Search Results (10,797)

Background/Objectives: Kidney donation remains a critical component of addressing end-stage renal disease. This study examines differences in awareness, willingness to donate, and concerns related to kidney donation among medical and non-medical university students. By comparing these groups within the context of Croatia’s presumed-consent system for organ donation, the study provides insights into how educational backgrounds shape attitudes in a setting with high transplantation rates but limited data on young adults. Methods: A cross-sectional observational study targeted at medical and non-medical university students in Croatia. Data were collected from 640 participants via a self-administered, close-ended, structured questionnaire with 33 items divided across three sections. Responses were analyzed using IBM SPSS Statistics program (v. 30.0), to identify significant differences. Due to the cross-sectional design, causal relationships could not be inferred. Results: Overall, 190 students (28.7%) reported willingness to donate a kidney during their lifetime, which was more common among medical students (N = 59; 39.0%) than non-medical students (N = 131; 26.8%). Collectively, willingness to donate postmortem was high in both groups (N = 527; 82.3%), as was willingness in a brain-dead state (N = 448; 70.0%). Medical and non-medical students mostly cited perceived health risks as a concern and concerns related to surgical complications. Regarding information sources, 33.2% of students reported inadequate knowledge of kidney donation, with social media and internet searches cited more frequently than healthcare professionals. Conclusions: Our findings indicate that medical and non-medical students exhibit distinct gaps in knowledge, risk perception and willingness toward kidney donation. Within Croatia’s presumed-consent framework, these findings highlight the importance of targeted educational strategies to support informed decision-making among future generations. Full article

The article examines the implementation of 3D printing in civil protection and crisis management with a focus on the educational process, while 3D printing technology enables the creation of various teaching aids that streamline teaching and enrich theoretical knowledge. The empirical part of the study is based on a quantitative questionnaire survey among students of the Faculty of Safety Engineering of the University of Žilina in Žilina, with hypotheses set in advance and forming the basis for the construction of the questionnaire. The questionnaire collected data on the subjective evaluation of 3D printing through continuous, nominal, and ordinal responses and was completed by 277 students. Statistical methods of simple and group classification, as well as t-test, ANOVA, Kruskal–Wallis and Pearson’s correlation analysis were used to evaluate the data. Statistical significance was used to determine whether observed differences and relationships were unlikely to have arisen by chance. In addition, effect size measures were used in correlation and regression analyses to assess the strength and practical relevance of statistically significant relationships. The results of the study show that 3D printing significantly contributes to improving education and preparedness in civil protection, as it allows for more material-efficient and flexible production of educational aids compared to traditional custom production. Thus, it supports the development of more resilient communities and contributes to long-term sustainability. The findings confirmed that 3D printing is a suitable tool for improving public preparedness for emergencies. Full article

University dropout is a problem that affects all universities around the world. It is multidimensional and multicausal in nature. The consequences of a student dropping out affect them not only financially but also in terms of their self-perception. In this article, an analysis of in-depth interviews was carried out to determine what motivations students have when leaving classrooms to provide solutions to the new cohorts that enter. The sample consisted of 21 students, including 14 men and 7 women, from different Andalusian universities. A logical minimization analysis was carried out, showing the necessary implications for each of the subjects who participated. Afterwards, frequency and percentage analyses were carried out for each of the dimensions that made up the interview, with success in primary education having the lowest percentage (4.7%) and academic orientation having the highest percentage (90.47%). Full article

Academic Development (AD) represents a fundamental strategy for improving the quality of university teaching in the digital era. This entry proposes a critical analysis of technologies supporting AD, examining theoretical models, emerging practices, and contemporary challenges through a systematic review of academic literature. The TPACK (Technological Pedagogical Content Knowledge) framework emerges as a crucial model for the effective integration of educational technologies, while innovative approaches such as blended learning, flipped classroom, and communities of practice demonstrate significant potential in promoting teaching innovation. However, the analysis highlights structural criticalities: resistance to change, lack of institutional recognition, technological pedagogical gaps, and identity tensions related to the teaching role. The concept of “Age of Evidence” orients future perspectives toward evidence-based, personalized, and collaborative programs. The entry concludes with operational recommendations for policymakers and institutions, emphasizing the need for systemic investments that valorize teaching as a core scholarly activity. The original contribution lies in the critical integration of established theoretical frameworks with analysis of post-pandemic transformations and in identifying strategic directions to make universities “transformative” in addressing global challenges of sustainability, technological innovation, and critical thinking education. Full article

