Search Results (195)

This study demonstrates that nature play meaningfully supports children’s well-being, engagement, sense of belonging, and connection to nature. Over 10 weeks, Year One students (n = 25) from a metropolitan government school in Sydney Australia, participated in a Bush School program, experiencing it as a space of joy, calm, challenge, and growth. Children came to see Bush School not as a break from learning but as a different kind of learning: active, relational, and purposeful. Using a quasi-experiment mixed-methods design, including reflective journals, self-report tools, and class assessments, the study found no negative impact on reading or mathematics outcomes, addressing concerns about lost instructional time. Instead, nature play enhanced number and algebra development, self-regulation, collaboration, and motivation to learn. The findings from this study highlight the potential of nature play to complement formal education in a developmentally appropriate way. Moreover, embedding nature play into mainstream schooling provides a timely and relevant response to current challenges facing education. The study also highlights the importance of listening to children as capable meaning-makers with valuable perspectives. In an era of growing pressure on children and schools, nature play invites a shift in mindset; to slow down, trust children, and embrace the natural world as a co-teacher. Full article

The accelerating global urbanization process has posed new challenges to urban planning. With the rapid advancement of artificial intelligence (AI) technology, the application of AI in urban planning has gradually emerged as a prominent research focus. This study systematically reviews the current state, development trends, and challenges of AI applications in urban planning through a combination of bibliometric analysis using Citespace, AI-assisted reading based on generative models, and predictive analysis via support vector machine (SVM) algorithms. The findings reveal the following: (1) The application of AI in urban planning has undergone three stages—namely, the budding stage (January 1984 to January 2017), the rapid development stage (January 2017 to January 2023), and the explosive growth stage (January 2023 to January 2025). (2) Research hotspots have shifted from early-stage basic data integration and fundamental technology exploration to a continuous fusion and iteration of foundational and emerging technologies. (3) Globally, China, the United States, and India are the leading contributors to research in this field, with inter-country collaborations demonstrating regional clustering. (4) High-frequency keywords such as “deep learning,” “machine learning,” and “smart city” are prevalent in the literature, reflecting the application of AI technologies across both macro and micro urban planning scenarios. (5) Based on current research and predictive analysis, the application scenarios of technologies like deep learning and machine learning are expected to continue expanding. At the same time, emerging technologies, including generative AI and explainable AI, are also projected to become focal points of future research. This study offers a technical application guide for urban planning, promotes the scientific integration of AI technologies within the field, and provides both theoretical support and practical guidance for achieving efficient and sustainable urban development. Full article

In the context of the global growth of the collaborative economy, Airbnb has established itself as one of the most influential players in the transformation of the tourist accommodation market, especially in the reconfiguration of urban tourist accommodation. This article examines empirically and critically how this platform operates in Buenos Aires, the most visited city in Argentina and one of the main tourist hubs in South America. Based on a database of 17,249 active listings, the price formation of accommodations is analyzed using a comparative methodological approach between a general linear model (GLM) and a geographically weighted regression (GWR) model. While the GLM allows for capturing general patterns, the GWR reveals significant territorial differences, offering a detailed reading of the spatial behavior of prices in the city. The results show that variables such as the capacity of the accommodation, its type (full house), the host’s condition, the users’ ratings and the proximity to strategic points such as the subway or Plaza de Mayo have a significant influence on prices. In addition, it is shown that the influence of these variables varies by neighborhood, confirming that the pricing logic in Airbnb is deeply territorialized. This study not only provides novel empirical evidence for a Latin American city that has been little explored in the international literature, but also offers useful tools for hosts, urban planners and public decision makers. Its main contribution lies in showing that prices respond not only to accommodation attributes, but also to broader spatial inequalities, opening the debate on the effects of Airbnb on housing access and urban management in cities with strained real estate markets. By shedding light on these territorial asymmetries, the study offers valuable insights for public policy and urban governance and contributes directly to the achievement of Sustainable Cities and Communities (SDG 11), while also supporting Industry, Innovation and Infrastructure (SDG 9) and Reduced Inequalities (SDG 10), by providing practical knowledge that fosters more equitable and sustainable urban development. Full article

