Search Results (634)

Artificial intelligence technologies, particularly machine learning and computer vision, are being increasingly utilized to preserve, restore, and create immersive virtual experiences with cultural artifacts and sites, thus aiding in conserving cultural heritage and making it accessible to a global audience. This paper examines the performance of Generative Adversarial Networks (GAN), especially Style-Based Generator Architecture (StyleGAN), as a deep learning approach for producing realistic images of Egyptian monuments. We used Sigmoid loss for Language–Image Pre-training (SigLIP) as a unique image–text alignment system to guide monument generation through semantic elements. We also studied truncation methods to regulate the generated image noise and identify the most effective parameter settings based on architectural representation versus diverse output creation. An improved discriminator design that combined noise addition with squeeze-and-excitation blocks and a modified MinibatchStdLayer produced 27.5% better Fréchet Inception Distance performance than the original discriminator models. Moreover, differential evolution for latent-space optimization reduced alignment mistakes during specific monument construction tasks by about 15%. We checked a wide range of truncation values from 0.1 to 1.0 and found that somewhere between 0.4 and 0.7 was the best range because it allowed for good accuracy while retaining many different architectural elements. Our findings indicate that specific model optimization strategies produce superior outcomes by creating better-quality and historically correct representations of diverse Egyptian monuments. Thus, the developed technology may be instrumental in generating educational and archaeological visualization assets while adding virtual tourism capabilities. Full article

It is well-established that principal leadership significantly influences student learning in developed countries, yet much less is known about how leadership practices manifest in complex systems like China’s, where rapid modernization intersects with deep-rooted educational traditions. In particular, Chinese principals face multiple challenges in balancing the implementation of educational reform policies, high parental expectations, and their own educational ideology, all within limited resources. The current study examines these challenges in Shenzhen, a city which typically manifests them through its rapid development. Specifically, we took a phenomenographic approach and interviewed the principals and staff from five prestigious primary schools to extract the key components behind the diverse school leaders’ styles and practices. Results showed that, the Chinese leadership practice model consists of five key components: mission setting, infrastructure reconstruction, teacher development, learning improvement, and educators’ networking. Although the first four components in this model align with established theories in developed countries, networking was identified as a distinctive and critical element for securing resources and fostering collaboration. These findings may broaden the scope of leadership theories and underscore the need to contextualize leadership practices based on local challenges and dynamics. It also offers practical insights for school leaders on navigating challenges to improve teacher and student outcomes. Full article

Value-at-Risk (VaR) is a key metric widely applied in market risk assessment and regulatory compliance under the Basel III framework. This study compares two Monte Carlo-based VaR models using publicly available equity data: a return-based model calibrated to historical portfolio volatility, and a CAPM-style factor-based model that simulates risk via systematic factor exposures. The two models are applied to a technology-sector portfolio and evaluated under historical and rolling backtesting frameworks. Under the Basel III backtesting framework, both initially fall into the red zone, with 13 VaR violations. With rolling-window estimation, the return-based model shows modest improvement but remains in the red zone (11 exceptions), while the factor-based model reduces exceptions to eight, placing it into the yellow zone. These results demonstrate the advantages of incorporating factor structures for more stable exception behavior and improved regulatory performance. The proposed framework, fully transparent and reproducible, offers practical relevance for internal validation, educational use, and model benchmarking. Full article

Educational institutions are facing a crisis characterized by the need to address diverse learning styles and vocational aspirations, exacerbated by ongoing financial pressures. To navigate these challenges effectively, there is an urgent need to innovate educational practices and learning environments, ensuring they are adaptable and responsive to the evolving needs of students and the workforce. The adoption of the Industry 5.0 framework offers a promising solution, providing a holistic approach that emphasizes the integration of human creativity and advanced technologies to transform educational institutions into resilient, human-centric, and sustainable learning environments. In this context, this article presents a transdisciplinary methodology that integrates Asset Management (AM) with Social Innovation (SI) through Design Thinking (DT) to co-design Educational Facilities 5.0 with stakeholders. The application of the proposed approach in an AgroLab case study—a food and agricultural laboratory—demonstrates how the methodology enables the definition of an Educational Facility 5.0 and generates AM Design Knowledge to support informed decision-making in the subsequent design, implementation, and operation phases. Following DT principles—where knowledge emerges through iterative experimentation and insights from practical applications—this article also discusses the role of SI and DT in AM, the role of Large Language Models in convergent processes, and a vision for Educational Facilities 5.0. Full article