This paper investigates the knowledge, attitudes, and practical adherence to the Mediterranean Diet (MD) among students of the Teacher Education Study Program in Split. Recent trends indicate a decline in adherence within Mediterranean regions, a phenomenon known as the Mediterranean paradox. Studying the relationship between students’ knowledge and practice is critical within the context of SDG 3 and SDG 4, as it highlights the role of future educators in promoting healthy communities. A quantitative approach was employed using the MDNK methodology, supplemented with the MEDAS test, to assess adherence to the Mediterranean Diet. Statistical analysis included p-values and effect size measures to assess practical relevance. Students averaged 13.39/18 on the MDNK test, with most showing moderate MEDAS adherence and only 5 reaching high adherence. The year of study or employment status has not been shown as an influential factor. While most students possess a high level of knowledge on the MD’s key components and express a positive attitude toward this dietary pattern, a significant knowledge-practice gap exists, confirming the Mediterranean paradox among future teachers. The need for nutritional and food education within the university curriculum is essential to move beyond theoretical instruction and actively promote food literacy and practical skills. Full article

Background and Objectives: Metabolic syndrome (MetS) is a complex condition marked by insulin resistance, central obesity, dyslipidemia, and chronic inflammation. Emerging evidence highlights the roles of hypoxia and mitochondrial stress in its pathophysiology. Hypoxia-inducible factor-1 alpha (HIF1α) and the mitophagy-associated proteins BNIP3 and BNIP3L are key components of hypoxia-responsive mitochondrial stress signaling. This study aimed to evaluate the circulating levels of HIF1α, BNIP3, and BNIP3L in MetS and to explore their associations with metabolic and inflammatory parameters. Materials and Methods: Serum concentrations of HIF1α, BNIP3, and BNIP3L were measured by ELISA in 40 patients with MetS and 40 age and sex-matched controls. Biochemical, hematological, and anthropometric parameters were assessed, and receiver operating characteristic (ROC) analyses were performed to evaluate diagnostic performance. Results: Serum levels of HIF1α, BNIP3, and BNIP3L levels were significantly higher in MetS patients compared with controls (p = 0.001). ROC analysis demonstrated strong diagnostic potential, particularly for BNIP3 (AUC = 0.928), followed by HIF1α (AUC = 0.885) and BNIP3L (AUC = 0.770). These markers showed significant associations with metabolic indicators such as BMI, fasting glucose, triglycerides, and inflammatory markers. Conclusions: The coordinated upregulation of circulating HIF1α, BNIP3, and BNIP3L in MetS is associated with metabolic dysregulation and systemic inflammation, reflecting alterations in hypoxia-responsive mitophagy-associated signaling rather than direct functional impairment of mitophagy. These findings support the potential relevance of these markers as indicators of metabolic stress in MetS. Further tissue-based and mechanistic studies are warranted to clarify their role in disease pathophysiology. Full article

Even though environmental health and climate change are rapidly intensifying the severity of determinants of disease and inequity, training for health professionals in these areas remains fragmented across Europe. To address this gap, the European Medical Association (EMA), in collaboration with the European Network on Climate and Health Education (ENCHE), the International Network on Public Health and Environment Tracking (INPHET) and University College London, convened a one-day hybrid roundtable in London on 17 September 2025, focused on “Preparing Health Professionals for Environmental Health and Climate Change: A Challenge for Europe”. The programme combined keynote presentations on global and European policy, health economics and curriculum design with three disease-focused roundtables (respiratory, cardiovascular and neurological conditions), each examining the following topics: (A) climate and environment as preventable causes of disease; (B) healthcare as a source of environmental harm; and (C) capacity building through education and training. Contributors highlighted how environmental epidemiology, community-based prevention programmes and sustainable clinical practice can be integrated into teaching, illustrating models from respiratory, cardiovascular, surgical and neurological care. EU-level speakers outlined the policy framework (European Green Deal, Zero Pollution Action Plan and forthcoming global health programme) and tools through which professional and scientific societies can both inform and benefit from European action on environment and health. Discussions converged on persistent obstacles, including patchy national commitments to decarbonising healthcare, isolated innovations that are not scaled and curricula that do not yet embed sustainability in examinable clinical competencies. The conference concluded with proposals to develop an operational education package on environmental and climate health; map and harmonise core competencies across undergraduate, postgraduate and Continuing -professional-development pathways; and establish a permanent EMA-led working group to co-produce a broader position paper with professional and scientific societies. This conference report summarises the main messages and is intended as a bridge between practice-based experience and a formal EMA position on environmental-health training in Europe. Full article

