Search Results (303)

This study investigates the relationship between digital leadership dimensions and school effectiveness in a rural school district in South Africa. This study advances the research frontier, which is predominantly focused on urban and well-resourced environments, by providing actual information from rural schools where infrastructural and contextual obstacles remain significant. A quantitative study design was employed, utilizing a census survey of 107 educators from primary and secondary public schools in a rural area of the Western Cape Province. The Digital Leadership and School Effectiveness Questionnaire (DLSEQ) was used to collect data. It measures five dimensions of digital leadership: visionary leadership, digital learning culture, professional development, systemic improvement, and digital citizenship. Partial Least Squares Structural Equation Modelling (PLS-SEM) using SmartPLS 4 was employed to assess both measurement and structural models. The findings reveal that digital citizenship and professional development were significant positive predictors of school effectiveness, whereas digital learning culture showed a significant negative relationship. Visionary leadership and systemic improvement were not significant predictors. Collectively, the five digital leadership dimensions explained 81.9% of the variance in school effectiveness. The study contributes theoretically by integrating Transformational Leadership Theory and the Technology Acceptance Model within the context of rural education. Practical implications advocate for context-sensitive digital leadership strategies to enhance school effectiveness in under-resourced rural environments. Full article

Simulation-based learning (SBL) is increasingly used within Initial Teacher Education (ITE) to bridge the gap between theory and practice, enhancing pre-service teachers’ (PSTs) preparedness for the complexities of classroom practice. Despite its growing adoption, limited research has examined how simulation design shapes PSTs’ learning experiences. This study addresses this gap by exploring PSTs’ experiences of two low-technology simulation modalities, mixed-media and multiple-choice formats, implemented within undergraduate primary ITE programmes at two UK universities. Using a sequential mixed-methods design, quantitative data were collected from 249 PSTs through the Educational Practices Questionnaire for Teacher Educators (EPQ-TE) and the Preparing Educators for Practice in Simulation Questionnaire (PEPS-Q), alongside qualitative data from open-text survey responses and focus groups. Findings indicate that PSTs reported high levels of perceived quality, engagement, and preparedness across both modalities, with no statistically significant differences between formats or institutions. Reflexive thematic analysis was used to explore simulation design features valued by PSTs, identifying three key themes: authenticity and realism, the benefits and challenges of peer collaboration, and the role of scaffolding and feedback in supporting professional learning. These findings suggest that learning in SBL emerges through the interaction of scenario design, learner participation, and tutor facilitation, offering practical insights for teacher educators seeking to design and implement simulation-based learning within ITE, as well as recommendations for future research. Full article

Globalization and international trade have increased the importance of customs authorities in ensuring national security. However, regulatory differences regarding substances such as cannabis derivatives, the emergence of new psychoactive substances (NPSs), and the limitations of detection technology challenge customs in identifying suspicious cross-border goods. Traditional attribute extraction methods struggle with professional terminology and cross-sentence reasoning, making it difficult to regulate unknown or emerging substances. To address this, we propose a generative question answering (QA) framework based on inverse reinforcement learning (IRL) that converts attribute extraction into natural language QA tasks. Our approach, CAESAR (Chemical-Attribute Extraction with Sub-rewArd Reinforcement), uses a customs database to match known profiles and cross-references extracted attributes with benchmarks to enhance detection. It integrates the BioBART model with multi-objective reward optimization, using QA templates to capture implicit attributes. IRL automates the learning of reward weights from expert annotations. Experiments show that CAESAR achieves a competitive F1 score of 77.82 on explicit attributes and obtains the highest BLEU score and the lowest perplexity among the compared generative methods. For implicit attributes, ROUGE-L and BLEU scores are 43.08 and 44.46, respectively, with a perplexity of 11.3. These results are obtained in an open-ended generative QA setting rather than a closed-set classification setting, indicating that the proposed framework can provide practically useful attribute-level evidence for customs-oriented risk pre-screening and expert-assisted prioritization. This study offers an efficient solution for mining implicit knowledge in chemical texts and provides insights into multi-objective generative tasks. Full article

