Search Results (5,427)

This paper applies a hybrid multi-criteria decision-making (MCDM) model that integrates the Analytic Hierarchy Process (AHP) for structured weighting of evaluation criteria with the Net Worth Analysis (NWA) method for value-based aggregation of scores. The proposed framework was employed to evaluate Learning Management Systems (LMS) in higher education, involving two independent expert panels representing management and IT perspectives. Results of the AHP analysis show that cost (28%), security (22%), and usability (17%) are the most influential criteria in the decision-making process, reflecting institutional priorities for financial efficiency, safety and ease of use. Based on the combined AHP-NWA model, Moodle 4.3 emerged as the most sustainable choice (0.586), followed by Atutor 2.2.1 (0.541) and Blackboard (SaaS edition) (0.490). The inclusion of sensitivity and scenario analyses confirmed the robustness of the model, demonstrating that the ranking of alternatives remains stable under variations in weighting factors and different strategic priorities. By framing LMS evaluation within the context of sustainable digital transformation, the study emphasizes how transparent and systematic decision-making supports long-term institutional resilience and aligns with the principles of Education for Sustainable Development (ESD). In addition, the framework contributes to the achievement of Sustainable Development Goal 4 (Quality Education), by guiding higher education institutions toward inclusive, resilient and cost-effective digital solutions. Full article

Children represent a large proportion of the world’s refugees. The United Nations High Commissioner for Refugees (UNHCR) reported that as of 2020, there were about 27.1 million refugees worldwide, and roughly half of all refugees were under the age of 18 at any given time. The challenges that refugee children face prior to resettlement include interrupted education, repeated moves, exposure to violence, family separation, lengthy stays in camps, and poverty or deprivation. As a result of the experiences gained from an unexpected relocation, being the child of an adult refugee may be traumatic. But it is more damaging when laws enacted in the new host countries fail to take refugee children into account, which in turn could result in socioeconomic harm or gain for these children. In this policy analysis, the researcher intends to look at the socioeconomic outcomes of refugee children while trying to navigate their new home country. In essence, this analysis will use the multiple streams analysis framework to understand how refugee policies in the United States and Nigeria are enacted and their socioeconomic impact on refugee children. Full article

Indoor Air Quality (IAQ) in educational environments is a critical determinant of students’ health, well-being, and learning performance, with inadequate ventilation and pollutant accumulation consistently associated with respiratory symptoms, fatigue, and impaired cognitive outcomes. Conventional monitoring approaches—based on periodic inspections or subjective perception—provide only fragmented insights and often underestimate exposure risks. Artificial intelligence (AI) offers a transformative framework to overcome these limitations through sensor calibration, anomaly detection, pollutant forecasting, and the adaptive control of ventilation systems. This review critically synthesizes the state of AI applications for IAQ management in educational environments, drawing on twenty real-world case studies from North America, Europe, Asia, and Oceania. The evidence highlights methodological innovations ranging from decision tree models integrated into large-scale sensor networks in Boston to hybrid deep learning architectures in New Zealand, and regression-based calibration techniques applied in Greece. Collectively, these studies demonstrate that AI can substantially improve predictive accuracy, reduce pollutant exposure, and enable proactive, data-driven ventilation management. At the same time, cross-case comparisons reveal systemic challenges—including sensor reliability and calibration drift, high installation and maintenance costs, limited interoperability with legacy building management systems, and enduring concerns over privacy and trust. Addressing these barriers will be essential for moving beyond localized pilots. The review concludes that AI holds transformative potential to shift school IAQ management from reactive practices toward continuous, adaptive, and health-oriented strategies. Realizing this potential will require transparent, equitable, and cost-effective deployment, positioning AI not only as a technological solution but also as a public health and educational priority. Full article

Humanity is opening up to cosmos in all its dimensions and areas of knowledge. In this context, Paraguay, due to its multicultural uniqueness and two official languages (Spanish and Guaraní), represents an emblematic example of how legends, traditions and its rich mythology are important in their sociocultural translation to space. They coexist as a link between the past and the future. Guarani traditions, mythology, their relationship with nature and their translation into cosmos are amazing and complex aspects of indigenous cultural heritage, which are still present in many Paraguayan initiatives. This article compiles and integrates the cultural information about this topic, which is dispersed in different sources, and frames it in its corresponding context. Likewise, it unequivocally confirms how this intangible heritage is crucial as a living roadmap and a contemporary challenge that should be preserved as it guides individuals, communities and initiatives to implement earth and space science and education. Guaraní cultural heritage offers valuable insights into how indigenous worldviews continue to shape contemporary ecological and cultural practices in our modern intersection pathway to the cosmos. Full article