Background/Objectives: The growing integration of Artificial Intelligence (AI) and chatbots in health professional education offers innovative methods to enhance learning and clinical preparedness. This study aimed to evaluate the educational impact and perceptions in university students of Human Nutrition and Dietetics, regarding the utility, usability, and design of the E+DIEting_Lab chatbot platform when implemented in clinical nutrition training. Methods: The platform was piloted from December 2023 to April 2025 involving 475 students from multiple European universities. While all 475 students completed the initial survey, 305 finished the follow-up evaluation, representing a 36% attrition rate. Participants completed surveys before and after interacting with the chatbots, assessing prior experience, knowledge, skills, and attitudes. Data were analyzed using descriptive statistics and independent samples t-tests to compare pre- and post-intervention perceptions. Results: A total of 475 university students completed the initial survey and 305 the final evaluation. Most university students were females (75.4%), with representation from six languages and diverse institutions. Students reported clear perceived learning gains: 79.7% reported updated practical skills in clinical dietetics and communication were updated, 90% felt that new digital tools improved classroom practice, and 73.9% reported enhanced interpersonal skills. Self-rated competence in using chatbots as learning tools increased significantly, with mean knowledge scores rising from 2.32 to 2.66 and skills from 2.39 to 2.79 on a 0–5 Likert scale (p < 0.001 for both). Perceived effectiveness and usefulness of chatbots as self-learning tools remained positive but showed a small decline after use (effectiveness from 3.63 to 3.42; usefulness from 3.63 to 3.45), suggesting that hands-on experience refined, but did not diminish, students’ overall favorable views of the platform. Conclusions: The implementation and pilot evaluation of the E+DIEting_Lab self-learning virtual patient chatbot platform demonstrate that structured digital simulation tools can significantly improve perceived clinical nutrition competences. These findings support chatbot adoption in dietetics curricula and inform future digital education innovations. Full article

Artificial Intelligence (AI) systems typically inherit biases from their training data, leading to discriminatory outcomes that undermine equity and inclusion. This issue is particularly significant when popular Generative AI (GAI) applications are used in educational contexts. To respond to this challenge, the study evaluates the effectiveness of dialogic reflection-based training for educators in identifying and mitigating biases in AI. Furthermore, it considers how these sessions contribute to the advancement of algorithmic justice and inclusive practices. A key component of the proposed training methodology involved equipping educators with the skills to design inclusive prompts—specific instructions or queries aimed at minimizing bias in AI outputs. This approach not only raised awareness of algorithmic inequities but also provided practical strategies for educators to actively contribute to fairer AI systems. A qualitative analysis of the course’s Moodle forum interactions was conducted with 102 university professors and graduate students from diverse regions of the Dominican Republic. Participants engaged in interactive activities, debates, and practical exercises addressing AI bias, algorithmic justice, and ethical implications. Responses were analyzed using Atlas.ti across five categories: participation quality, bias identification strategies, ethical responsibility, social impact, and equity proposals. The training methodology emphasized collaborative learning through real case analyses and the co-construction of knowledge. The study contributes a hypothesis-driven model linking dialogic reflection, bias awareness, and inclusive teaching, offering a replicable framework for ethical AI integration in higher education. Full article