Murals are not merely independent visual artworks. Rather, they are an integral part of architectural heritage, directly attached to buildings’ structural elements, such as brick walls and vaults. However, murals are susceptible to various building-related types of damage, including structural cracks and moisture-induced peeling, due to long-term exposure to environmental factors and geological changes. As the progressive deterioration of these murals hastens the loss of mural value, professional assessment and restoration are urgently required. To tackle the issues of low efficiency in traditional structural damage detection and the absence of predictable repair plans, this paper presents a semi-automatic building-mural protection solution that integrates morphological assessment of mural deterioration with computer vision technology. This study establishes an image prediction system that integrates intelligent damage identification with virtual restoration. First, employing the PaddleSeg deep learning framework and the DeepLabv3 semantic segmentation model, this study used existing mural damage datasets to build a recognition model. The model allows for intelligent identification and labeling of multiple damage types. Subsequently, relying on the ComfyUI platform, Stable Diffusion was used to construct a virtual restoration model. LoRA (low-rank adaptation) technology was introduced to fine-tune the model specifically for the mural style, thus enhancing the directivity and accuracy of virtual restoration. Finally, by applying the results of the recognition model to the virtual restoration model, this study built an integrated system for mural damage diagnosis and virtual restoration. The results show that the damage recognition model achieved a mean intersection over union (mIoU) of 47.8% and a pixel accuracy of 77.97% on the test set, validating the feasibility of using semantic segmentation for mural damage detection. This study presents an integrated workflow framework integrating automatic damage identification and intelligent repair. As an expert-assisted tool, this framework shows application potential for preliminary exploration of mural disease diagnosis and virtual restoration plans, providing technical references for the digital protection of cultural heritage. Full article

Purpose: Access to assistive technology (AT) is a fundamental human right and a critical component of Universal Health Coverage (UHC). In the Middle East and North Africa (MENA) region, the scarcity of trained professionals remains a significant barrier to AT service provision. This study evaluates the effectiveness and perceived usefulness of the Assistive Technology Training Program (ATTP), a specialized continuing education initiative designed to build workforce capacity among rehabilitation and healthcare professionals. Methods: A convergent mixed methods design was used to analyze quantitative pre/post-test scores and qualitative focus group open-ended responses. Quantitative data were gathered from 386 participants across 11 MENA countries using a pre- and post-test assessment of AT knowledge. Qualitative utility and participant satisfaction were assessed through a 5-point Likert scale survey evaluating content relevance, trainer expertise, and facilities. Association tests (ANOVA and t-tests) were conducted to identify factors influencing knowledge gain. Results: Participants demonstrated a statistically significant improvement in AT knowledge, with the overall mean score increasing from 3.67 ± 1.13 to 7.50 ± 1.25 (p < 0.001). High levels of satisfaction were reported, with 92% of participants rating the training as “Very Good” or “Excellent” regarding its relevance to clinical needs. Association tests revealed that professional background (p < 0.001), employment status (p = 0.0017), level of education (p = 0.011), and prior training experience (p = 0.026) were significant factors in the magnitude of improvement, although all subgroups achieved significant learning gains. Qualitative thematic analysis per the focus group discussions using the WHO-GATE 5 P framework identified three major themes: (1) Structural Challenges: Issues with Products and Provision point toward a need for better infrastructure and localized supply chains. (2) Human Capital: Personnel barriers emphasize that training shouldn’t just be for professionals, but should extend to caregivers as well. (3) Systemic and Social Change: Policy and People focus on the “soft” side of AT moving toward user-involved guidelines and fighting social stigma to ensure rights are upheld. Conclusions: The ATTP is an impactful educational intervention that significantly enhances the foundational competencies of healthcare professionals in the MENA region. By addressing knowledge gaps and fostering practical skills, the program serves as a preliminary model that demonstrates potential for building regional capacity and supporting the United Nations’ Sustainable Development Goal (SDG) #3 related to health and wellbeing and SDG #4 related to quality education and lifelong learning opportunities for all. Further research is required to evaluate its long-term scalability and clinical impact. Full article

