Search Results (101)

Augmented reality (AR) offers a novel way to enrich museum visits by deepening engagement and enhancing learning. This study presents the development of a mobile application for the Abylkhan Kasteyev State Museum of Arts (Almaty, Kazakhstan), designed to recognize and visualize exhibits through AR. Using computer vision and machine learning, the application identifies artifacts via a smartphone camera and overlays interactive 3D models in an augmented environment. The system architecture integrates Flutter plugins for AR rendering, YOLOv8 for exhibit recognition, and a cloud database for dynamic content updates. This combination enables an immersive educational experience, allowing visitors to interact with digital reconstructions and multimedia resources linked to the exhibits. Pilot testing in the museum demonstrated recognition accuracy above 97% and received positive feedback on usability and engagement. These results highlight the potential of AR-based mobile applications to increase accessibility to cultural heritage and enhance visitor interaction. Future work will focus on enlarging the exhibit database, refining performance, and incorporating additional interactive features such as multi-user collaboration, remote access, and gamified experiences. Full article

Higher education (HE) has become a central site where the relations between democracy, pedagogy and technology are being reshaped through algorithmic infrastructures. In this context, a specific tension becomes visible: as educational processes become intertwined with systems of classification, prediction and optimization, recognition risks becoming conditional on data legibility, while pedagogical judgement is redirected toward procedural efficiency. Against this background, this article investigates how subjectivity, recognition and pedagogical responsibility can be conceptually framed when formative encounters are mediated through pedagogical practice as well as through algorithmic operations. To address this question, it develops the DEA model (Democratic Education under Algorithmic Conditions) as a reflexive, education–theoretical heuristic grounded in educational theory, subjectivation research, democratic thought and critical data studies. The model positions education, democracy and digitalisation as interdependent fields and specifies three analytical dimensions: formative, normative and inferential. These are elaborated through relational vectors and framing structures that include societal discourses, institutional configurations, cultural imaginaries and biographical conditions. The reconstruction shows how pedagogical responsibility becomes vulnerable to displacement by optimization routines, how recognition is reorganised by regimes of data legibility and how didactic relations are reconfigured through automated feedback and recommendation systems. Rather than prescribing technical solutions, the DEA model offers a conceptual orientation for tracing how algorithmic mediation redistributes recognition, responsibility and legitimacy in HE, and for sustaining Bildung and democratic subject formation under digital conditions. Full article

The integration of artificial intelligence (AI) is rapidly transforming the field of breast reconstruction, with applications spanning surgical planning, complication prediction, patient-reported outcome assessment, esthetic evaluation, and patient education. A comprehensive narrative review was performed to evaluate the integration of AI technologies in breast reconstruction, encompassing preoperative planning, intraoperative use, and postoperative care. Emerging evidence highlights AI’s growing utility across these domains. Machine learning algorithms can predict postoperative complications and patient-reported outcomes by leveraging clinical, surgical, and patient-specific factors. Neural networks provide objective assessments of breast esthetics following reconstruction, while large language models enhance patient education by guiding consultation questions and reinforcing in-clinic discussions with accessible medical information. As these tools continue to advance, their adoption in everyday practice is becoming increasingly relevant. Staying current with AI applications is essential for plastic surgeons, as AI is not only reshaping breast reconstruction today, but is also poised to become an integral component of routine clinical care. Full article

Cognitive diagnostic models (CDMs) are commonly used in educational assessment to uncover the specific cognitive skills that contribute to student performance, allowing for precise identification of individual strengths and weaknesses and the design of targeted interventions. Traditional CDMs, however, depend heavily on a predefined Q-matrix that specifies the relationship between test items and underlying attributes. In this study, we introduce a hidden Markov log-linear additive cognitive diagnostic model (HM-LACDM) that does not require a Q-matrix, making it suitable for analyzing longitudinal assessment data without prior structural assumptions. To support scalable applications, we develop a variational Bayesian inference (VI) algorithm that enables efficient estimation in large datasets. Additionally, we propose a method to reconstruct the Q-matrix from estimated item-effect parameters. The effectiveness of the proposed approach is demonstrated through simulation studies. Full article