Digital technologies have become increasingly crucial during and, after the COVID-19 pandemic, have sparked significant scientific interest around their impact on sustainable well-being. Despite extensive research, conclusive evidence on whether digital technologies enhance or undermine sustainable well-being remains elusive. Saudi Arabia has made significant progress in its technological infrastructure, but comprehending the implications of this progress still poses a challenge. Drawing on the prior literature and grounded in the theoretical perspective of the Capability Approach, this study proposes five dimensions of digital inclusion (accessibility, usability, digital skills, affordability, and connectivity) and examines their collective influence on students’ sustainable well-being, specifically happiness and life satisfaction. This study employs a cross-sectional design, with data collected from 238 university students in Saudi Arabia using convenience sampling. Ten hypotheses were tested using partial least squares structural equation modeling in SmartPLS-4. This study supports the conceptualization of digital inclusion as a multidimensional construct comprising five key dimensions. The results indicate that affordability, usability, connectivity, and digital skills have a substantial impact on happiness, whereas accessibility, usability, connectivity, and digital skills have a considerable effect on life satisfaction. Nonetheless, the correlations between accessibility and happiness, as well as between affordability and life satisfaction, were not found to be supported. This implies that these dimensions might have different effects on the affective and cognitive aspects of sustainable well-being. These results suggest that digital inclusion may play a role in shaping individuals’ interactions with technology and their perceived sustainable well-being. This study proposes and evaluates a strategic framework that may guide efforts to promote digital inclusion and support sustainable well-being among university students. It provides valuable insights for policymakers, educational institutions, and industry stakeholders seeking to enhance digital access and capabilities. The findings highlight the potential value of developing strategies that address students’ digital needs as part of a holistic approach to sustainable well-being. The findings also highlight the importance of viewing digital inclusion as an interconnected framework, rather than as a set of discrete, unrelated factors. By demonstrating how digital inclusion promotes sustainable well-being, this study contributes to the broader sustainability agenda by highlighting digital equity as an essential component of socially sustainable development in the Saudi context. Full article

Large Language Models (LLMs) are increasingly used in higher education, but their pedagogical role in fields like cybersecurity remains under-investigated. This research explores integrating LLMs into a university cybersecurity course using a designed pedagogical approach based on active learning, problem-based learning (PBL), and computational thinking (CT). Instead of viewing LLMs as definitive sources of knowledge, the framework sees them as cognitive tools that support reasoning, clarify ideas, and assist technical problem-solving while maintaining human judgment and verification. The study uses a qualitative, practice-based case study over three semesters. It features four activities focusing on understanding concepts, installing and configuring tools, automating procedures, and clarifying terminology, all incorporating LLM use in individual and group work. Data collection involved classroom observations, team reflections, and iterative improvements guided by action research. Results show that LLMs can provide valuable, customized support when students actively engage in refining, validating, and solving problems through iteration. LLMs are especially helpful for clarifying concepts and explaining procedures during moments of doubt or failure. Still, common issues like incomplete instructions, mismatched context, and occasional errors highlight the importance of verifying LLM outputs with trusted sources. Interestingly, these limitations often act as teaching opportunities, encouraging critical thinking crucial in cybersecurity. Ultimately, this study offers empirical evidence of human–AI collaboration in education, demonstrating how LLMs can enrich active learning. Full article

Growing interest in food sustainability education aims to increase awareness of food distribution systems, environmental degradation, and the connectivity of sustainable and ethical food practices. However, recent scholarship has questioned whether such pedagogical efforts are meaningfully internalized by students or lead to sustained behavioral change. Prior studies document persistent gaps in students’ understanding of sustainability impacts and the limited effectiveness of existing instructional approaches in promoting transformative engagement. To address these concerns, the Food Ethics, Sustainability and Alternatives (FESA) course was implemented with 21 undergraduate and graduate students at Montclair State University (Montclair, NJ, USA). Course outcomes were evaluated using a mixed-methods design integrating qualitative analysis with quantitative measures informed by the Theory of Planned Behavior, to identify influences on students’ attitudes, and a Transtheoretical Model (TTM) panel survey to address progression from awareness to action, administered pre- and post-semester. Qualitative findings revealed five central themes: increased self-awareness of food system contexts, heightened attention to animal ethics, the importance of structured classroom dialogue, greater recognition of food waste, and increased openness to alternative food sources. TTM results indicated significant reductions in contemplation and preparation stages, suggesting greater readiness for change, though no significant gains were observed in action or maintenance scores. Overall, the findings suggest that while food sustainability education can positively shape student attitudes, the conversion of attitudinal shifts into sustained behavioral change remains limited by external constraints, including time pressures, economic factors, culturally embedded dietary practices, structural tensions within contemporary food systems, and perceptions of limited individual efficacy. Full article