Education can play a pivotal role in the eradication of energy poverty by facilitating the transfer of knowledge and skills to all interested stakeholders whilst also promoting the adoption of sustainable energy solutions. In the context of this paper, a comprehensive review of European master’s programs related to energy poverty is carried out, resulting in the identification of approximately of 100 programs across seven European countries that either explicitly or implicitly address the topic. In most cases, energy poverty is embedded in a broader academic discipline—such as energy systems, renewable energy, or sustainable development—rather than being treated as a standalone field. In Europe, the United Kingdom, France, Greece, and Romania were singled out as the leading contributors to energy poverty education. Within the framework of the EU-funded project “MSc in Energy Poverty Alleviation Technologies”, implemented in collaboration with South African universities, this study focuses on South Africa, which represents a characteristic example of a country facing high levels of energy poverty and significant inequalities in energy access. This work highlights the critical need for targeted academic curricula specifically designed to bridge the persistent gap between academic research and its real-world applications, particularly in regions of the world where such integration is most urgent. It also emphasizes the essential role of linking STEM education with the social and humanitarian sciences. Finally, this work underscores the need for interdisciplinary approaches that connect energy poverty alleviation and education by additionally expanding the research and documentation of relevant good initiatives from Asia (China). Full article

Acid-base analysis is a high-load diagnostic skill that many medical students struggle to master when taught using traditional text-based flowcharts. This multi-institution mixed-methods study evaluated a novel visual mnemonic algorithm that integrated Medimon characters, symbolic imagery, and pop-culture references into the standard acid-base diagnostic framework. First-year medical students (n = 273) at six distributed WWAMI campuses attended an identical lecture on acid-base physiology. Students at five control campuses received the original text-based algorithm, while students at one experimental campus received the Medimon algorithm in addition. Achievement was measured with a unit exam (nine focal items, day 7) and a final exam (four focal items, day 11). A Differences-in-Differences approach compared performance on focal items versus baseline items across sites. Students at the experimental campus showed no significant advantage on the unit exam (DiD = +1.2%, g = 0.12) but demonstrated a larger, but still non-significant, medium-to-large effect on the final exam (DiD = +11.0%, g = 0.85). At the experimental site, 39 students completed the Situational Interest Survey for Multimedia (SIS-M), revealing significantly higher triggered, maintained-feeling, maintained-value, and overall situational interest scores for the Medimon algorithm (all p < 0.001). Thematic analysis of open-ended responses identified four themes: enhanced clarity, improved memorability, increased engagement, and barriers to interpretation. Collectively, the findings suggest that embedding visual mnemonics and serious-game characters into diagnostic algorithms can enhance learner interest and may improve long-term retention in preclinical medical education. Full article

(1) Background: While Hong Kong is renowned for being a multicultural city, its South and Southeast Asian population has experienced disadvantages in various aspects of life, particularly career development. This study adopts the Systems Theory Framework (STF) to investigate the school-to-work transition of Pakistani, Nepalese, Filipino, and Indian youths in the Hong Kong Chinese context. (2) Methods: A qualitative approach using individual and focus group interviews was employed to uncover and critically examine educational and career aspirations and contextual factors in the transition pathways of educational and career advancement experienced by these ethnic groups. (3) Results: Findings show that career aspirations among South and Southeast Asian youths undergoing the school-to-work transition are comparatively lower than those of their counterparts who remain in secondary education. This disparity is attributed to a range of contextual factors, particularly shortcomings in education policy and limited cultural competence within Hong Kong Chinese society, both of which contribute to the erosion of occupational outlook among these underrepresented groups. (4) Conclusions: This study demonstrates the critical impact of contextual factors on the ethnic inequality of school-to-work transition, which are more overwhelming than can be overcome by personal and family efforts. If these issues are not addressed, achieving racial equality and equal opportunity in school-to-work transition will remain a persistent challenge. Full article

Computational thinking (CT) is recognized as a core competency for the 21st century, and its development is shaped by multiple factors, including students’ individual characteristics and their use of information and communication technology (ICT). Drawing on large-scale international data from the 2023 cycle of the International Computer and Information Literacy Study (ICILS), this study analyzes a sample of 81,871 Grade 8 students from 23 countries and one regional education system who completed the CT assessment. This study is the first to apply a predictive modeling framework that integrates two machine learning techniques to systematically identify and explain the key variables that predict CT and their nonlinear effects. The results reveal that various student-level predictors—such as educational expectations and the number of books at home—as well as ICT usage across different contexts, demonstrate significant nonlinear patterns in the model, including U-shaped, inverted U-shaped, and monotonic trends. Compared with traditional linear models, the SHapley Additive exPlanations (SHAP)-based approach facilitates the interpretation of the complex nonlinear effects that shape CT development. Methodologically, this study expands the integration of educational data mining and explainable artificial intelligence (XAI). Practically, it provides actionable insights for ICT-integrated instructional design and targeted educational interventions. Future research can incorporate longitudinal data to explore the developmental trajectories and causal mechanisms of students’ CT over time. Full article

