Search Results (101)

Background and objectives: Evaluating Large Language Models (LLMs) presents two interrelated challenges: the general problem of assessing model performance across diverse tasks and the specific problem of using LLMs themselves as evaluators in pedagogical and educational contexts. Existing approaches often rely on single metrics or opaque preference-based methods, which fail to capture critical dimensions such as explanation quality, robustness, and argumentative diversity—attributes essential in instructional settings. This paper introduces PEARL, a novel framework conceived, operationalized, and evaluated in the present work using LLM-based scorers, designed to provide interpretable, reproducible, and pedagogically meaningful assessments across multiple performance dimensions. Methods: PEARL integrates three specialized rubrics—Technical, Argumentative, And Explanation-focused—covering aspects such as factual accuracy, clarity, completeness, originality, dialecticality, and explanatory usefulness. The framework defines seven complementary metrics: Rubric Win Count (RWC), Global Win Rate (GWR), Rubric Mean Advantage (RMA), Consistency Spread (CS), Win Confidence Score (WCS), Explanation Quality Index (EQI), and Dialectical Presence Rate (DPR). We evaluated PEARL by evaluating eight open-weight instruction-tuned LLMs across 51 prompts, with outputs scored independently by GPT-4 and LLaMA 3:instruct. This constitutes LLM-based evaluation, and observed alignment with the GPT-4 proxy is mixed across metrics. Results: Preference-based metrics (RMA, RWC, and GWR) show evidence of group separation, reported with bootstrap confidence intervals and interpreted as exploratory due to small samples, while robustness-oriented (CS and WCS) and reasoning-diversity (DPR) metrics capture complementary aspects of performance not reflected in global win rate. RMA and RWC exhibit statistically significant, FDR-controlled correlations with the GPT-4 proxy, and correlation mapping highlights the complementary and partially orthogonal nature of PEARL’s evaluation dimensions. Originality: PEARL is the first LLM evaluation framework to combine multi-rubric scoring, explanation-aware metrics, robustness analysis, and multi-LLM-evaluator analysis into a single, extensible system. Its multidimensional design supports both high-level benchmarking and targeted diagnostic assessment, offering a rigorous, transparent, and versatile methodology for researchers, developers, and educators working with LLMs in high-stakes and instructional contexts. Full article

Radical prostatectomy remains a cornerstone treatment for localized prostate cancer. While oncological control is essential, functional outcomes such as urinary continence and erectile function play a critical role in patient satisfaction and quality of life. Despite the growing emphasis on surgical quality, no standardized intraoperative scoring system has been universally adopted. This narrative review summarizes current approaches to evaluating the technical quality of radical prostatectomy and associated functional outcomes. It focuses on objective intraoperative assessment tools and functional evaluation scales used in clinical research and surgical education. A non-systematic literature search was conducted using the PubMed and Scopus databases to identify relevant intraoperative assessment tools (e.g., GEARS, PACE, and OSATS), functional scales (e.g., IIEF, EPIC, and pad test), and outcome reporting systems. Articles were reviewed for scale structure, clinical applicability, validation status, and limitations. Several tools have been developed to evaluate surgical skills in minimally invasive surgery, yet few are specific to radical prostatectomy. Most rely on subjective surgeon assessment or delayed functional outcomes, limiting their utility for intraoperative feedback. Video-based assessment is promising but underutilized. A gap remains for a prostatectomy-specific, reproducible, and real-time assessment scale. There is a pressing need for validated tools that bridge the gap between surgical technique and functional outcomes. Current methods lack specificity and reproducibility. Development of an objective, intraoperative scoring system may support surgeon feedback, quality improvement, and improved patient counseling. Full article

As the demand for high-quality visual experiences continues to grow, advanced imaging technologies offering higher realism and immersion are being increasingly integrated into various fields. Among them, glasses-free 3D imaging has gained significant attention for enhancing user experience without the need for wearable equipment. Holoscopic 3D imaging systems, known for their capability to reconstruct true volumetric images and provide natural depth perception, have emerged as a promising direction within this domain. Originating from early 20th-century optical theory, holoscopic imaging has evolved in response to diversified application scenarios and rapid advancements in micro-optics and computational imaging. This paper presents a representative historical overview of the development of holoscopic 3D systems, their unique features compared to other glasses-free 3D technologies, and their expanding presence in these applications. By analyzing representative use cases across sectors such as healthcare, education, cultural heritage, and media entertainment, this review offers a broader and more detailed perspective on the deployment of holoscopic 3D systems. Furthermore, this paper discusses current technical challenges and outlines future research directions, with a particular focus on the transformative potential of holoscopic 3D in the creative and entertainment industries. This study aims to provide both theoretical grounding and practical insights to support the next generation of holoscopic 3D imaging technologies. Full article