ICT tools are fundamental for promoting educational inclusion, as they allow for flexible teaching practices adapted to the diversity of students. Their appropriate integration into teaching makes it easier to respond to different paces, styles, and needs, promoting the active and meaningful participation of all learners. However, this inclusive potential is only fulfilled if equitable access to and use of ICT is guaranteed, both at school and at home. This qualitative study explores the digital divide in its three main dimensions: access to technology, digital skills, and the meaningful use of ICT in educational settings. Through focus groups and semi-structured interviews, the study gathers the perceptions of different members of the educational community. The sample consists of 89 participants, including teachers, students, families, and school administrators from the Communities of Madrid and Castilla-La Mancha. The results reveal a common concern about inequalities in ICT access and use, related to economic, geographical, and educational factors. These findings emphasise the need for targeted public policies to bridge digital gaps and highlight the importance of promoting digital competence across the entire educational community to achieve true educational inclusion. Full article

Throughout history, communities have struggled to build homes in places actively hostile to their presence, a challenge long faced by African descendants in the American diaspora. In cities across the U.S., including Washington, D.C., efforts have often been made to erase Black cultural identity. D.C., once a hub of Black culture, saw its urban fabric devastated during the 1968 riots following Dr. Martin Luther King Jr.’s assassination. Since then, redevelopment has been slow and, more recently, marked by gentrification, which has further displaced Black communities. Amid this context, Black architects such as Michael Marshall, FAIA, and Sean Pichon, AIA, have emerged as visionary leaders. Their work exemplifies Value-Inclusive Design and aligns with Roberto Verganti’s Design-Driven Innovation by embedding cultural relevance and community needs into development projects. These architects propose an intentional approach that centers Black identity and brings culturally meaningful businesses into urban redevelopment, shifting the paradigm of design practice in D.C. This collective case study (methodology) argues that their work represents a distinct architectural style, Black Modernism, characterized by cultural preservation, community engagement, and spatial justice. This research examines two central questions: Where does Black Modernism begin, and where does it end? How does it fit within and expand beyond the broader American Modernist architectural movement? It explores the consequences of the destruction of Black communities, the lived experiences of Black architects, and how those experiences are reflected in their designs. Additionally, the research suggests that the work of Black architects aligns with heutagogical pedagogy, which views community stakeholders not just as beneficiaries, but as educators and knowledge-holders in architectural preservation. Findings reveal that Black Modernism, therefore, is not only a design style but a method of reclaiming identity, telling untold histories, and building more inclusive cities. Full article

Parental attention and care is essential for children and adolescents who are unable to take care of their own oral health. There have been studies on the characteristics of mothers and the oral conditions of children in Korea, but there are very few previous studies that report on the oral health status of children according to the employment status of mothers. The aim of this study was to investigate the relationship between maternal employment and educational status and children’s oral health. Using data from the 10th Panel Study on Korean Children (PSKC), we analyzed the association between maternal employment and education status and the occurrence of dental caries among 1175 nine-year-old Korean children. The relationship was examined through cross-tabulation and logistic regression analysis. After adjusting for the mother’s age, parental style, parental relationship, family talk time, family meal time, leisure time, area of residence, and household income, the study found that children with working and studying mothers were 1.159 times more likely to have dental caries than children with non-working and non-studying mothers. The relationship between maternal employment and educational status and children’s oral health was confirmed. Based on the results of this study, it is expected that systematic follow-up studies will be needed to better understand the association and causal relationship between dental caries and oral disease in children according to whether mothers are employed or educated. Full article

This article presents a comparative study of content generated by artificial intelligence (AI) and articles authored by professional journalists, focusing on the perspective of a Kazakhstani audience. The analysis was conducted based on several key criteria, including the structure of the article, writing style, factual accuracy, citation of sources, and completeness of the information. The study spans a variety of topics, such as politics, economics, law, sports, education, and social issues. The results indicate that AI-generated articles tend to exhibit greater structural clarity and neutrality. On the other hand, articles written by journalists score higher in terms of factual accuracy, analytical depth, and the use of verified sources. Furthermore, the research explores the significance of journalistic ethics in ensuring transparency and information completeness in content production. Ultimately, the findings emphasize the importance of upholding rigorous journalistic standards when integrating AI into media practices. Full article