Conventional innovation management methodologies (IMMs) often struggle to respond to the complexity, uncertainty, and cognitive diversity that characterise contemporary innovation projects. This study introduces Innovation Flow (IF), a human-centred and adaptive framework grounded in Flow Theory and enhanced by Generative Artificial Intelligence (GenAI). At its core, IF operationalises Personalised Innovation Techniques (PInnTs)—adaptive variations of established methods tailored to project genetics and team profiles, generated dynamically through a GenAI-based system. Unlike traditional IMMs that rely on static toolkits and expert facilitation, Innovation Flow (IF) introduces a dynamic, GenAI-enhanced system capable of tailoring techniques in real time to each project’s characteristics and team profile. This adaptive model achieved a 60% reduction in ideation and prototyping time while maintaining high creative performance and autonomy. IF thus bridges the gap between human-centred design and AI augmentation, providing a scalable, personalised, and more inclusive pathway for managing innovation. Using a mixed-methods design that combines grounded theory with quasi-experimental validation, the framework was tested in 28 innovation projects across healthcare, manufacturing, and education. Findings show that personalisation improves application fidelity, engagement, and resilience, with 87% of cases achieving high efficacy. GenAI integration accelerated ideation and prototyping by more than 60%, reduced dependence on expert facilitators, and broadened participation by lowering the expertise barrier. Qualitative analyses emphasised the continuing centrality of human agency, as the most effective teams critically adapted rather than passively adopted AI outputs. The research establishes IF as a scalable methodology that augments, rather than replaces, human creativity, accelerating innovation cycles while reinforcing motivation and autonomy. Full article

Background/objectives: The nutrition care process (NCP) is an evidence-based practice framework used in Medical Nutrition Therapy for the prevention, treatment, and management of non-communicable chronic health conditions. This review aimed to explore available artificial intelligence (AI)-integrated technologies across the NCP in dietetic primary care, their uses, and their impacts on the NCP and patient outcomes. Method: Six databases were searched: MEDLINE, Embase, PsycINFO, Scopus, IEEE, and ACM digital library. Eligible studies were published between January 2007 and August 2024 and included human adult studies, AI-integrated technologies in the dietetic primary care setting, and patient-related outcomes. Extracted details focused on participant characteristics, dietitian involvement, and the type of AI system and its application in the NCP. Results: Ninety-seven studies were included. Three different AI systems (image or audio recognition, chatbots, and recommendation systems) were found. These were implemented in web-based or smartphone applications, wearable sensor systems, smart utensils, and software. Most AI-integrated technologies could be incorporated into one or more NCP stages. Seventy-nine studies reported user- or patient-related outcomes, with mixed findings, but all highlighted efficiencies of using AI. Higher patient engagement was observed with Chatbots. Seventeen studies raised concerns encompassing ethics and patient safety. Conclusions: AI systems show promise as a clinical support tool across most stages of the NCP. Whilst they have varying degrees of accuracy, AI demonstrates potential in improving efficiency, supporting personalised nutrition, and enhancing chronic disease management outcomes. Integrating AI education into dietetic training and professional development will be essential to ensure safe and effective use in practice. Full article

This study presents the initial validation of the IMPACT model, a psychometric tool developed to evaluate how university faculty adopt ChatGPT in higher education. It specifically addresses the existing gap in validated instruments designed for educators, as most prior research has focused on student-based adoption models. A total of 206 professors completed a 39-item Likert-scale questionnaire. Exploratory factor analysis using principal axis factoring with oblimin rotation identified the underlying structure of the instrument. Reliability and internal consistency were examined through Cronbach’s alpha and McDonald’s omega. The analysis revealed a five-factor structure comprising functional appropriation, ethical and academic concerns, cost and accessibility, facilitating conditions, and perceived reliability and trustworthiness. Intention to use and performance expectancy merged into a single factor, and social influence did not emerge as a determinant. The model demonstrated strong reliability and internal consistency across all dimensions. The IMPACT model offers a validated framework for understanding faculty adoption of ChatGPT, emphasizing functional, ethical, and infrastructural factors over social influence. These findings provide a foundation for confirmatory analyses and contribute to advancing theoretical and practical insights into AI integration in higher education teaching. Full article