Aim: This study assessed how pharmacists perceive the impact of their role in the mental health (MH) services in two National Health Service (NHS) Trusts in England and their views on this service. Methods: An interview-based study was conducted from September to December 2023 on Microsoft Teams^® by interviewing the pharmacists involved in MH services in Berkshire Healthcare NHS Foundation Trust & the Birmingham and Solihull Mental Health Trust (BSMHFT) in England. Interviews were conducted using a semi-structured interview guide containing questions related to pharmacists’ roles, activities, perceptions about the service, and future recommendations. Transcripts were prepared and analysed using thematic analysis. The study was approved by the ethics committee of the School of Pharmacy at the University of Reading and was registered as a service evaluation with both Trusts. Results: A total of 11 participants attended the interviews. Most of the participants self-identified as women (n = 9), worked between 25 and 40 h on average weekly (n = 8), and had training in MH (n = 7). Few (n = 4) had work experience >20 years. Four themes emerged: (1) Roles and responsibilities—pharmacists play a vital role in medication management, clinical decision-making, and patient counselling; (2) satisfaction and positive impacts—a high job satisfaction derived from improved patient outcomes and effective multidisciplinary collaboration was reported; (3) challenges and barriers—stigma, role ambiguity, limited training in mental health, and institutional challenges (workload, funding, etc.), were identified; participants also expressed scepticism about the readiness of newly qualified prescriber pharmacists; (4) recommendations—participants advocated for enhanced MH content in pharmacy curricula, societal awareness and de-stigmatisation. Conclusions: Pharmacists viewed their role as integral to providing MH services; however, progress is impeded by challenges such as stigma, fragmented care, training gaps, and staffing shortages. It seemed unclear at the moment how the new prescriber-ready pharmacists will contribute to services. Additional findings from primary-care settings would provide a collective account of the current roles of pharmacists and their potential in MH. Full article

Artificial intelligence (AI) has exploded in public awareness over recent years and is already beginning to reshape the health care sector. Yet, even as AI becomes more prevalent, it remains a mystery to many providers who lack hands-on exposure during their training or on the job. Intended for medical professionals, this article defines essential concepts in AI interspersed with illustrations of how such concepts may be applied within cardiology and radiology—fields that have garnered the most approved medical AI applications to date. No experience in the field of AI is requisite before reading. To assist providers encountering novel machine learning tools, we also present an AI model checklist to empower critical assessment. We finally discuss hurdles in the path of developing pediatric AI tools—including challenges distinct from the adult setting—and discuss potential solutions, including various methods of multisite collaboration. This article aims to increase the engagement of health care professionals who may encounter AI models in practice or who seek to become involved in AI development themselves. We encourage the reader the freedom to either peruse this article in its entirety or to reference specific concepts individually. Terminology central to machine learning is emphasized in bold. Full article

Industrial environments are complex, making the monitoring of gauge meters challenging. This is especially true in confined spaces, underground, or at high altitudes. These difficulties underscore the need for intelligent solutions in the inspection and monitoring of plant assets, such as gauge meters. In this study, we plan to integrate unmanned ground vehicles and unmanned aerial vehicles to address the challenge, but the integration of these heterogeneous systems introduces additional complexities in terms of coordination, interoperability, and communication. Our goal is to develop a multi-domain robotic swarm system for industrial mapping and asset monitoring. We created an experimental setup to simulate industrial inspection tasks, involving the integration of a TurtleBot 2 and a QDrone 2. The TurtleBot 2 utilizes simultaneous localization and mapping (SLAM) technology, along with a LiDAR sensor, for mapping and navigation purposes. The QDrone 2 captures high-resolution images of meter gauges. We evaluated the system’s performance in both simulation and real-world environments. The system achieved accurate mapping, high localization, and landing precision, with 84% accuracy in detecting meter gauges. It also reached 87.5% accuracy in reading gauge indicators using the paddle OCR algorithm. The system navigated complex environments effectively, showcasing the potential for real-time collaboration between ground and aerial robotic platforms. Full article