Higher education continues to encounter the challenge of redesigning writing pedagogy beyond the rapid adoption of emerging technologies. This challenge is particularly evident in English writing courses, which play a role in developing students’ writing and research skills in universities across the United Arab Emirates (UAE). While generative artificial intelligence (GenAI) tools offer practical affordances for writing instruction, their growing use has also raised concerns about academic integrity, authenticity, and critical engagement. Although early discourse has focused on the risks and potential of GenAI, there remains a clear dearth of frameworks to guide instructors in designing meaningful and engaging writing assignments. This paper introduces Ai-RACE, an adaptable pedagogical framework for designing purposeful and innovative writing assignments. Grounded in classroom-based insights, principles of writing pedagogy, constructivist and multimodal learning theories, Ai-RACE conceptualises assignment design around five interconnected components: AI integration, Relevance, Authenticity, the 4Cs, and Engagement. Employing a design-focused qualitative approach, the study uses classroom implementation and student reflections to examine the implementation of Ai-RACE in writing contexts. Although situated within a specific institutional context, the study offers transferable guidelines for designing writing assignments across international higher education settings. By positioning Ai-RACE as a design heuristic, the study highlights its significance in supporting engagement, critical thinking, writing skills and ethical use of AI, and highlights the importance of rethinking writing pedagogy and the role of professional development in AI- influenced contexts. Full article

With the advancement of artificial intelligence and educational informatization, there is a growing demand for intelligent teaching assistance systems in universities. Focusing on the university “Algorithms” course in the computer science department, this study develops a multi-terminal collaborative knowledge service system, Course-Oriented Knowledge Service–Based AI Teaching Assistant System (CKS-AITAS), which consists of a PC terminal and a mobile terminal, where the PC terminal integrates functions including knowledge graph, semantic retrieval, intelligent question-answering, and knowledge recommendation. While the mobile terminal enables classroom check-in and teaching interaction, thus forming a closed-loop platform for teaching organization, resource acquisition, and knowledge inquiry. For the document retrieval module, paragraph-level semantic modeling of textbook content is conducted using Word2Vec, combined with approximate nearest neighbor indexing, and this module achieves an MRR@10 of 0.641 and an average query time of 0.128 s, balancing accuracy and efficiency; the intelligent question-answering module, based on a self-built course FAQ dataset, is trained via the BERT model to enable question matching and answer retrieval, achieving an accuracy rate of 86.3% and an average response time of 0.31 s. Overall, CKS-AITAS meets the core teaching needs of the course, provides an AI-empowered solution for university teaching, and boasts promising application prospects in facilitating education sustainability. Full article

While artificial intelligence (AI) has advanced urban land use classification, its application in high-stakes decision making, such as urban planning, demands not only high accuracy but also transparency and interpretability. This study evaluates the potential of Google Satellite Embeddings (GSE), a ready-to-use dataset of AI-generated numerical features that capture deep land cover characteristics, for land use classification in the central urban area of Nanning in 2022. A synergistic analytical framework was constructed by integrating the 64 high-dimensional features of GSE data with the feature attribution of Shapley Additive Explanations (SHAP), merging deep learning features with explainable machine learning. The results demonstrate that the XGBoost model (OA = 85.00% ± 2.24%) significantly outperformed the Random Forest (RF) model (OA = 81.87% ± 1.72%) overall. Key abstract features were successfully interpreted as comprehensible geographic semantics, with A51 and A36 corresponding to built-up intensity and vegetation cover, respectively. Moreover, XGBoost enabled more refined decisions than Random Forest (RF) due to its superior ability to distinguish between functionally distinct classes that have similar physical appearances. This framework provides a scalable and transferable analytical solution for the challenges of feature limitations and insufficient model transparency in urban land use classification. Full article