with long-term competitive performance in business-to-business (B2B) contexts. This study adopts a systems-informed perspective and examines how ethical governance, environmental dynamism, exploratory and exploitative innovation, and GenAI adoption are associated with long-term competitive performance in B2B firms. Using survey data from 104 Portuguese B2B managers and Partial Least Squares Structural Equation Modelling (PLS-SEM), the findings show that ethical governance is the strongest organisational correlate of long-term competitive performance, underscoring the central role of governance structures in responsible GenAI use. GenAI adoption is positively associated with performance, but its role is complementary rather than dominant. Exploratory innovation does not show a significant direct association with performance; instead, its association with performance operates through GenAI adoption in the estimated model, suggesting that experimentation becomes more performance-relevant when translated into digitally enabled routines. In contrast, exploitative innovation is directly associated with performance through incremental efficiency mechanisms. These findings challenge technology-deterministic assumptions and suggest that long-term competitive performance in B2B firms is more closely associated with the organisational alignment of governance structures, innovation capabilities, and GenAI adoption than with technology adoption alone. Full article

The evolving digital landscape, including artificial intelligence (AI) and its generative forms, is changing how younger generations learn. As students utilize generative AI systems, they cultivate trust in such technology to support their current and long-term learning. The objective of this study was to investigate the relationship between generative AI use among students in health professions and their trust in this technology to support their lifelong learning as future health professionals. This study employed a survey methodology using a cross-sectional study design. The survey included sociodemographic variables and questions regarding students’ generative AI use and their trust in this technology to support their lifelong learning. Descriptive and inferential statistical procedures were used to analyze the data. A total of 558 students representing various health professions responded to the survey. In the regression analysis, after controlling for student’s sex and location variables, greater generative AI use was associated with students’ increased trust in this technology to support their lifelong learning (beta = 0.58, p < 0.001), explaining close to 40% of the total variance. Given the rapidly evolving digital landscape, this finding warrants further study, with implications for training of the future health workforce. Full article

Artificial intelligence (AI) is increasingly being adopted for decision support in sustainable building risk management, yet the trustworthiness of AI-supported sustainability risk decisions depends as much on data quality as on analytical capability. Poor data conditions can amplify sustainability risks by producing unreliable decision support, yet existing studies provide limited insights into which data quality dimensions should be prioritized to enable trustworthy AI outcomes. This study identifies and prioritizes the critical data quality dimensions for trustworthy AI-supported decisions in sustainable building risk management. A questionnaire survey was conducted of accredited sustainable building professionals and their expert judgements were analyzed through an Analytic Hierarchy Process (AHP). The findings reveal that system-dependent dimensions, particularly traceability and interoperability, are prioritized over intrinsic dimensions like accuracy and consistency. The findings suggest that trustworthy AI-supported sustainability decisions depend strongly on a verifiable data provenance, cross-system integration and interpretable outputs rather than data correctness alone. This study reframes data quality from a general prerequisite to a prioritized, context-sensitive construct underpinning trustworthy AI applications, extending data-driven decision theory in the sustainable building domain. Ultimately, a phased data governance approach is recommended to prioritize traceability and interoperability as the foundational conditions for construction organizations implementing trustworthy AI in sustainable building risk management. Full article

Introduction: Chronic obstructive pulmonary disease (COPD) is associated with substantial symptom burden and functional limitations, which may co-occur with psychological distress. This pilot study aimed to assess depressive symptoms, anxiety, insomnia, and sexual quality of life in patients with COPD living in the Podlaskie Voivodeship. Materials and Methods: This cross-sectional pilot study included 47 patients with COPD, including outpatients (n = 11) and inpatients (n = 36), recruited at the University Teaching Hospital in Bialystok between February and August 2025. The original survey questionnaire, Beck Depression Inventory (BDI), Hamilton Anxiety Rating Scale (HAM-A), Generalized Anxiety Disorder-7 (GAD-7), Athens Insomnia Scale (AIS), Insomnia Severity Index (ISI), and Sexual Quality of Life (SQoL) questionnaires were used. Results: In the study sample, median scores indicated a considerable burden of depressive symptoms (BDI Me = 16), anxiety (HAM-A Me = 27; GAD-7 Me = 15), and insomnia (AIS Me = 9; ISI Me = 14), alongside reduced sexual quality of life (SQoL Me = 46). Age in the total sample correlated positively with depressive symptoms, anxiety, and sleep difficulties, and negatively with SQoL; however, these relationships were not consistently maintained in age-stratified analyses. Crude inpatient–outpatient differences were substantial, but supplementary adjusted models showed that subjective symptom severity was the most consistent predictor across outcomes, whereas the independent role of hospitalization status was attenuated. Strong associations were observed between depression, anxiety, insomnia, and sexual quality of life. Conclusions: This pilot study indicates a substantial within-sample psychological burden in patients with COPD and suggests that these outcomes are closely associated with subjective symptom burden. Given the small sample size, marked group imbalance, cross-sectional design, and lack of objective COPD severity measures, the findings should be interpreted as exploratory and require confirmation in larger multicenter studies. Full article