This article examines the methodological challenges inherent in the digital 3D reconstructions of historical buildings using archival documentation. Unlike photogrammetry or laser scanning, archival-based modeling is crucial for buildings that never existed, no longer exist, or have undergone extensive modifications. Present research insights from a pilot educational project where 65 university students reconstructed 70 heritage buildings from Budapest (Hungary) in Archicad based solely on archival sources. In total, 75% of the buildings lacked at least one façade drawing, while nearly 20% showed contradictions between different plans (e.g., floor plan and section). Common challenges were identified, including missing drawings, contradictory plans, stylistic uncertainty, and software constraints, and their patterns were analyzed. To enhance modeling transparency, structured methods for recording paradata were proposed. Findings contribute to methodological rigor in virtual heritage reconstruction and support the reuse of archival models in architectural practice, research, and conservation. This study is among the first to propose a structured paradata framework tailored explicitly to archival-based 3D reconstructions, bridging methodological gaps between educational practice and professional heritage research. Full article

Background: Local flaps are a cornerstone of reconstructive plastic surgery for oncological skin defects, ensuring functional recovery and aesthetic integration. Their selection, however, varies with surgeon experience. Generative artificial intelligence has emerged as a potential decision-support tool, although its clinical role remains uncertain. Methods: We evaluated three generative AI platforms (ChatGPT-5 by OpenAI, Grok by xAI, and Gemini by Google DeepMind) in their free-access versions available in September 2025. Ten preoperative photographs of suspected cutaneous neoplastic lesions from diverse facial and limb sites were submitted to each platform in a two-step task: concise description of site, size, and tissue involvement, followed by the single most suitable local flap for reconstruction. Outputs were compared with the unanimous consensus of experienced plastic surgeons. Results: Performance differed across models. ChatGPT-5 consistently described lesion size accurately and achieved complete concordance with surgeons in flap selection. Grok showed intermediate performance, tending to recognise tissue planes better than lesion size and proposing flaps that were often acceptable but not always the preferred choice. Gemini estimated size well, yet was inconsistent for anatomical site, tissue involvement, and flap recommendation. When partially correct answers were considered acceptable, differences narrowed but the overall ranking remained unchanged. Conclusion: Generative AI can support reconstructive reasoning from clinical images with variable reliability. In this series, ChatGPT-5 was the most dependable for local flap planning, suggesting a potential role in education and preliminary decision-making. Larger studies using standardised image acquisition and explicit uncertainty reporting are needed to confirm clinical applicability and safety. Full article

This article examines the role of digital reconstructions of historical scenes in the teaching of history, highlighting their theoretical foundations, their methods, and the educational benefits they offer. Drawing from perspectives in educational sciences and digital humanities, we explore how the use of 3D modeling, virtual reality (VR), and augmented reality (AR) can create immersive environments that enhance learners’ engagement, curiosity, and critical thinking. After outlining the epistemological and didactic underpinnings—namely constructivism and the investigative approach to history—we detail the practical steps involved in reconstructing historical scenes (documentary research, iconographic analysis, 3D modeling). Two case studies illustrate how virtual reconstructions can bring historical contexts to life, improve knowledge retention, and encourage interdisciplinary collaboration. We then discuss the benefits for students, including improved understanding, motivation, and the development of critical analysis skills. Finally, we address the limitations and challenges associated with this pedagogical approach, such as technical and financial constraints, scientific validation, and teacher training. We conclude by identifying research perspectives, especially regarding the potential of artificial intelligence and collaborative international projects. Ultimately, digital reconstructions can be a powerful educational tool, enabling learners not only to “see” the past but also to reflect upon its complexities and debates. Full article

The structural behavior of tile vaults remains challenging to evaluate accurately through numerical models, due to their geometry, the heterogeneity of its mechanical properties, and its boundary conditions. This study presents an experimental investigation carried out as part of a teaching innovation project aimed at deepening the understanding of masonry behavior through hands-on construction and collapse testing. Scaled vaults were built using traditional methods, employing thin bricks and fast-setting gypsum, materials typically selected for their accessibility and compatibility with heritage-inspired craftsmanship. The models were incrementally loaded until failure, enabling direct observation of collapse mechanisms. Plastic limit analysis was used to estimate structural capacity, with a focus on verifying the compatibility conditions of hinge formation. The vaults were documented using photogrammetric reconstruction (Structure-from-Motion) to generate accurate 3D models, and the evolution of collapse mechanisms was analyzed through digital motion tracking of observed hinges. Experimental loading reached values up to 4 kN/m² without collapse, confirming that even thin-tile vaults exhibit considerable reserve capacity. While these values should be understood as conservative lower-bound estimates due to the workshop conditions, results also highlight the significant influence of construction imperfections and boundary conditions. This work reinforces the educational value of physical experimentation and offers empirical insights into tile masonry behavior that cannot be captured through purely digital or parametric models. Full article