Artificial intelligence is transforming industrial marketing by reshaping processes, decision-making, and inter-firm relationships. However, research remains fragmented, with limited evidence on how adoption drivers create new capabilities and sustainability outcomes. This study develops and empirically validates an integrated framework that combines technology, organization, environment, user acceptance, resource-based perspectives, dynamic capabilities, and explainability. A convergent mixed-methods design was applied, combining survey data from industrial firms with thematic analysis of practitioner insights. The findings show that technological readiness, organizational commitment, environmental pressures, and user perceptions jointly determine adoption breadth and depth, which in turn foster marketing capabilities linked to measurable improvements. These include shorter quotation cycles, reduced energy consumption, improved forecasting accuracy, and the introduction of carbon-based pricing mechanisms. Qualitative evidence further indicates that explainability and human–machine collaboration are decisive for trust and practical use, while sustainability-oriented investments act as catalysts for long-term transformation. The study provides the first empirical integration of adoption drivers, capability building, and sustainability outcomes in industrial marketing. By demonstrating that artificial intelligence advances competitiveness and sustainability simultaneously, it positions marketing as a strategic lever in the transition toward digitally enabled and environmentally responsible industrial economies. We also provide a simplified mapping of theoretical lenses, detail B2B-specific scale adaptations, and discuss environmental trade-offs of AI use. Given the convenience/snowball design, estimates should be read as upper-bound effects for mixed-maturity populations; robustness checks (stratification and simple reweighting) confirm sign and significance. Full article

This paper explores ways to use AI for active learning strategies so that students in higher education may perceive generative artificial intelligence (generative AI) as a collaborative partner in their learning experience. This study proposes AI can help advance educational sustainability when students read texts on critical posthumanism, reflect on the philosophical and ontological paradigms through which the human has been understood, and discuss the collaborative relationship between humans and AI using literary texts. By analyzing AI-collaborated writing assignments, student questionnaires, and peer evaluations, this study concludes there are three learning types based on the different levels of students’ perceived difficulties: a cognitive learner, who focuses on AI’s functional aspects such as information retrieval; a metacognitive learner, who engages with generative AI in a two-way communication; and an affective learner, who strictly differentiates the human from the nonhuman and claims reciprocity in human–AI communication to be impossible. This study utilizes a mixed-methods approach by integrating quantitative analysis of the student questionnaires and qualitative analysis of the writing assignments. The findings of the study will serve as a valuable resource for researchers and educators committed to fostering future-oriented citizenship through collaboration between humans and generative AI in higher education. Full article

The intersection of Artificial Intelligence (AI) and education is revolutionizing learning and teaching in this digital era, with Generative AI and large language models (LLMs) providing even greater possibilities for the future. The digital transformation of language education demands innovative approaches that combine pedagogical rigor with explainable AI (XAI) principles, particularly for low-resource languages. This paper presents a novel methodology that integrates Business Process Model and Notation (BPMN) with Multi-Agent Systems (MAS) to create transparent, workflow-driven language tutors. Our approach uniquely embeds XAI through three mechanisms: (1) BPMN’s visual formalism that makes agent decision-making auditable, (2) Retrieval-Augmented Generation (RAG) with verifiable knowledge provenance from textbooks of the National Institute of Languages of Luxembourg, and (3) human-in-the-loop validation of both content and pedagogical sequencing. To ensure realism in learner interaction, we integrate speech-to-text and text-to-speech technologies, creating an immersive, human-like learning environment. The system simulates intelligent tutoring through agents’ collaboration and dynamic adaptation to learner progress. We demonstrate this framework through a Luxembourgish language learning platform where specialized agents (Conversational, Reading, Listening, QA, and Grammar) operate within BPMN-modeled workflows. The system achieves high response faithfulness (0.82) and relevance (0.85) according to RAGA metrics, while speech integration using Whisper STT and Coqui TTS enables immersive practice. Evaluation with learners showed 85.8% satisfaction with contextual responses and 71.4% engagement rates, confirming the effectiveness of our process-driven approach. This work advances AI-powered language education by showing how formal process modeling can create pedagogically coherent and explainable tutoring systems. The architecture’s modularity supports extension to other low-resource languages while maintaining the transparency critical for educational trust. Future work will expand curriculum coverage and develop teacher-facing dashboards to further improve explainability. Full article