The rapid integration of artificial intelligence (AI) into higher education is reshaping teaching, learning, and assessment, particularly in programming education. While AI coding assistants can enhance feedback, scaffolding, and student engagement, their educational value depends on pedagogical alignment, institutional readiness, and faculty practice, not merely technical capability. Existing adoption frameworks, however, inadequately address these pedagogical and institutional dimensions in domain-specific contexts. This study proposes the AI Coding Assistant Adoption Framework (AICAAF), a theoretically grounded model integrating the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT), and Self-Determination Theory (SDT). The framework was developed iteratively from prior literature and refined through faculty perspectives. It conceptualises adoption across four interrelated dimensions: usability, pedagogical adequacy, institutional readiness, and faculty engagement. Using PyChatAI as an instrumental case study, this qualitative research draws on semi-structured interviews with 15 faculty members teaching programming courses at Jouf University, a public institution in Saudi Arabia operating in a low- to mid-resource context. Data were analysed using reflexive thematic analysis. Findings indicate that PyChatAI is intuitive and beneficial for novice learners, particularly through instant feedback and automated error correction. However, its pedagogical value is limited in advanced and industry-aligned contexts. Institutional barriers, such as inadequate infrastructure, limited technical support, and the absence of policy frameworks, significantly constrain effective integration. Despite this, faculty expressed strong commitment to adopting AI tools, proposing strategies including curriculum redesign, professional development, and gamified instruction. The study reconceptualises AI adoption as a pedagogical and institutional transformation rather than a purely technological shift. The AICAAF provides a robust foundation to guide curriculum design, teaching practice, and policy development for responsible AI integration in programming education. Full article

This study developed and validated a multidimensional instrument to assess teachers’ perceptions and attitudes toward the reuse of treated sewage effluent (TSE) in agriculture, a key sustainability strategy in water-scarce regions such as Qatar and the wider Gulf Cooperation Council (GCC). Using factor-analytic validation procedures on data from 444 teachers, the study confirms a robust five-factor model comprising Perceived Knowledge and Awareness, Attitudes, Pedagogy, Perceived Limitations, and Sources of Knowledge, demonstrating strong psychometric properties. Beyond validation, the findings provide insight into how educators understand and engage with wastewater reuse as an educational and sustainability issue. While teachers generally demonstrate sound conceptual understanding and positive attitudes toward sustainability, persistent concerns related to health risks, food quality, and cultural acceptability reveal a notable cognitive–behavioral gap. Although respondents express strong support for student-centered pedagogical approaches, including experiential learning and technology-enhanced instruction, implementation is often constrained by limited curricular guidance, time pressures, and insufficient professional development. These findings have important implications for sustainability education policy and teacher preparation in arid contexts pursuing water resilience strategies. Recommendations include integrating TSE-related content across disciplines, expanding experiential professional development opportunities, and strengthening curricular frameworks that support applied sustainability education. The validated instrument offers a transferable model for comparable regions globally, enabling cross-cultural research and evidence-based interventions in sustainability education. Full article

The digital transformation has intensified demands for university teachers to develop technology-enhanced teaching competence, especially under China’s High-Quality Course initiative for blended learning excellence. While existing well-recognized frameworks (e.g., TPACK, DigCompEdu) provide valuable foundational guidance, they inadequately capture the dynamic, ecological processes through which teachers systematically reconstruct curricula and professional identities in blended contexts. This study addresses this gap by proposing an ecological model of competence development, building on the strengths of existing frameworks while capturing the dynamic interplay between teachers, technology, and blended environments. Using a qualitative multiple-case design, we conducted semi-structured interviews with six national recognized exemplary university English teachers. Data were analyzed via Braun & Clarke’s six-phase thematic analysis in MaxQDA. Findings reveal that technology-enhanced teaching competence comprises five co-evolving dimensions: Curriculum Empowerment (systematic course redesign), Role Transformation (shifting from lecturer to learning designer), Environment Integration (orchestrating online-offline spaces), Technology Application (selective tool use), and Competence Spanning (transferring expertise across contexts). These dimensions form an ecological system: when teachers redesign curricula, they simultaneously rethink their professional identities; when they adopt technologies, they reshape classroom environments; and when all four dimensions align, higher-order spanning competence emerges naturally. Theoretically, this ecological model advances beyond technology addition by illuminating relational mechanisms and emergent properties of competence. Practically, it informs a shift from fragmented tool-training to systemic faculty support architectures that honor the complexity of blended teaching transformation. Full article

In transport and logistics systems, decision-making is increasingly influenced by uncertainty stemming from demand variability, technological disruptions, and systemic risks present in supply chains. In these contexts, organizations need approaches that are rooted in data and analysis to assess key elements affecting system resilience and performance. Although current studies widely utilize stochastic and fuzzy models for operational decision-making, there has been insufficient focus on the systematic assessment of human-centric system elements—especially competencies—as decision variables in intricate logistics systems. This research proposes an analytical framework for multi-criteria decision-making that is driven by data and aimed at evaluating the significance of various competencies that affect labor market competitiveness and the adaptability of supply chains. The approach combines expert assessment with statistical and information-theoretic metrics, utilizing Kendall’s coefficient of concordance for evaluating consistency, Shannon entropy for analyzing distributional uncertainty, and the Gini coefficient for measuring concentration. This integrated method allows for the measurement of both variability and inequality within decision frameworks in the face of uncertainty. The findings indicate that hands-on experience and professional skills play a crucial role in decision-making structures, whereas the ability to adapt to technological advancements and a commitment to ongoing learning greatly enhance system resilience. The entropy results reveal a significant degree of structural balance in the decision criteria, while the low Gini values affirm a lack of concentration, indicating a distributed and multi-dimensional decision-making environment. The study provides analytical insights into the structure and relative importance of competencies in decision-making contexts related to supply chain resilience. Full article