The deployment of large language models (LLMs) in academic paper evaluation is increasingly widespread, yet their trustworthiness remains debated; to expose fundamental flaws often masked under conventional testing, this study employed extreme-scenario testing to systematically probe the lower performance boundaries of LLMs in assessing the scientific validity and logical coherence of papers from the humanities and social sciences (HSS). Through a highly credible quasi-experiment, 40 high-quality Chinese papers from philosophy, sociology, education, and psychology were selected, for which domain experts created versions with implanted “scientific flaws” and “logical flaws”. Three representative LLMs (GPT-4, DeepSeek, and Doubao) were evaluated against a baseline of 24 doctoral candidates, following a protocol progressing from ‘broad’ to ‘targeted’ prompts. Key findings reveal poor evaluation consistency, with significantly low intra-rater and inter-rater reliability for the LLMs, and limited flaw detection capability, as all models failed to distinguish between original and flawed papers under broad prompts, unlike human evaluators; although targeted prompts improved detection, LLM performance remained substantially inferior, particularly in tasks requiring deep empirical insight and logical reasoning. The study proposes that LLMs operate on a fundamentally different “task decomposition-semantic understanding” mechanism, relying on limited text extraction and shallow semantic comparison rather than the human process of “worldscape reconstruction → meaning construction and critique”, resulting in a critical inability to assess argumentative plausibility and logical coherence. It concludes that current LLMs possess fundamental limitations in evaluations requiring depth and critical thinking, are not reliable independent evaluators, and that over-trusting them carries substantial risks, necessitating rational human-AI collaborative frameworks, enhanced model adaptation through downstream alignment techniques like prompt engineering and fine-tuning, and improvements in general capabilities such as logical reasoning. Full article

This article presents an innovative methodological approach for the documentation and enhancement of ancient ironworking heritage in the Guéra region of Chad. By combining ethno-historical and archaeological surveys, 3D modeling with Blender, and the generation of images and video sequences through artificial intelligence (AI), we propose an integrated production pipeline enabling the faithful reconstruction of three types of metallurgical furnaces. Our method relies on rigorously collected field data to generate multiple and plausible representations from fragmentary information. A standardized evaluation grid makes it possible to assess the archaeological fidelity, cultural authenticity, and visual quality of the reconstructions, thereby limiting biases inherent to generative models. The results offer strong potential for integration into immersive environments, opening up perspectives in education, digital museology, and the virtual preservation of traditional ironworking knowledge. This work demonstrates the relevance of multimodal approaches in reconciling scientific rigor with engaging visual storytelling. Full article

Background/Objectives: Surgical ward rounds are central to trainee education but are often associated with stress, cognitive overload, and inconsistent learning. Advances in artificial intelligence (AI), particularly large language models (LLMs), offer new ways to support trainees by simulating ward-round questioning, enhancing preparedness, and reducing anxiety. This study explores the role of generative AI in surgical ward-round education. Methods: Hypothetical plastic and reconstructive surgery ward-round scenarios were developed, including flexor tenosynovitis, DIEP flap monitoring, acute burns, and abscess management. Using de-identified vignettes, AI platforms (ChatGPT-4.5 and Gemini 2.0) generated consultant-level questions and structured responses. Outputs were assessed qualitatively for relevance, educational value, and alignment with surgical competencies. Results: ChatGPT-4.5 showed a strong ability to anticipate consultant-style questions and deliver concise, accurate answers across multiple surgical domains. ChatGPT-4.5 consistently outperformed Gemini 2.0 across all domains, with higher expert Likert ratings for accuracy, clarity, and educational value. It was particularly effective in pre-ward round preparation, enabling simulated questioning that mirrored consultant expectations. AI also aided post-round consolidation by providing tailored summaries and revision materials. Limitations included occasional inaccuracies, risk of over-reliance, and privacy considerations. Conclusions: Generative AI, particularly ChatGPT-4.5, shows promise as a supplementary tool in surgical ward-round education. While both models demonstrated utility, ChatGPT-4.5 was superior in replicating consultant-level questioning and providing structured responses. Pilot programs with ethical oversight are needed to evaluate their impact on trainee confidence, performance, and outcomes. Although plastic surgery cases were used for proof of concept, the findings are relevant to surgical education across subspecialties. Full article