Artificial intelligence (AI) is increasingly transforming architectural education, shifting design studios toward human–AI collaborative workflows. This study investigates the impact of AI integration across the six stages of the architectural design process: pre-design, conceptual design, schematic design, design development, documentation, and presentation. A mixed-methods approach was adopted, combining survey data from 17 master’s degree students with reflective insights from eight faculty members involved in hybrid AI-supported studio environments. AI’s influence was evaluated using six indicators: efficiency, creativity enhancement, accuracy, interdisciplinary integration, adoptability, and environmental or architectural impact. The findings indicate that AI is most effective during early design stages, where it supports idea generation, visualization, and rapid iteration. Its impact becomes less pronounced in later technical phases, where human expertise and critical reasoning remain essential. Students perceived AI as a creative catalyst and productivity enhancer, while faculty emphasized its analytical and evaluative potential in supporting informed decision-making. Overall, AI functions most effectively as a complementary partner rather than a replacement for human agency. The study proposes a structured framework to guide ethical and pedagogically sound AI integration within architectural design studios. Full article

The increasing integration of Generative AI (GenAI) into higher education, particularly in the domain of language teaching, presents both opportunities and challenges. While AI-powered tools such as ChatGPT-5 can support language learning by generating personalised content which enables real-time interaction and feedback, they also risk perpetuating biases embedded in training data. These biases can appear in linguistic, cultural or socio-political forms, reinforcing stereotypes and influencing language norms. Therefore, equipping students and educators with strategies to critically assess AI outputs is essential for ethical and responsible AI use in language education. While recent research highlights the risks of algorithmic bias, less attention has been given to the perceptions and attitudes of pre-service teachers, whose future practice will shape classroom uses of these technologies. This exploratory pilot study adopts a survey-based approach to examine pre-service teachers’ baseline awareness of bias in artificial intelligence, with particular attention to linguistic and cultural dimensions Data were collected through an online questionnaire administered to 65 undergraduate students enrolled in Primary Education degree programmes. The study documents baseline perceptions prior to any instructional intervention and provides preliminary empirical evidence to inform the future design of pedagogical strategies aimed at developing critical AI literacy in teacher education. Full article

The transition to AI-driven Cyber–Physical Systems has fundamentally reshaped transportation, introducing systemic risks that transcend traditional physical boundaries. Unlike prior reviews focused on isolated technological domains, this survey proposes a novel “End-to-End” analytical framework that models the causal propagation of vulnerabilities from physical sensing hardware to human cognitive responses. Synthesizing 140 research contributions (2017–2025), we evaluate the paradigm shift from deterministic control to Generative AI and Large Language Models (Transportation 5.0). To substantiate our framework, we introduce a structured cross-layer threat matrix and mathematically formalize the technology–cognition cascade, explicitly mapping how physical layer perturbations, such as optical jamming, bypass digital edge security to trigger hazardous behavioral reactions in human drivers. We conclude that ensuring the resilience of next-generation infrastructure requires a unified analytical architecture that formally bounds hardware constraints, algorithmic safety, and human trust. Full article