Integrating artificial intelligence (AI) into design education is no longer optional; it has become an essential tool for enhancing innovative design and preparing students for data-driven practice and rapid technological acceleration. However, ignoring AI risks professional irrelevance; it introduces a range of concerns about students’ cognitive skills and comes with many drawbacks in the education process, as it threatens the attainment of learning outcomes, renders a fair assessment process unachievable, and places academic integrity in a vulnerable position. Using a qualitative case study approach, this research employs semi-structured interviews with 27 senior-year students in the interior design department to gain in-depth academic insights into how AI influenced their design process in their term project and its impact on their cognitive development and decision -making. Instructors’ observations on students’ skills, their pace in the project, and their end-products were documented. This study demonstrates that integrating AI into design education cannot be avoided, making a new paradigm for addressing design education inevitable. Based on the analysis, the paper proposes a conceptual framework outlining key dimensions in teaching and assessing strategies in design education adopting AI, focusing on analysis, critical thinking, reasoning, and process rather than on the end-product and its presentation. Full article

While Artificial Intelligence (AI) approaches date back more than 60 years, there is no doubt that in the last 4 years, we have entered the era of AI. The advanced capabilities of Generative AI (GenAI) and Large Language Models (LLMs) have noticeably reshaped multiple sectors, becoming a driving force in participatory environments. Recent developments in Machine/Deep Learning (ML/DL) and Natural Language Processing (NLP) have enabled the introduction of tools and applications integrated into various professional fields. Areas ranging from education and media to art, tourism, and food science incorporate AI technologies to optimize established workflows, facilitate change, enhance creativity, and foster interaction. The current Special Issue includes nineteen multidisciplinary research works exploring AI in participatory environments, primarily focusing on technologies, ethics, and literacy aspects. Employing diverse methodologies, the research identifies various uses of AI along with the critical ethical and legal risks and challenges they entail. Concerns about inaccuracy, algorithmic bias, data infringements, and the potential erosion of transparency and interpretability need to be addressed in every phase of the design and implementation of AI technologies. Co-creative human-in-the-loop processes and human judgment need to be further strengthened and supported through digital/AI literacy initiatives. In this regard, effective regulatory frameworks, inclusive institutional strategies, and targeted training programs can ensure responsible and trustworthy AI use with a balance between technological evolution and human oversight. Full article

School-based neuromuscular training interventions have the potential to mitigate dynapenia in the paediatric population and enhance movement skill outcomes; however, translating research into practice in primary school settings has been slow due to the expertise and professional learning required for implementation. This review describes the new teacher-supported intervention ‘Kids Innovative Neuromuscular Enhancement & Teacher-supported Instructional Coaching with AI’ (Kinetic AI) and presents evidence supporting its use in primary school settings. The Scale for the Assessment of Narrative Review Articles (SANRA) was used to guide the narrative and conceptual review methodology employed to synthesise peer-reviewed literature on paediatric dynapenia, school-based neuromuscular training, and AI technology-supported instructional models. This synthesis informed the development of a conceptual approach to neuromuscular training delivery in primary schools. The newly developed Kinetic AI conceptual model provides a pathway to embed neuromuscular training within active class breaks, offering adaptive feedback and targeted teacher support to facilitate implementation. This approach has the potential to bridge gaps between research, access, and practice. The Kinetic AI application is designed to support children’s muscular fitness and movement skills through school-based neuromuscular training, while addressing barriers to research translation and teacher expertise. When applied during school breaks, this approach has the potential to reduce the risk of dynapenia and contribute to scalable improvements in paediatric health and wellbeing. Full article