Extended reality (XR) technologies are gaining traction in technical education due to their potential for creating immersive and interactive training environments. This study presents the development and empirical evaluation of X-RAPT, a collaborative VR-based platform designed to train students in industrial robotics programming. The system enables multi-user interaction, cross-platform compatibility (VR and PC), and real-time data logging through a modular simulation framework. A pilot evaluation was conducted in a vocational training institute with 15 students performing progressively complex tasks in alternating roles using both VR and PC interfaces. Performance metrics were captured automatically from system logs, while post-task questionnaires assessed usability, comfort, and interaction quality. The findings indicate high user engagement and a distinct learning curve, evidenced by progressively shorter task completion times across levels of increasing complexity. Role-based differences were observed, with main users showing greater interaction frequency but both roles contributing meaningfully. Although hardware demands and institutional constraints limited the scale of the pilot, the findings support the platform’s potential for enhancing robotics education. Full article

This paper contributes conceptually and empirically to a more rigorous understanding of the role of intellectual capital in the sustainable development of the BRICS+ bloc. We investigate the growing command of technical competencies over social competencies across the entire knowledge process. A range of factors, including the ever-increasing tension between AI and humans, the multidimensional nature of intellectual capital, and a focus on competency-based approaches, shape the theory of a knowledge economy. This study presents a spatial modeling approach to analyze the sustainable development of economic systems, reevaluates the importance of intellectual capital in the era of Industry 4.0, introduces the concept of scientific management of intellectual capital by categorizing it into the AI, individual, and collective human mind, and enhances the methodology of managing the knowledge economy to foster intellectual capital development. The primary finding of the research is that the advancement of the knowledge economy is driving digital communication and network-based collaboration on a larger scale within the BRICS+ bloc. Policy implications are intricately linked to the necessity for the holistic development of intellectual capital, encompassing both human and artificial intelligence. This development requires enhancements in quality of life and living standards, advancements in education and healthcare, optimization of the labor market, and reinforcing its connection with the educational sector. Concurrently, it is vital to stimulate research and development (R&D), support the commercialization of high-tech innovations, and accelerate the process of robotization. These combined efforts are essential to fostering economic growth effectively. Full article

The rapid proliferation of Chat Generative Pre-trained Transformer (ChatGPT) marks a pivotal moment in artificial intelligence, eliciting responses from academic shock to industrial awe. As these technologies advance from passive tools toward proactive, agentic systems, their transformative potential and inherent risks are magnified globally. This paper presents a comprehensive, critical review of ChatGPT’s impact across five key domains: natural language understanding (NLU), content generation, knowledge discovery, education, and engineering. While ChatGPT demonstrates profound capabilities, significant challenges remain in factual accuracy, bias, and the inherent opacity of its reasoning—a core issue termed the “Black Box Conundrum”. To analyze these evolving dynamics and the implications of this shift toward autonomous agency, this review introduces a series of conceptual frameworks, each specifically designed to illuminate the complex interactions and trade-offs within these domains: the “Specialization vs. Generalization” tension in NLU; the “Quality–Scalability–Ethics Trilemma” in content creation; the “Pedagogical Adaptation Imperative” in education; and the emergence of “Human–LLM Cognitive Symbiosis” in engineering. The analysis reveals an urgent need for proactive adaptation across sectors. Educational paradigms must shift to cultivate higher-order cognitive skills, while professional practices (including practices within education sector) must evolve to treat AI as a cognitive partner, leveraging techniques like Retrieval-Augmented Generation (RAG) and sophisticated prompt engineering. Ultimately, this paper argues for an overarching “Ethical–Technical Co-evolution Imperative”, charting a forward-looking research agenda that intertwines technological innovation with vigorous ethical and methodological standards to ensure responsible AI development and integration. Ultimately, the analysis reveals that the challenges of factual accuracy, bias, and opacity are interconnected and acutely magnified by the emergence of agentic systems, demanding a unified, proactive approach to adaptation across all sectors. Full article