The development of Braille recognition technology is intrinsically linked to the educational rights of individuals with visual impairments. The key challenges in natural scene Braille detection include three core trade-offs: difficulty extracting small-target features under complex background interference, a balance between model accuracy and real-time performance, and generalization across diverse scenes. To address these issues, this paper proposes an improved YOLOv11 algorithm that integrates a lightweight gating mechanism and subspace attention. By reconstructing the C3k2 module into a hybrid structure containing Gated Bottleneck Convolutions (GBC), the algorithm effectively captures weak Braille dot matrix features. A super-lightweight subspace attention module (ULSAM) enhances the attention to Braille regions, while the SDIoU loss function optimizes bounding box regression accuracy. Experimental results on a natural scene Braille dataset show that the algorithm achieves a Precision of 0.9420 and a Recall of 0.9514 with only 2.374 M parameters. Compared to the base YOLOv11, this algorithm improves the combined detection performance (Precision: 0.9420, Recall: 0.9514) by 3.2% and reduces computational complexity by 6.3% (with only 2.374 M parameters). Ablation experiments validate the synergistic effect of each module: the GBC structure reduces the model parameter count by 8.1% to maintain lightweight properties, and the ULSAM effectively lowers the missed detection rate of ultra-small Braille targets. This study provides core algorithmic support for portable Braille assistive devices, advancing the technical realization of equal information access for individuals with visual impairments. Full article

In the context of global modernization, both the United States and China faced major challenges in rural social development. In the early twentieth century, the American federal government launched the Country Life Movement, during which Christianity addressed the rural crisis through rural church reforms. Meanwhile, influenced by the American-led World Agricultural Mission Movement, the Christian churches applied the experiences and insights gained from the U.S. rural church reforms to China’s rural reconstruction movement. During the first half of the twentieth century, the Christian rural reconstruction models in China evolved to become increasingly comprehensive and targeted. In the early decades, Christian missions promoted the establishment of an agricultural education system to cultivate rural talents. By the 1920s, churches in China had developed a comprehensive rural social reform program. After the 1928 Jerusalem Meeting of the International Missionary Council (IMC), the concept of “Rural Community Parish” emerged as the guiding principle for the comprehensive rural reconstruction program in China. The Christian church further clarified its ultimate goal: to build a “Christian rural civilization in China.” Based on this, Christian rural work in China developed steadily until 1950, when the withdrawal of Christian forces brought an end to their rural influence in China. Full article

This study analyzed the intersection between Afrodescendant ethnoeducation and the school-to-work transition in three marginalized communities of the Colombian Caribbean: La Boquilla, Tierra Bomba, and Libertad-Sucre. Using a qualitative methodology, the research reconstructed, on the one hand, the institutional framework of Afro-Colombian ethnoeducation since the 1991 Constitution, highlighting public policies implemented and their impacts. On the other hand, it examined the educational dynamics in these localities and their link (or lack thereof) to local labor markets, identifying innovations, limitations, and structural barriers affecting young people’s transition from school to work. The findings show that the Colombian ethnoeducational model has introduced curricular and participatory innovations aimed at enhancing cultural relevance and preparing students for productive life. However, its implementation faces persistent barriers including inadequate infrastructure, the legacies of internal armed conflict, structural racism, limited employment opportunities, and chronic public disinvestment. Despite valuable local initiatives—such as technical training in collaboration with the SENA (National Learning Service, Colombia’s public technical education system) in sectors like fishing and tourism—Afrodescendant youth continue to experience limited labor market integration. Finally, the article offers policy and practical recommendations from a decolonial ethnoeducational perspective, inspired by the pedagogy for liberation, to strengthen the school-to-work transition in contexts of vulnerability. 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