The aim of this paper is to clarify the difference between soft and hard news and to explore consumer preferences so that newspapers can adapt their artificial intelligence (AI) tools accordingly. The work focuses on the analysis of the results from a focus group discussion held on 26 March 2024, on the reading habits among readers of the news website Večernji.hr. The analysis shows that readers are not fully aware of their reading habits, often overestimating their interest in hard news while underestimating their interest in miscellaneous entertaining content in the media commonly referred to as soft news. In order to verify their statements, a content analysis of the 50 most-read articles on that news website from 25 to 29 March 2024 was conducted, also from the perspective of hard and soft news, so that this data can be compared with the results of the focus group discussion. The analysis of article readership confirmed and further emphasized the readers’ interest in the miscellanea. These findings have been contextualized within previous experiences of using artificial intelligence in the media, which show that AI tools are highly suitable for informative genres based on service data—typically classified as hard news, and are also compatible with some types of soft news. The great interest of readers in soft news suggests that significant effort should be put in its production and that it should not be considered an unimportant supplement to hard news. Artificial intelligence tools are less suitable for creating miscellanea, but they can be helpful in analyzing trends and detecting events. In these areas collaboration between humans and machines is essential, as only a journalist can accurately understand the human dimension and social context, which is the necessary framework for producing soft news. Full article

As technology continues to shape how students read and write, digital literacy practices have become increasingly multimodal and complex—posing new challenges for researchers seeking to understand these processes in authentic educational settings. This paper presents three qualitative studies that use multimodal analyses and visual modeling to examine digital reading and writing across age groups, learning contexts, and literacy activities. The first study introduces collaborative composing snapshots, a method that visually maps third graders’ digital collaborative writing processes and highlights how young learners blend spoken, written, and visual modes in real-time online collaboration. The second study uses digital reading timescapes to track the multimodal reading behaviors of fifth graders—such as highlighting, re-reading, and gaze patterns—offering insights into how these actions unfold over time to support comprehension. The third study explores multimodal composing timescapes and transmediation visualizations to analyze how bilingual high school students compose across languages and modes, including text, image, and sounds. Together, these innovative methods illustrate the power of multimodal analysis and visual modeling for capturing the complexity of digital literacy development. They offer valuable tools for designing more inclusive, equitable, and developmentally responsive digital learning environments—particularly for culturally and linguistically diverse learners. Full article

Background/Objectives: Artificial Intelligence (AI) is improving dentistry through increased accuracy in diagnostics, planning, and workflow automation. AI tools, including machine learning (ML) and deep learning (DL), are being adopted in oral medicine to improve patient care, efficiency, and lessen clinicians’ workloads. AI in dentistry, despite its use, faces an issue of acceptance, with its obstacles including ethical, legal, and technological ones. In this article, a review of current AI use in oral medicine, new technology development, and integration barriers is discussed. Methods: A narrative review of peer-reviewed articles in databases such as PubMed, Scopus, Web of Science, and Google Scholar was conducted. Peer-reviewed articles over the last decade, such as AI application in diagnostic imaging, predictive analysis, real-time documentation, and workflows automation, were examined. Besides, improvements in AI models and critical impediments such as ethical concerns and integration barriers were addressed in the review. Results: AI has exhibited strong performance in radiographic diagnostics, with high accuracy in reading cone-beam computed tomography (CBCT) scan, intraoral photographs, and radiographs. AI-facilitated predictive analysis has enhanced personalized care planning and disease avoidance, and AI-facilitated automation of workflows has maximized administrative workflows and patient record management. U-Net-based segmentation models exhibit sensitivities and specificities of approximately 93.0% and 88.0%, respectively, in identifying periapical lesions on 2D CBCT slices. TensorFlow-based workflow modules, integrated into vendor platforms such as Planmeca Romexis, can reduce the processing time of patient records by a minimum of 30 percent in standard practice. The privacy-preserving federated learning architecture has attained cross-site model consistency exceeding 90% accuracy, enabling collaborative training among diverse dentistry clinics. Explainable AI (XAI) and federated learning have enhanced AI transparency and security with technological advancement, but barriers include concerns regarding data privacy, AI bias, gaps in AI regulating, and training clinicians. Conclusions: AI is revolutionizing dentistry with enhanced diagnostic accuracy, predictive planning, and efficient administration automation. With technology developing AI software even smarter, ethics and legislation have to follow in order to allow responsible AI integration. To make AI in dental care work at its best, future research will have to prioritize AI interpretability, developing uniform protocols, and collaboration between specialties in order to allow AI’s full potential in dentistry. Full article