Artificial intelligence (AI) is revolutionarily shaping the entire landscape of medicine and particularly the privileged field of radiology, since it produces a significant amount of data, namely, images. Currently, AI implementation in radiology is continuously increasing, from automating image analysis to enhancing workflow management, and specifically, pediatric neuroradiology is emerging as an expanding frontier. Pediatric neuroradiology presents unique opportunities and challenges since neonates’ and small children’s brains are continuously developing, with age-specific changes in terms of anatomy, physiology, and disease presentation. By enhancing diagnostic accuracy, reducing reporting times, and enabling earlier intervention, AI has the potential to significantly impact clinical practice and patients’ quality of life and outcomes. For instance, AI reduces MRI and CT scanner time by employing advanced deep learning (DL) algorithms to accelerate image acquisition through compressed sensing and undersampling, and to enhance image reconstruction by denoising and super-resolving low-quality datasets, thereby producing diagnostic-quality images with significantly fewer data points and in a shorter timeframe. Furthermore, as healthcare systems become increasingly burdened by rising demands and limited radiology workforce capacity, AI offers a practical solution to support clinical decision-making, particularly in institutions where pediatric neuroradiology is limited. For example, the MELD (Multicenter Epilepsy Lesion Detection) algorithm is specifically designed to help radiologists find focal cortical dysplasias (FCDs), which are a common cause of drug-resistant epilepsy. It works by analyzing a patient’s MRI scan and comparing a wide range of features—such as cortical thickness and folding patterns—to a large database of scans from both healthy individuals and epilepsy patients. By identifying subtle deviations from normal brain anatomy, the MELD graph algorithm can highlight potential lesions that are often missed by the human eye, which is a critical step in identifying patients who could benefit from life-changing epilepsy surgery. On the other hand, the integration of AI into pediatric neuroradiology faces technical and ethical challenges, such as data scarcity and ethical and legal restrictions on pediatric data sharing, that complicate the development of robust and generalizable AI models. Moreover, many radiologists remain sceptical of AI’s interpretability and reliability, and there are also important medico-legal questions around responsibility and liability when AI systems are involved in clinical decision-making. Future promising perspectives to overcome these concerns are represented by federated learning and collaborative research and AI development, which require technological innovation and multidisciplinary collaboration between neuroradiologists, data scientists, ethicists, and pediatricians. The paper aims to address: (1) current applications of AI in pediatric neuroradiology; (2) current challenges and ethical considerations related to AI implementation in pediatric neuroradiology; and (3) future opportunities in the clinical and educational pediatric neuroradiology field. AI in pediatric neuroradiology is not meant to replace neuroradiologists, but to amplify human intellect and extend our capacity to diagnose, prognosticate, and treat with unprecedented precision and speed. Full article

In the context of the rapid digitalization of higher education, proactive artificial intelligence (AI) agents embedded within multi-agent systems (MAS) offer new opportunities for personalized learning, improved quality of education, and alignment with sustainable development goals. This study aims to analyze how such AI solutions are perceived by students at Narxoz University (Kazakhstan) prior to their practical implementation. The research focuses on four key aspects: the level of student trust in AI agents, perceived educational value, concerns related to privacy and autonomy, and individual readiness to use MAS tools. The article also explores how these solutions align with the Sustainable Development Goals—specifically SDG 4 (“Quality Education”) and SDG 8 (“Decent Work and Economic Growth”)—through the development of digital competencies and more equitable access to education. Methodologically, the study combines a bibliometric literature analysis, a theoretical review of pedagogical and technological MAS concepts, and a quantitative survey (n = 150) of students. The results reveal a high level of student interest in AI agents and a general readiness to use them, although this is tempered by moderate trust and significant ethical concerns. The findings suggest that the successful integration of AI into educational environments requires a strategic approach from university leadership, including change management, trust-building, and staff development. Thus, MAS technologies are viewed not only as technical innovations but also as managerial advancements that contribute to the creation of a sustainable, human-centered digital pedagogy. Full article

Handover of patient care is the most common form of communication across hospitals. Enabling effective handover has been identified as a key priority for patient safety. This Human Factors Ergonomics socio-technical systems study aims to understand the current system of handover within departments, across departments, and at the interface of provider services, and then use this knowledge to co-design recommendations to enable resilience in clinical handover. The Systems Engineering Initiative for Patient Safety 3.0 (SEIPS3.0) framework is used to take a systems approach to observing clinical handover. Over 26 h of handover, involving 218 healthcare professionals handing over patient care across an acute hospital setting and at the interface of two external ambulance service systems, was observed. From these observations of clinical handovers, we co-designed—with the input of 41 medical, nursing, health, and social care professionals, quality and safety professionals, and patient partners—70 recommendations for enabling resilience in handover using two socio-technical systems analysis frameworks: SEIPS3.0 and the Cube. These 70 recommendations were inductively coded, and ten emergent properties that can support resilience in handover were identified, including person-centred care, multi-disciplinary team working, culture, communication, evidence-based practice, operations management, education, digitally enabled care, evidence-based design, and understanding context. This study contributes important knowledge for healthcare professionals and Human Factors Ergonomics practitioners on the systemic enablers of resilience in clinical handover in acute hospital settings. Full article