Expressive imitation in the performing arts is typically trained through animal behavior imitation, aiming not only to reproduce action trajectories but also to recreate rhythm, style and emotional states. However, evaluation of such animal imitation behaviors relies heavily on teachers’ subjective judgments, lacking structured criteria, exhibiting low inter-rater consistency and being difficult to quantify. To enhance the objectivity and interpretability of the scoring process, this study develops a machine learning and structured pose data-based auxiliary evaluation framework for imitation quality. The proposed framework innovatively constructs three types of feature sets, namely baseline, ablation, and enhanced, and integrates recursive feature elimination with feature importance ranking to identify a stable and interpretable set of core structural features. This enables the training of machine learning models with strong capabilities in structured modeling and sensitivity to informative features. The analysis of the modeling results indicates that temporal–rhythm features play a significant role in score prediction and that only a small number of key feature values are required to model teachers’ ratings with high precision. The proposed framework not only lays a methodological foundation for standardized and AI-assisted evaluation in performing arts education but also expands the application boundaries of computer vision and machine learning in this field. Full article

Background: Data accessibility remains a significant barrier in healthcare AI due to privacy constraints and logistical challenges. Synthetic data, which mimics real patient information while remaining both realistic and non-identifiable, offers a promising solution. Large Language Models (LLMs) create new opportunities to generate high-fidelity clinical conversations between patients and physicians. However, the value of this synthetic data depends on careful evaluation of its realism, accuracy, and practical relevance. Objective: To assess the performance of four leading LLMs: ChatGPT 4.5, ChatGPT 4o, Claude 3.7 Sonnet, and Gemini Pro 2.5 in generating synthetic transcripts of patient–physician interactions in plastic surgery scenarios. Methods: Each model generated transcripts for ten plastic surgery scenarios. Transcripts were independently evaluated by three clinically trained raters using a seven-criterion rubric: Medical Accuracy, Realism, Persona Consistency, Fidelity, Empathy, Relevancy, and Usability. Raters were blinded to the model identity to reduce bias. Each was rated on a 5-point Likert scale, yielding 840 total evaluations. Descriptive statistics were computed, and a two-way repeated measures ANOVA was used to test for differences across models and metrics. In addition, transcripts were analyzed using automated linguistic and content-based metrics. Results: All models achieved strong performance, with mean ratings exceeding 4.5 across all criteria. Gemini 2.5 Pro received mean scores (5.00 ± 0.00) in Medical Accuracy, Realism, Persona Consistency, Relevancy, and Usability. Claude 3.7 Sonnet matched the scores in Persona Consistency and Relevancy and led in Empathy (4.96 ± 0.18). ChatGPT 4.5 also achieved perfect scores in Relevancy, with high scores in Empathy (4.93 ± 0.25) and Usability (4.96 ± 0.18). ChatGPT 4o demonstrated consistently strong but slightly lower performance across most dimensions. ANOVA revealed no statistically significant differences across models (F(3, 6) = 0.85, p = 0.52). Automated analysis showed substantial variation in transcript length, style, and content richness: Gemini 2.5 Pro generated the longest and most emotionally expressive dialogues, while ChatGPT 4o produced the shortest and most concise outputs. Conclusions: Leading LLMs can generate medically accurate, emotionally appropriate synthetic dialogues suitable for educational and research use. Despite high performance, demographic homogeneity in generated patients highlights the need for improved diversity and bias mitigation in model outputs. These findings support the cautious, context-aware integration of LLM-generated dialogues into medical training, simulation, and research. Full article

As generative artificial intelligence (GenAI) chatbots gain traction in educational settings, a growing number of studies explore their potential for personalized, scalable learning. However, methodological fragmentation has limited the comparability and generalizability of findings across the field. This study proposes a unified, learning analytics–driven framework for evaluating the impact of GenAI chatbots on student learning. Grounded in the collection, analysis, and interpretation of diverse learner data, the framework integrates assessment outcomes, conversational interactions, engagement metrics, and student feedback. We demonstrate its application through a multi-week, quasi-experimental study using a Socratic-style chatbot designed with pedagogical intent. Using clustering techniques and statistical analysis, we identified patterns in student–chatbot interaction and linked them to changes in learning outcomes. This framework provides researchers and educators with a replicable structure for evaluating GenAI interventions and advancing coherence in learning analytics–based educational research. Full article