This study investigates how Spanish heritage language (SHL) learners, and second language (L2) learners cognitively and socially engage with texts through collaborative digital annotations. Conducted in two advanced online writing courses with forty students, the study employed Perusall, a social annotation platform, to examine reading behaviors and peer interactions. Quantitative analysis revealed both similarities and differences in strategy use: while both groups demonstrated equal levels of interaction, SHL learners favored Evaluating and Connecting strategies, suggesting reflective, experience-based engagement, whereas L2 learners more frequently used Questioning and Translating strategies, indicating a more analytical approach. Survey responses further highlighted perceived gains in vocabulary, motivation, grammar, and academic language awareness. These findings challenge deficit-based assumptions about SHL literacy and underscore the value of integrating culturally relevant, digitally mediated tasks in language instruction. The study affirms that collaborative annotation not only fosters cognitive engagement but also promotes social presence and academic identity development. It offers practical recommendations for grouping, scaffolding, and platform use, and contributes to a broader understanding of how digital tools can support inclusive, meaningful reading experiences for diverse learners in the twenty-first-century classroom. Full article

Introduction: Recent guidelines developed by the European Association for the Study of Obesity (EASO) and the European Federation of the Associations of Dietitians (EFAD) focused on the dietetic management of obesity in adults. The present study aimed to explore the perspectives of healthcare professionals regarding these guidelines. Methods: In total, 85 registered dietitians/nutritionists from Greece, the Netherlands, the Republic of Ireland, and the United Kingdom completed an online survey, and 10 were interviewed, in February–March 2023. Demographic data were also collected. Results: Awareness of the EASO-EFAD guidelines among registered dietitians/nutritionists was moderate (57.6%), but only 20% had read them in full. Dietitians with higher education and relevant experience were more likely to have read the guidelines. Less than half reported that key evidence-based recommendations, such as individualized medical nutrition therapy and intensive behavioral interventions, are already included in national guidance. Recommendations like portfolio or DASH diets, partial meal replacements, and calorie restriction were less commonly part of national guidance/usual practice. A small percentage of participants described their adoption of several nutritional approaches novel to them. These included the portfolio dietary pattern, partial meal replacements, and intermittent fasting or continuous calorie restriction. For some Irish dietitians, prioritizing weight as the main outcome conflicted with their emphasis on overall health and individualized nutrition therapy. Other barriers of recommendation implementation included exclusive availability in English, rapid changes in obesity management, staffing shortages, limited multidisciplinary collaboration, and inconsistent knowledge among healthcare providers. Conclusions: The present study identified gaps in the adoption of the EASO-EFAD guidelines into dietetic/clinical practice. EFAD will develop strategies to disseminate these guidelines at different levels of stakeholders (national/local authorities, dietitians/nutritionists, and patients). Full article

Background/Objectives: The middle-income countries (MICs) in the Middle East and North Africa (MENA) region face multifaceted challenges—including fiscal constraints, conflict, and vaccine hesitancy—that impede the timely introduction of critical vaccines. This study examines the status, barriers, and facilitators to introducing three critical vaccines—human papillomavirus vaccine (HPV), pneumococcal conjugate vaccine (PCV), and rotavirus vaccine (RV)—across seven MENA MICs, to identify actionable solutions to enhance vaccine uptake and immunisation coverage. Methods: Using the READ methodology (ready materials, extract, analyse, and distil data), this review systematically analysed policy documents, reports, and the literature on the introduction of HPV, PCV, and RV vaccines in seven MENA MICs. A data extraction framework was designed to capture the status of vaccine introduction and barriers and facilitators to introduction. Findings and data gaps were validated with stakeholder consultations. Results: Of the seven study countries, progress in introducing PCV and RV has been uneven across the region (five countries have introduced PCV, four have introduced RV, and only a single country has introduced HPV at time of writing), hindered by vaccine hesitancy, fiscal challenges, and insufficient epidemiological data. Morocco is the only country to introduce all three vaccines, while Egypt has yet to introduce any. Other common barriers include the impact of conflict and displacement on healthcare infrastructure, delayed introduction due to the 2020 COVID-19 pandemic, and limited local production facilities and regional cooperation. In addition, not all countries eligible for Gavi MICs support have applied. These findings provide a roadmap for policymakers to accelerate equitable vaccine introduction in the MENA region. Conclusions: Targeted efforts, such as addressing fiscal constraints, improving local manufacturing, tackling gender barriers, and fostering public trust, paired with regional collaboration, can help bridge gaps and ensure no community is left behind in preventing vaccine-preventable diseases. Full article