Search Results (3,919)

Design education involves complex historical knowledge structures that often impose a high extraneous cognitive load on students. This study proposes and evaluates an intelligent instructional system that integrates Retrieval-Augmented Generation (RAG) with anthropomorphic digital humans to function as scalable cognitive scaffolding. We developed a locally deployed architecture utilizing the Qwen3-30B Large Language Model (LLM) for reasoning, BGE-Large-Zh for high-precision semantic embedding, and LiveTalking for real-time audiovisual generation. To validate the system’s pedagogical efficacy, a multi-center randomized controlled trial (RCT) was conducted across three universities ($N = 150$). The experimental group utilized the RAG-enhanced digital human system, while the control group received traditional instruction. Quantitative results demonstrate that the system significantly improved learning outcomes ($p<0.001, Cohen’s d=1.14$) and classroom engagement ($p<0.001, d=1.39$). Crucially, measurements using the Paas Mental Effort Rating Scale revealed a significant reduction in mental effort ($p<0.001, d=1.71$) for the experimental group. Instructional efficiency analysis (E) confirmed that the system successfully converted reduced extraneous load into germane learning gains (Experimental $E=+0.72$ vs. Control $E=-0.68$). These findings validate the technical feasibility and educational value of combining localized RAG architectures with embodied AI, offering a replicable framework for reducing cognitive load in intensive learning environments. Full article

Technological advances have influenced and changed cultural heritage in the galleries, libraries, archives, and museums (GLAM) sector by facilitating new forms of experimentation and knowledge exchange. In this context, this paper explores the evolving practice of archaeological facial depiction using AI-powered deepfake avatar creator software programs, such as Epic Games’ MetaHuman Creator (MHC), which offer new affordances in terms of agility, realism, and engagement, and build upon traditional workflows involving the physical sculpting or digital modelling of faces from the past. Through a case-based approach, we illustrate these affordances via real-world applications, including four-dimensional portraits, multi-platform presentations, Augmented Reality (AR), and enhanced audience interaction. We consider the limitations and challenges of these digital avatar systems, such as misrepresentation or cultural insensitivity, and we position this advanced technology within the broader context of digital heritage, considering both the technical possibilities and ethical concerns around synthetic representations of individuals from the past. Finally, we propose that the use of MHC is not a replacement for current practice, but rather an augmentation, expanding the potential for storytelling and public learning outcomes in the GLAM sector, as a result of increased efficiency and new forms of public engagement. Full article

The advent of Generative Artificial Intelligence (GenAI) has ushered in a transformative wave within the field of language education. However, the applications of GenAI are primarily in language teaching and learning, with assessment receiving much less attention. Drawing on task characteristics identified from a corpus of authentic prior tests, this study investigated the capacity of GenAI tools to develop a short College English Test-Band 4 (CET-4) listening test and examined the degree to which its content, concurrent, and face validity corresponded to those of an authentic, human-generated counterpart. The findings indicated that the GenAI-created test aligned well with the task characteristics of the target test domain, supporting its content validity, whereas sufficient robust evidence to substantiate its concurrent or face validity was limited. Overall, GenAI has demonstrated potential in developing listening tests; however, further optimization is needed to enhance their validity. Implications for language teaching, learning and assessment are therefore discussed. Full article

Objective: To analyse written submissions from individuals and families with lived experience of attention-deficit hyperactivity disorder (ADHD) to the 2023 Australian Senate Inquiry, using artificial intelligence (AI)-assisted thematic analysis. The aim was to identify priority concerns, service needs, and community-proposed solutions. Methods: A mixed-methods study of 505 publicly available submissions from individuals with ADHD and their families. Submissions were analysed using large language model (LLM)-assisted data extraction and thematic clustering, with human validation and review. Main Outcome Measures: Frequency and thematic distribution of (1) problems experienced; (2) services wanted; and (3) solutions suggested. Results: Thematic analysis of 480 eligible submissions revealed high costs and long wait times for assessment and treatment (each cited by 46%), lack of specialised care (39%), diagnostic delays (36%), and gender bias (27%). The most common service request was for affordable and accessible ADHD-specific care (71%), followed by services tailored to diverse populations and life stages. Proposed solutions focused on Medicare-funded access to psychological and psychiatric services (68%), expanded roles for general practitioners, improved provider training (39%), and recognition of ADHD under the National Disability Insurance Scheme. Submissions also highlighted misalignment between current clinical guidelines and public expectations. Conclusions: The findings highlight substantial unmet needs and systemic barriers in ADHD diagnosis and care in Australia. The AI-assisted analysis of consumer submissions offers a scalable method for integrating lived experience into policy development, providing numerical weighting to the individuals’ responses. Coordinated reforms in access, funding, and workforce training are needed to align services with both clinical evidence and community expectations. Full article