The concept of governance has gained increasing attention across various fields of study. However, its application within the specific context of educational policies, particularly within compulsory public education, remains fragmented and underexplored. To answer the questions “How is governance conceptualized in the context of the compulsory public education system?” and “What contributions to future research emerge from this review?”, 32 peer-reviewed articles published in open-access journals between 2019 and 2023 were extracted from the Web of Science, Scopus, and ERIC databases and selected following PRISMA guidelines. Results from this systematic literature review analysis suggest a sustained yet moderate interest in the field, as evidenced by the reviewed publications, different theoretical and conceptual approaches, and research themes that illustrate different aspects of educational systems. Research gaps include the lack of a consolidated and integrated theoretical–conceptual framework on educational governance; the under-representation of specific actors, contexts, and points of view about how educational policies intentions are interpreted and enacted; insufficient critical analyses of, among others, educational leadership, digital transformation, and non-state actors’ influence in educational governance; and limited discussion of governance’s effects on educational justice, equity and quality. The main limitations relate to geographic, linguistic, and cultural biases of the analyzed studies, the exclusion of non-open-access articles, and the predominance of qualitative methodological approaches, which restrict generalizability. To address these challenges, future research should follow the adoption of interdisciplinary approaches, longitudinal and context-sensitive studies, and the use of mixed methodologies. These findings could contribute to a more informed discussion, avoiding reductionist interpretations and more open and critical perspectives on how educational governance transcends organizational and technical structures by incorporating political, ethical, and contextual dimensions that challenge the quality of educational systems. Full article

Addressing the issues of coarse emotional representation, low cross-modal alignment efficiency, and insufficient real-time response capabilities in current human–computer emotional language interaction, this paper proposes an affective interaction framework integrating BERT-based semantic understanding with VITS-based speech synthesis. The framework aims to enhance the naturalness, expressiveness, and response efficiency of human–computer emotional interaction. By introducing a modular layered design, a six-dimensional emotional space, a gated attention mechanism, and a dynamic model scheduling strategy, the system overcomes challenges such as limited emotional representation, modality misalignment, and high-latency responses. Experimental results demonstrate that the framework achieves superior performance in speech synthesis quality (MOS: 4.35), emotion recognition accuracy (91.6%), and response latency (<1.2 s), outperforming baseline models like Tacotron2 and FastSpeech2. Through model lightweighting, GPU parallel inference, and load balancing optimization, the system validates its robustness and generalizability across English and Chinese corpora in cross-linguistic tests. The modular architecture and dynamic scheduling ensure scalability and efficiency, enabling a more humanized and immersive interaction experience in typical application scenarios such as psychological companionship, intelligent education, and high-concurrency customer service. This study provides an effective technical pathway for developing the next generation of personalized and immersive affective intelligent interaction systems. Full article

Background/Objectives: Radiology often presents communication challenges due to its technical complexity, particularly for patients, trainees, and non-specialist clinicians. This study aims to evaluate the effectiveness of RadioBot, an AI-powered chatbot developed on the Botpress platform, in enhancing radiological communication through natural language processing (NLP). Methods: RadioBot was designed to provide context-sensitive responses based on guidelines from the American College of Radiology (ACR) and the Radiological Society of North America (RSNA). It addresses queries related to imaging indications, contraindications, preparation, and post-procedural care. A structured evaluation was conducted with twelve participants—patients, residents, and radiologists—who assessed the chatbot using a standardized quality and satisfaction scale. Results: The chatbot received high satisfaction scores, particularly from patients (mean = 4.425) and residents (mean = 4.250), while radiologists provided more critical feedback (mean = 3.775). Users appreciated the system’s clarity, accessibility, and its role in reducing informational bottlenecks. The perceived usefulness of the chatbot inversely correlated with the user’s level of expertise, serving as an educational tool for novices and a time-saving reference for experts. Conclusions: RadioBot demonstrates strong potential in improving radiological communication and supporting clinical workflows, especially with patients where it plays an important role in personalized medicine by framing radiology data within each individual’s cognitive and emotional context, which improves understanding and reduces associated diagnostic anxiety. Despite limitations such as occasional contextual incoherence and limited multimodal capabilities, the system effectively disseminates radiological knowledge. Future developments should focus on enhancing personalization based on user specialization and exploring alternative platforms to optimize performance and user experience. Full article