Background/Objectives: The digital transformation of dental practice is positioning artificial intelligence (AI) as a key tool for both clinical support and administrative optimization. While clinical uses of AI are well documented, there is limited evidence on managerial perspectives. This study explored how dental clinic managers view digital workflow transformation and AI implementation. Methods: A cross-sectional questionnaire-based study was conducted among 200 managers of dental clinics from urban and rural areas in Bucharest, Romania. The survey evaluated perceived difficulty and availability related to digitalization, current use of digital tools, demographic characteristics (age, professional experience, practice environment), and attitudinal dimensions reflecting digital pragmatism and efficiency versus human impac. Results: Managers demonstrated moderate digital pragmatism (median 2.84, IQR 2.29–3.44), embracing AI mainly when linked to efficiency, operational control, and economic sustainability. Lower perceived difficulty was associated with higher availability, current use of digital tools, younger age, and fewer years of managerial experience. Urban managers were more likely than rural managers to report higher availability and current use of digital tools, although this comparison should be interpreted cautiously given the small rural subgroup. Efficiency considerations outweighed human-impact concerns (median 3.9, IQR 3.46–4.2), yet caution persisted toward solutions requiring major organizational restructuring or potentially affecting clinician–patient interaction. This study is a pilot, exploratory investigation aimed at generating preliminary insights into the phenomenon of interest and refining the methodological approach and hypotheses for subsequent, larger-scale research. Conclusions: Dental clinic managers approach AI adoption through an efficiency-driven and risk-aware framework, favoring incremental digital integration over disruptive transformation. The results underline the need for context-sensitive implementation strategies, managerial training, and targeted support, to ensure that AI-enhanced workflows improve efficiency while preserving organizational stability and patient-centered care. Full article

Generative AI chatbots are becoming routine study companions in STEM, which raises a pedagogical question: what do students expect human lecturers to do differently when AI support is ubiquitous? This study examines STEM undergraduates’ expectations for a transformation of the lecturer role and their social ambivalence toward AI chatbots in Sino-foreign transnational education (TNE) programmes in China. We administered an online survey to 467 consenting undergraduates across four partnership institutions (three with sufficient subgroup sizes for institutional comparison). The survey instrument captured adoption readiness, perceived AI-enabled learning enhancement, expected changes to the lecturer role (multi-select), perceived social enhancement and social reduction mechanisms, and perceived support needs; it also asked an open-ended question, collecting 454 usable comments. We report descriptive statistics, χ² tests, Spearman correlations, and exploratory content analysis results. Students expected lecturers to shift from content delivery to facilitation: 52.7% anticipated that chatbots would handle routine questions, enabling more discussion and practical activities, and 49.7% expected greater emphasis on guiding deep thinking and problem solving. Perceived social impacts were strongly ambivalent: 92.2% endorsed at least one social enhancement and at least one social reduction mechanism, and enhancement and reduction indices were positively associated (ρ = 0.547, p < 0.001), a pattern that remained stable under alternative scoring and response-style trimming (ρ range = 0.526–0.590). Importantly, higher social ambivalence was linked to stronger expectations of lecturer governance and orchestration, including the curation of chatbot resources (42.5% vs. 9.7% in high vs. low ambivalence; χ²(1) = 44.12, p < 0.001) and accuracy checking (27.6% vs. 13.4%; χ²(1) = 8.82, p = 0.003). We therefore propose ambivalence literacy as a conceptual framework for responsible AI integration: a teachable capability to recognise and navigate simultaneous social benefits and risks of AI use, and to translate that recognition into concrete expectations for lecturer governance, orchestration, and facilitative teaching design in AI-integrated transnational STEM programmes. Full article

Artificial Intelligence (AI) is being increasingly used for equid health and welfare. This study aimed to establish consensus on where and how AI should be developed to achieve maximum benefit in this field. A workshop involving 41 stakeholders generated statements about current welfare concerns, areas for AI development, and barriers and solutions to AI use. Statements were circulated through Delphi surveys (acceptance set at 75% agreement). One-hundred-and-six statements reached agreement. Ethological needs not being met and poor equid management practices were key welfare concerns. Participants identified that insufficient owner/carer knowledge and understanding were important factors contributing to welfare concerns. Priority areas for AI development included assessment of equid wellbeing, as well as individual and population-level monitoring. Barriers included limited understanding of both equine behaviour and AI, biased, unethical, or insufficient data collection, difficulties developing accurate models, challenges to validation, and uncertainty around interpretation. Proposed solutions included development of evidence-based, unbiased AI systems, following best practice guidelines, requiring approval/regulation of AI tools, collaboration, and education of AI users. This is the first study to identify stakeholders’ opinions about where AI is likely to have the greatest benefit for equids, potential barriers, and solutions. The findings should be used to prioritise funding and development. Full article