Artificial intelligence (AI) refers to autonomous or semi-autonomous systems capable of interpreting data, generating inferences, and guiding decisions, thereby reshaping the foundations of work and organizational processes. Its rapid integration into productive settings gives rise to emerging risks, understood as new or evolving hazards that stem from human–machine interaction, algorithmic decision-making, and shifting sociotechnical conditions. Within occupational safety and health (OSH), these risks encompass novel cognitive, psychosocial, organizational, and ethical challenges, making it necessary to develop preventive frameworks that align technological innovation with human well-being, transparency, and responsible governance. Full article

Risk management in large-scale construction projects is a critical yet complex process influenced by financial, safety, environmental, scheduling, and regulatory uncertainties. Effective risk management contributes directly to project optimization by minimizing disruptions, controlling costs, and enhancing decision-making efficiency. Early identification and mitigation of risks allow resources to be allocated where they have the greatest effect, thereby optimizing overall project outcomes. However, conventional methods such as expert judgment and probabilistic modeling often struggle to process extensive datasets and complex interdependencies among risk factors. This study explores the potential of an AI-based framework for risk identification, utilizing artificial intelligence to analyze project documentation and generate a preliminary set of identified risks. The proposed methodology is implemented on the ‘Trg pravde’ judicial infrastructure project in Zagreb, Croatia, applying AI models (GPT-5, Gemini 2.5, Sonnet 4.5) to identify phase-specific risks throughout the project lifecycle. The approach aims to improve the efficiency of risk identification, reduce human bias, and align with established project management methodologies such as PM². Initial findings suggest that the use of AI may broaden the range of identified risks and support more structured risk analysis, indicating its potential value as a complementary tool in risk management processes. However, human expertise remains crucial for prioritization, contextual interpretation, and mitigation. The study demonstrates that AI augments, rather than replaces, traditional risk management practices, enabling more proactive and data-driven decision-making in construction projects. Full article

In this digital era, many companies are integrating new solutions involving Artificial Intelligence (AI)-based automation systems to optimize processes, reach higher efficiency, and help them with decision-making. While implementing these changes, various challenges may arise, including resistance to AI integration from employees. This study examines how employees’ perceived benefits, concerns, and trust regarding AI-driven algorithmic decision-making influence their comfort with AI-driven algorithmic decision-making in the workplace. This study employed a quantitative method by surveying employees in the Gulf Cooperation Council (GCC) and Lebanon with a final sample size of 388 participants. The results demonstrate that employees are more likely to feel comfortable with AI-driven algorithmic decision-making in the workplace if they believe AI will increase efficiency, promote fairness, and decrease errors. Unexpectedly, employee concerns were positively associated with comfort, suggesting an adaptive response to AI adoption. Lastly, comfort with AI-driven algorithmic decision-making is positively correlated with greater levels of trust in AI systems. These findings provide actionable guidance to organizations, underscoring the need to communicate clearly about AI’s role, address employees’ concerns through transparency and human oversight, and invest in training and reskilling initiatives that build trust and foster responsible, employee-centered adoption of AI. Full article

This paper presents an Artificial Intelligence (AI)-driven framework for high-precision indoor localization using single-link Wi-Fi channel state information (CSI), targeting real-time deployment in complex multipath environments. To overcome challenges such as signal distortion and environmental dynamics, the proposed system integrates adaptive and unsupervised intelligence modules into the localization pipeline. A refined two-stage time-of-flight (TOF) estimation method is introduced, combining a minimum-norm algorithm with a probability-weighted refinement mechanism that improves ranging accuracy under non-line-of-sight (NLOS) conditions. Simultaneously, an adaptive parameter-tuned DBSCAN algorithm is applied to angle-of-arrival (AOA) sequences, enabling unsupervised spatio-temporal clustering for stable direction estimation without requiring prior labels or environmental calibration. These AI-enabled components allow the system to dynamically suppress multipath interference, eliminate positioning ambiguity, and maintain robustness across diverse indoor layouts. Comprehensive experiments conducted on the Widar2.0 dataset demonstrate that the proposed method achieves decimeter-level accuracy with an average localization error of 0.63 m, outperforming existing methods such as “Widar2.0” and “Dynamic-MUSIC” in both accuracy and efficiency. This intelligent and lightweight architecture is fully compatible with commodity Wi-Fi hardware and offers significant potential for real-time human tracking, smart building navigation, and other location-aware AI applications. Full article