Dance encompasses physical, emotional, and social elements, creating a dynamic platform for the exploration of well-being. As a therapeutic approach, dance movement further applies these dimensions to enhance emotional resilience, foster mindfulness, and improve overall mental health. This study examined the relationship between self-compassion and life satisfaction among 912 recreational dancers (80% female and 20% male) in Greece. Participants completed the Self-Compassion Scale and Satisfaction with Life Scale. Confirmatory Factor Analysis validated the five-factor self-compassion model, and regression analysis identified predictors of life satisfaction. Self-kindness emerged as a strong positive predictor (β = 0.258, p < 0.001), while isolation (β = −0.307, p < 0.001) and self-judgment (β = −0.083, p = 0.029) negatively predicted life satisfaction. Common humanity (β = 0.064, p = 0.066) and mindfulness (β = 0.004, p = 0.907) showed no significant predictive effect. The model explained 21.7% of the variance in life satisfaction (R² = 0.217). Small but statistically significant differences in self-compassion dimensions were observed across dance styles. Partner-oriented dancers such as those practicing tango reported slightly higher self-kindness and mindfulness, while ballet dancers showed a small increase in self-judgment and isolation. Life satisfaction remained consistent across styles, highlighting dance’s overall contribution to well-being. These findings suggest that integrating self-compassion training into dance education and psychotherapy, particularly within a Therapeutic-Based Art Pedagogy framework, may contribute to emotional resilience, foster social connection, and promote mental health, positioning dance as a potentially transformative tool for holistic development. Full article

The rapid expansion of virtual education has highlighted both its opportunities and limitations. Conventional virtual learning environments tend to lack flexibility, often applying standardized methods that do not account for individual learning differences. In contrast, Artificial Intelligence (AI) empowers the creation of customized educational experiences that address specific student needs. Such personalization is essential to mitigate educational inequalities, particularly in areas with limited infrastructure, scarce access to trained educators, and varying levels of digital literacy. This study explores the role of AI in advancing virtual education, with particular emphasis on supporting differentiated learning. It begins by selecting an appropriate pedagogical model to guide personalization strategies and proceeds to investigate the application of AI techniques across three key areas: the characterization of educational resources, the detection of learning styles, and the recommendation of tailored content. The primary contribution of this research is the development of a scalable framework that can be adapted to a variety of educational contexts, with the goal of enhancing the effectiveness and personalization of virtual learning environments through AI. Full article

The acquisition of new knowledge by students represents a significant area of interest for universities, which seek to facilitate this process to enhance educational experience. There are two principal categories of learning approaches: surface and deep. The prevalence of a particular approach is contingent upon a number of individual and contextual factors. The aim of this study is to determine whether there are discernible differences in learning styles based on the geographical area of origin of the student. To this end, a multivariate analysis will be employed to compare the predominant learning approaches of health science university students using the Biggs R-SPQ-2F scale. A sample of 464 students was subjected to a multivariate analysis, specifically a Manova-Biplot, with the objective of facilitating the graphical representation of the relationships between the two learning approaches. A confirmatory factor analysis was conducted on the sample to corroborate the factor structure of the R-SPQ-2F. The findings indicated that the majority of students demonstrated proclivity towards deep learning, although their profiles exhibited heterogeneity related to their geographical context. The results may prove valuable in the characterization of the predominant learning approaches in a university community and the design of teaching strategies. Full article

This article addresses a gap in the literature by offering the first structured reconstruction of the origins, motivations, and development of Ignatian Leadership, connecting its spiritual roots, conceptual foundations, and institutional applications within a coherent and transferable framework. The study explores the genesis, evolution, and contemporary relevance of Ignatian Leadership as a transformative model that combines organisational management principles with the spirituality of Saint Ignatius of Loyola and the Jesuit tradition. Through a qualitative methodology, we conducted an exhaustive review of 54 documents and interviews with key experts, incorporating diverse phenomenological perspectives. The findings show that this leadership model emerged to renew the apostolic mission of the Society of Jesus, modernise its educational management, and empower both laypeople and Jesuits in leadership roles. Grounded in Ignatian spirituality—particularly in the practice of discernment aimed at promoting actions inspired by the Magis, in ever deeper and greater service to the most universal good—it seeks to serve others and promote the common good. Over time, the model has expanded beyond religious contexts, offering a counter-cultural and ethically grounded leadership style applicable in educational, managerial and civic settings. This shift of focus not only paves the way for institutional change but also guides individuals towards a more authentic and meaningful life. Full article