Unmanned Aerial Vehicles (UAVs) are increasingly integrated into critical applications ranging from logistics and agriculture to defence and security operations, surveillance and emergency response. At the core of these systems lies the communication link between the UAV and its ground control station (GCS), which serves as the backbone for command, control and data exchange. However, communications links remain highly vulnerable to cyber-threats, including eavesdropping, signal falsification, radio frequency interference (RFI) and hijacking. These risks highlight the urgent need for secure communication protocols and effective defence mechanisms capable of protecting data confidentiality, integrity, availability and authentication. This study performs a comprehensive survey of secure UAV-GCS communication protocols and artificial intelligence (AI)-driven intrusion detection techniques. Initially, we review widely used communication protocols, examining their security features, vulnerabilities and existing countermeasures. Accordingly, a taxonomy of UAV-GCS security threats is proposed, structured around confidentiality, integrity, availability and authentication and map these threats to relevant attacks and defences. In parallel, our study examines state-of-the-art intrusion detection systems for UAVs, while particular emphasis is placed on emerging methods such as deep learning, federated learning, tiny machine learning and explainable AI, which hold promise for lightweight and real-time threat detection. The survey concludes by identifying open challenges, including resource constraints, lack of standardised secure protocols, scarcity of UAV-specific datasets and the evolving sophistication of attackers. Finally, we outline research directions for next-generation UAV architectures that integrate secure communication protocols with AI-based anomaly detection to achieve resilient and intelligent drone ecosystems. Full article

As artificial intelligence (AI) changes educational practices, understanding what sustains pre-service teachers’ generative AI use beyond initial adoption becomes important. However, existing research mainly focuses on initial acceptance rather than continuance intention, which is a more realistic indicator for sustainable technology integration. This study drew on an integrated framework including psychological (GAI anxiety, GAI self-efficacy), contextual (facilitating conditions, social influence), and perceptual factors (perceived ease of use, perceived usefulness) to examine pre-service teachers’ continuance intention toward GAI in future teaching. Survey data from 549 Chinese pre-service teachers were analyzed using structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA). Results showed that GAI self-efficacy had the strongest positive associations with both perceived ease of use and perceived usefulness. GAI anxiety negatively influenced both perceptions. However, facilitating conditions did not significantly relate to perceived usefulness. The fsQCA identified six configurational pathways clustered into the following three patterns: intrinsic value driven, efficacy capability driven, and external support driven. These findings suggest that teacher education programs should prioritize building GAI self-efficacy and supportive peer environments and not focus solely on infrastructure provision. Full article

Objectives: As artificial intelligence (AI) technologies become increasingly integrated into healthcare systems, understanding healthcare professionals’ psychological responses—particularly AI-related anxiety—has become increasingly important for the safe and effective implementation of these technologies in clinical practice. This study aimed to examine the relationships between oncology nurses’ readiness for artificial intelligence, their attitudes toward artificial intelligence, and their levels of AI-related anxiety. Design: A descriptive, cross-sectional study. Setting: An oncology hospital within a state hospital in Istanbul, Turkey. Participants: A total of 207 oncology nurses working full-time in clinical settings. Methods: Data were collected using an online survey consisting of a demographic information form, the Medical Artificial Intelligence Readiness Scale (MAIRS-MS), the Artificial Intelligence Anxiety Scale (AIAS), and the General Attitudes toward Artificial Intelligence Scale (GAAIS). Spearman correlation analysis, general linear modeling, and conditional mediation analysis were performed using JAMOVI (v2.6.17). A p-value of <0.05 was considered statistically significant. Results: AI-related anxiety was significantly and negatively correlated with both readiness and attitudes toward AI. General linear modeling showed that attitudes toward AI significantly predicted anxiety (β = −0.327, p < 0.001), whereas readiness did not have a direct significant effect. Conditional mediation analysis demonstrated that attitudes fully mediated the relationship between readiness and AI anxiety. The indirect effect of readiness on anxiety through attitudes was stronger among nurses who had received prior AI-related education. While the indirect effect remained significant among untrained nurses, its magnitude was considerably smaller. The total effect of readiness on anxiety was significant only in the untrained group, suggesting that structured education redirects the impact of readiness primarily through attitudes. Conclusions: Attitudes toward artificial intelligence represent the key psychological mechanism linking readiness to AI-related anxiety among oncology nurses. Prior AI education appears to strengthen this relationship by enhancing the association between readiness and attitudes and by being associated with lower anxiety levels. Educational and implementation strategies that emphasize ethical awareness and the development of positive, informed attitudes—rather than focusing solely on technical competence—are likely to be more effective in reducing anxiety and promoting the safe and ethical integration of AI into oncology nursing practice. Full article