West Africa’s agri-food systems face a triple burden of malnutrition, climate vulnerability, and structural inefficiencies that compromise nutrition and public health. Despite increased attention to food security, agricultural strategies often prioritize yield over dietary quality. This paper explores the critical role of food processing in advancing Nutrition-Sensitive Agriculture (NSA) and Nutrition-Smart Agriculture (NSmartAg) across West Africa. Drawing on a systems lens, it positions food processing not as a peripheral activity, but as a catalytic mechanism that connects nutrient-dense production with improved consumption outcomes. Food processing can reduce post-harvest losses, preserve micronutrients, extend food availability, and foster inclusive value chains particularly for women and youth. Yet, persistent challenges remain, including institutional fragmentation, infrastructure gaps, and limited financial and technical capacity. This paper proposes a conceptual framework linking food processing to NSA and NSmartAg objectives and outlines operational entry points for implementation. By integrating processing into agricultural policies, investment, education, and monitoring systems, stakeholders and policymakers can reimagine agriculture as a platform for resilience and nutritional equity. Strategic recommendations emphasize multisectoral collaboration, localized solutions, and evidence-informed interventions to drive the transformation toward sustainable, nutrition-oriented food systems. Full article

The rise of knowledge-based economies has positioned higher education institutions as key actors in human capital development, requiring them to engage more actively with labor markets through strategic partnerships. Within this context, university-affiliated science and technology parks (STPs) have evolved into integrated learning environments that support experiential learning and mentorship practices. This study aims to explore the lived experiences of undergraduate students who participated in these processes within an STP in İstanbul, Türkiye. Using a qualitative phenomenological approach, data were collected through semi-structured interviews with 15 students selected via purposive maximum variation sampling. Thematic analysis, supported by MAXQDA 2024, was used to examine the data. Two main themes were identified: (i) talent development through experiential learning and (ii) talent development through mentorship. The findings indicate that students reconstructed theoretical knowledge through real-world applications, developed a clearer professional identity, and gained strategic career awareness. Mentorship provided both technical and psychosocial support, fostering self-confidence, emotional security, and role modeling. This study concludes that STPs play a strategic role in aligning academic learning with employability and institutional talent development goals. These results contribute to broader educational and workforce development discussions and are closely aligned with Sustainable Development Goals 4 (Quality Education) and 8 (Decent Work and Economic Growth), highlighting STPs as transformative platforms in higher education. Moreover, this study offers practical implications for aligning higher education with employment systems through structured experiential learning and mentorship practices. Full article

This study proposes a GPU-accelerated Position-Based Dynamics (PBD) system for realistic and interactive cloth simulation in Extended Reality (XR) environments, and comprehensively evaluates its performance and functional capabilities on standalone XR devices, such as the Meta Quest 3. To overcome the limitations of traditional CPU-based physics simulations, we designed and optimized highly parallelized algorithms utilizing Unity’s Compute Shader framework. The proposed system achieves real-time performance by implementing efficient collision detection and response handling with complex environmental meshes (RoomMesh) and dynamic hand meshes (HandMesh), as well as capsule colliders based on hand skeleton tracking (OVRSkeleton). Performance evaluations were conducted for both single-sided and double-sided cloth configurations across multiple resolutions. At a 32 × 32 resolution, both configurations maintained stable frame rates of approximately 72 FPS. At a 64 × 64 resolution, the single-sided cloth achieved around 65 FPS, while the double-sided configuration recorded approximately 40 FPS, demonstrating scalable quality adaptation depending on application requirements. Functionally, the GPU-PBD system significantly surpasses Unity’s built-in Cloth component by supporting double-sided cloth rendering, fine-grained constraint control, complex mesh-based collision handling, and real-time interaction with both hand meshes and capsule colliders. These capabilities enable immersive and physically plausible XR experiences, including natural cloth draping, grasping, and deformation behaviors during user interactions. The technical advantages of the proposed system suggest strong applicability in various XR fields, such as virtual clothing fitting, medical training simulations, educational content, and interactive art installations. Future work will focus on extending the framework to general deformable body simulation, incorporating advanced material modeling, self-collision response, and dynamic cutting simulation, thereby enhancing both realism and scalability in XR environments. Full article