In recent years, with the rapid advancement of science and technology, generative artificial intelligence has increasingly entered the public eye. Primarily through intelligent algorithms that simulate human logic and integrate vast amounts of network data, it provides designers with solutions that transcend traditional thinking, enhancing both design efficiency and quality. Compared to traditional design methods reliant on human experience, generative design possesses robust data processing capabilities and the ability to refine design proposals, significantly reducing preliminary design time. This study employs the CiteSpace visualization tool to systematically organize and conduct knowledge map analysis of research literature related to generative AI in architectural design within the Web of Science database from 2005 to 2025. Findings reveal the following: (1) International research exhibits a trend toward interdisciplinary convergence. In recent years, research in this field has grown rapidly across nations, with continuously increasing academic influence; (2) Research primarily focuses on technological applications within architectural design, aiming to drive innovation and development by providing superior, more efficient technical support; (3) Generative AI in architectural design has emerged as a prominent international research focus, reflecting a shift from isolated design to industry-wide integration; (4) Generative AI has become a core global architectural design topic, with future research advancing toward full-process intelligent collaboration. High-quality knowledge graphs tailored for the architecture industry should be constructed to overcome data silos. Concurrently, a multidimensional evaluation system for generative quality must be established to deepen the symbiotic design paradigm of human–machine collaboration. This significantly enhances efficiency while reducing the iterative nature of traditional methods. This study aims to provide empirical support for theoretical and practical advancements, offering crucial references for practitioners to identify business opportunities and policymakers to optimize relevant strategies. Full article

Artificial intelligence (AI) is now embedded across many aspects of healthcare, yet most implementations remain fragmented, task-specific, and layered onto legacy workflows. This paper does not review AI applications in healthcare per se; instead, it examines what an AI-first healthcare system would look like, one in which AI functions as a foundational organizing principle of care delivery rather than an adjunct technology. We synthesize evidence across ambulatory, inpatient, diagnostic, post-acute, and population health settings to assess where AI capabilities are sufficiently mature to support system-level integration and where critical gaps remain. Across domains, the literature demonstrates strong performance for narrowly defined tasks such as imaging interpretation, documentation support, predictive surveillance, and remote monitoring. However, evidence for longitudinal orchestration, cross-setting integration, and sustained impact on outcomes, costs, and equity remains limited. Key barriers include data fragmentation, workflow misalignment, algorithmic bias, insufficient governance, and lack of prospective, multi-site evaluations. We argue that advancing toward AI-first healthcare requires shifting evaluation from accuracy-centric metrics to system-level outcomes, emphasizing human-enabled AI, interoperability, continuous learning, and equity-aware design. Using hypertension management and patient journey exemplars, we illustrate how AI-first systems can enable proactive risk stratification, coordinated intervention, and continuous support across the care continuum. We further outline architectural and governance requirements, including cloud-enabled infrastructure, interoperability, operational machine learning practices, and accountability frameworks—necessary to operationalize AI-first care safely and at scale, subject to prospective validation, regulatory oversight, and post-deployment surveillance. This review contributes a system-level framework for understanding AI-first healthcare, identifies priority research and implementation gaps, and offers practical considerations for clinicians, health systems, researchers, and policymakers. By reframing AI as infrastructure rather than isolated tools, the AI-first approach provides a pathway toward more proactive, coordinated, and equitable healthcare delivery while preserving the central role of human judgment and trust. Full article

Mass production in product design typically relies on standardized geometries and dimensions to accommodate a broad user population. However, when products are required to interface directly with the human body, such generalized design approaches often result in inadequate fit and reduced user comfort. This limitation highlights the necessity of fully personalized design methodologies based on individual anthropometric characteristics. This paper presents a novel application that automates the design of custom-fit sunglasses through the integration of Artificial Intelligence (AI) and Computational Design. The system is implemented using both textual (Python™ version 3.10.11) and visual (Grasshopper 3D™ version 1.0.0007) programming environments. The proposed workflow consists of the following four main stages: (a) acquisition of user facial images, (b) AI-based detection of facial landmarks, (c) three-dimensional reconstruction of facial features via an optimization process, and (d) generation of a personalized sunglass frame, exported as a three-dimensional model. The application demonstrates a robust performance across a diverse set of test images, consistently generating geometries that conformed closely to each user’s facial morphology. The accurate recognition of facial features enables the successful generation of customized sunglass frame designs. The system is further validated through the fabrication of a physical prototype using additive manufacturing, which confirms both the manufacturability and the fit of the final design. Overall, the results indicate that the combined use of AI-driven feature extraction and parametric Computational Design constitutes a powerful framework for the automated development of personalized wearable products. Full article