This comprehensive literature review explores the latest advancements in explainable artificial intelligence (XAI) techniques within the field of medical imaging (MI). Over the past decade, machine learning (ML) and deep learning (DL) technologies have made significant strides in healthcare, enabling advancements in tasks such as disease diagnosis, medical image segmentation, and the detection of various medical conditions. However, despite these successes, the widespread adoption of AI-driven tools in clinical practice remains slow, primarily due to the “black-box” nature of many AI models. These models make decisions without transparent reasoning, which poses significant barriers in critical medical and legal environments, where accountability and trust are paramount. This review investigates various XAI methods, focusing on both intrinsic and post-hoc techniques, to evaluate their potential in addressing these challenges. The paper examines how XAI can enhance the transparency of healthcare algorithms, thereby fostering greater trust and confidence among clinicians, patients, and regulators. Key challenges faced by XAI in healthcare, such as limited interpretability, computational complexity, and the absence of standardized evaluation frameworks, are discussed in detail. Furthermore, this work highlights existing gaps in the literature, including the lack of detailed comparative analyses of specific XAI techniques, especially in terms of their mathematical foundations and applicability across diverse medical imaging contexts. In response to these gaps, the paper introduces a new set of standardized evaluation metrics aimed at assessing XAI performance across various medical imaging tasks, such as image segmentation, classification, and diagnosis. The review proposes actionable recommendations for enhancing the effectiveness of XAI in healthcare, with a focus on real-world clinical applications. Unlike previous studies that focus on broader overviews or limited subsets of methods, this work provides a comprehensive comparative analysis of over 18 XAI techniques, emphasizing their strengths, weaknesses, and practical implications. By offering a detailed understanding of how XAI methods can be integrated into clinical workflows, this paper aims to bridge the gap between cutting-edge AI technologies and their practical use in medical settings. Ultimately, the insights provided are valuable for researchers, clinicians, and industry professionals, encouraging the adoption and standardization of XAI practices in clinical environments, thus ensuring the successful integration of transparent, interpretable, and reliable AI systems into healthcare. Full article

The rapid advancement of artificial intelligence (AI) and the continuous reform of English education in China have greatly reshaped the professional requirements for college English teachers. In contrast to the predominance of general teacher perspectives in adaptive expertise research, college English teaching offers a discipline-specific context in which adaptive expertise becomes particularly salient in response to AI-related pedagogical change. This study adopts a grounded theory approach to explore the qualities that college English teachers should possess in the era of artificial intelligence. A five-dimensional dynamic model of adaptive expertise was developed, consisting of knowledge expertise, competence expertise, vision expertise, emotional expertise, and development expertise. It shows that knowledge expertise embodies the integration of multiple knowledge domains required in the AI era; competence expertise emphasizes the ability to design and implement AI-enhanced instruction; vision expertise reflects global and future awareness in AI-technology integration; emotional expertise sustains resilience and motivation amid technological change; and development expertise promotes lifelong learning and innovation. These dimensions transfer, enhance, and inspire one another, forming a closed and self-reinforcing loop. It enriches the understanding of teacher professionalism in the AI era and offers a framework for cultivating AI-resilient expertise in higher education. Full article

Rapid advancements in artificial intelligence (AI) technologies, coupled with UNESCO’s Education 2030 vision, necessitate a re-evaluation of teachers’ technological and pedagogical competencies aligned with sustainability goals. This study investigates the impact of pre-service teachers developing digital materials within the framework of the Sustainable Development Goals (SDGs) using AI and AI-supported Web 2.0 tools (e.g., ChatGPT, DeepSeek, Alayna, Padlet, Canva, Kahoot) on their Artificial Intelligence Technological Pedagogical Content Knowledge (AI-TPACK) levels. Employing an explanatory sequential mixed-methods design, the research was conducted with 31 pre-service teachers over a 10-week applied training period. Data were collected via the AI-TPACK Scale and semi-structured interviews. Quantitative findings revealed that the applied training significantly enhanced the pre-service teachers’ Pedagogical Knowledge (PK), AI-Technological Knowledge (AI-TK), Pedagogical Content Knowledge (PCK), and overall AI-TPACK levels. However, no statistically significant difference was observed in the Content Knowledge (CK) dimension. Qualitative data demonstrated that AI-supported tools made the learning environment more engaging and efficient, concretized abstract sustainability concepts, and bolstered the pre-service teachers’ digital self-confidence. Consequently, this study establishes that integrating AI tools into SDG education is an effective strategy for cultivating pre-service teachers’ technopedagogical competencies, empowering them to perceive technology as a facilitator of professional development rather than an instructional barrier. Full article