Effective classification and progress monitoring are central to inclusive education, ensuring that students with learning challenges receive timely and appropriate support. However, both international research and Qatari educators’ experiences reveal inconsistencies, limited resources, and a persistent gap between policy and practice. This qualitative study explored the perspectives of 20 stakeholders, including teachers, school leaders, coordinators, and policymakers. Thematic analysis conducted using ATLAS.ti 25 produced six main themes: inconsistent classification; staff and resource shortages; family resistance and collaboration; policy and accommodation gaps; fragmented monitoring; and innovative, inclusive practices. Participants described over-reliance on external diagnostic reports, inconsistent eligibility criteria, limited access to specialists, overcrowded classrooms, and insufficient early screening. Disconnected tools and the lack of a centralized data system hindered monitoring. Despite these barriers, educators showed adaptability through classroom-based interventions, behavioral support, and the emerging use of digital and AI tools. Stake-holders emphasized the need for a unified national framework, systematic early screening, expanded accommodations, integrated Education Management Information System (EMIS) records, and continuous professional development with parent involvement. Findings highlight that classification and monitoring depend on governance, capacity, and data culture, underscoring the need for coordinated policy and practice to achieve equitable, sustainable inclusion in Qatar. Full article

Teaching in a globalised world offers opportunities to reduce barriers, enhance understanding, and expand access for diverse learners. Blended approaches, combining in-person and online delivery, can encourage learning communities across geographical boundaries. However, disparities in access to technology, internet reliability, and conducive study environments highlight inequalities and varied learner experiences. While digital networks may support identity and belonging, some students report feeling distracted or disengaged in online settings. This study explores the experiences of first-year doctoral candidates completing the final taught module of their Doctorate in Business Administration (DBA) at the University of Gloucestershire, UK. Participants, mostly international students now based in the UK, are engaged in both in-person classes and online staff-led webinars. Data was gathered through four in-person focus groups. Engeström’s Cultural-Historical Activity Theory was applied as an analytical framework, conceptualising teaching and learning as an activity system mediated by Tools, Rules, Community, and Division of Labour. This enabled a comparison of students’ experiences in online and in-person contexts. The findings revealed contradictions within the system, identifying barriers to engagement and adaptation, and offering insights into the evolving pedagogical demands of blended doctoral education. Full article

As conversational artificial intelligence (CAI), including smart speakers, social robots, and dialogic learning apps, becomes increasingly present in children’s lives, its potential to support early mathematical thinking warrants closer attention. While existing research largely concentrates on literacy and language development, the role of CAI in early numeracy remains underexplored. This paper examines how voice-based CAI might contribute to informal mathematical thinking in early childhood. Adopting a conceptual lens, this paper synthesises existing theory and research to examine the potential roles of CAI in early mathematical learning. Grounded in sociocultural theory and dialogic pedagogy, this paper positions CAI as a potential mediator of early mathematical thinking through responsive dialogue. Four interrelated dimensions (child agency, cognitive scaffolding, mathematical language quality, and responsiveness and timing) are identified as a conceptual lens for evaluating how dialogue-based interactions with CAI may support or constrain young children’s mathematical thinking. Rather than framing CAI as a direct teaching tool, this paper explores its potential role as a dialogic partner in play-based numeracy and inquiry. The framework contributes to early mathematics education by highlighting both the promise and the limitations of CAI, offering guidance for research, technology design, and educational practice. It underscores the need for intentional, ethically informed integration of CAI that approximates the qualities of human dialogue while acknowledging current constraints in real-world use. Full article

The rapid integration of artificial intelligence technologies into higher education presents unprecedented opportunities for enhancing computational thinking development while simultaneously raising significant concerns about educational equity and algorithmic bias. This comprehensive review examines the intersection of AI integration, computational thinking pedagogy, and diversity, equity, and inclusion imperatives in higher education through a comprehensive narrative review of 167 sources of current literature and theoretical frameworks. From distilling principles from Human–AI Symbiotic Theory (HAIST) and established pedagogical integration models, this review synthesizes evidence-based strategies for ensuring that AI-enhanced computational thinking environments advance rather than undermine educational equity. The analysis reveals that effective AI integration in computational thinking education requires comprehensive frameworks that integrate ethical AI governance with pedagogical design principles, creating practical guidance for institutions seeking to harness AI’s potential while protecting historically marginalized students from algorithmic discrimination. This review contributes to the growing body of knowledge on responsible AI implementation in educational settings and provides actionable recommendations for educators, researchers, and policymakers working to create more effective, engaging, and equitable AI-enhanced learning environments. Full article