This study examines how Artificial Intelligence (AI) is reshaping the role of university teachers and transforming the foundations of academic work in the digital age. Building on the Dynamic Capabilities Theory (sensing–seizing–transforming), the article proposes a theoretical reframing of university teachers’ perceptions of AI. This approach allows us to bridge micro-level emotions with meso-level HR policies and macro-level sustainability goals (SDGs 4, 8, and 9). The empirical foundation includes a survey of 453 Ukrainian university teachers (2023–2025) and statistics, supplemented by a bibliometric analysis of 26,425 Scopus-indexed documents. The results indicate that teachers do not anticipate a large-scale replacement by AI within the next five years. However, their fear of losing control over AI technologies is stronger than the fear of job displacement. This divergence, interpreted through the lens of dynamic capabilities, reveals weak sensing signals regarding professional replacement but stronger signals requiring managerial seizing and institutional transformation. The bibliometric analysis further demonstrates a theoretical evolution of the university teacher’s role: from a technological adopter (2021–2022) to a mediator of ethics and integrity (2023–2024), and, finally, to a designer and architect of AI-enhanced learning environments (2025). The study contributes to theory by extending the application of Dynamic Capabilities Theory to higher education governance and by demonstrating that teachers’ perceptions of AI serve as indicators of institutional resilience. Based on Dynamic Capabilities Theory, the managerial recommendations are divided into three levels: government, institutional, and scientific-didactic (academic). Full article

Automata and artificial intelligence (AI) have long occupied a central place in cultural and artistic imagination, and the recent proliferation of AI-generated artworks has intensified debates about authorship, creativity, and human agency. Empirical studies show that audiences often perceive AI-generated works as less authentic or emotionally resonant than human creations, with authorship attribution strongly shaping esthetic judgments. Yet little attention has been paid to how AI systems themselves evaluate creative authorship. This study investigates how large language models (LLMs) evaluate literary quality under different framings of authorship—Human, AI, or Human+AI collaboration. Using a questionnaire-based experimental design, we prompted four instruction-tuned LLMs (ChatGPT 4, Gemini 2, Gemma 3, and LLaMA 3) to read and assess three short stories in Italian, originally generated by ChatGPT 4 in the narrative style of Roald Dahl. For each story × authorship condition × model combination, we collected 100 questionnaire completions, yielding 3600 responses in total. Across esthetic, literary, and inclusiveness dimensions, the stated authorship systematically conditioned model judgments: identical stories were consistently rated more favorably when framed as human-authored or human–AI co-authored than when labeled as AI-authored, revealing a robust negative bias toward AI authorship. Model-specific analyses further indicate distinctive evaluative profiles and inclusiveness thresholds across proprietary and open-source systems. Our findings extend research on attribution bias into the computational realm, showing that LLM-based evaluations reproduce human-like assumptions about creative agency and literary value. We publicly release all materials to facilitate transparency and future comparative work on AI-mediated literary evaluation. Full article

Non-digital traditional games such as board and card formats are increasingly recognised as valuable tools for active learning in higher education. These analogue approaches promote engagement, collaboration, and conceptual understanding through embodied and social interaction. This scoping review mapped research on the use of traditional, non-digital games as active learning strategies in tertiary education and examined whether the rise in generative artificial intelligence (GenAI) since 2022 has influenced their pedagogical role. Following the PRISMA-ScR framework, a systematic search of Scopus (October 2025) identified 2480 records; after screening, 26 studies met all inclusion criteria (explicitly using card and/or board games). Whilst this was a scoping, not a systematic review, some bias due to using only one database and evidence could have missed some studies. Results analysed the use and impacts of the games and whether AI was a specific driver in its use. Studies spanned STEM, business, health, and social sciences, with board and card games most frequently employed to support engagement, understanding, and collaboration. Most reported positive learning outcomes. Post-2023 publications suggest renewed interest in analogue pedagogies as authentic, human-centred responses to AI-mediated education. While none directly investigated GenAI, its emergence appears to have acted as an indirect catalyst, highlighting the continuing importance of tactile, cooperative learning experiences. Analogue games therefore remain a resilient, adaptable form of active learning that complements technological innovation and sustains the human dimensions of higher-education practice. Full article

Automated quality assurance is essential for low-dose computed tomography (LDCT) lung screening, yet manual checks strain clinical workflows. We present a fully automated artificial intelligence tool that quantifies scan coverage and image noise in LDCT without user input. Lungs and the aorta are segmented to measure cranial/caudal over- and underscanning, and noise is computed as the standard deviation of Hounsfield units (HUs) within descending aortic blood, normalized to a 1 ${\mathrm{mm}}^3$ voxel. Performance was verified in a reader study of 98 LDCT scans from the National Lung Screening Trial (NLST), and then applied to 38,834 NLST scans reconstructed with a standard kernel. In the reader study, lung masks were rated ≥“Nearly Perfect” in 90.8% and aorta-blood masks in 96.9% of cases. Across 38,834 scans, mean overscanning distances were 31.21 mm caudally and 14.54 mm cranially; underscanning occurred in 4.36% (caudal) and 0.89% (cranial). The tool enables objective, large-scale monitoring of LDCT quality—reducing routine manual workload through exception-based human oversight, flagging protocol deviations, and supporting cross-center benchmarking—and may facilitate dose optimization by reducing systematic over- and underscanning. Full article