Search Results (2,897)

In project-based organizations, novices engaged in project-contextualized learning often struggle to balance sustained project learning with immediate task delivery, creating a tension between developmental sustainability and execution sustainability. While informal support mechanisms such as apprenticeship help alleviate this tension, their effectiveness remains limited. To address this issue, this study adopts a Design Science Research approach to develop a generative AI-driven project scaffolding system prototype. The study contributes design knowledge comprising two core elements. First, based on the task execution process, scaffolding support is organized into three dimensions, namely contextualization, cognitive guidance, and cognitive evolution, which correspond to the progression from task understanding to cognitive construction. Second, a cognition-driven scaffolding mechanism is constructed through prompt-driven and knowledge-augmented generation, enabling human-centered intelligent guidance, augmentation, and automation during task execution. Evaluation in a software implementation firm suggests that the system may improve task output quality and support novices’ application of task-relevant strategies in subsequent tasks. These findings indicate the system’s potential to support the sustainability of both project learning and task execution in project practice. This study provides design insights for embedding GenAI-driven scaffolding in project practice, helping organizations in similar project contexts establish sustainable project support approaches. Full article

This study aimed to identify the level of awareness and application of learning principles among agricultural extension service providers when communicating extension recommendations to farmers. It also sought to determine the major constraints that may hinder the effective application of these principles in extension practice. The study adopted a descriptive analytical approach. Data were collected using a structured questionnaire designed to achieve the objectives of the research. The study was conducted on all agricultural extension service providers in Kafr El-Sheikh Governorate, totaling 55 respondents. The study focused on nine learning principles relevant to extension education: motivation, clarity of objectives, self-activity, transfer of learning, learner individuality, readiness, reinforcement, modification or relearning, and repetition. The findings revealed variation in the levels of knowledge and application of these principles among the respondents. The results indicated that 65.4% of the respondents had a moderate level of knowledge of the motivation principle, while 67.2% applied it at a moderate level. In contrast, 81.8% of the respondents had a low level of knowledge of the principle of clarity of objectives, and 85.4% applied it at a low level. The results also revealed several constraints that limit the effective application of learning principles in extension work, most notably the limited effectiveness of communication with farmers and the need to strengthen the educational competencies of extension service providers. Accordingly, the study recommends developing the instructional capacities of extension service providers through specialized training programs on learning principles and extension education methods in order to improve the effectiveness of communicating agricultural recommendations and enhance the adoption of agricultural innovations. Full article

Background: Schools are widely recognized as key settings for promoting healthy eating behaviors and supporting childhood obesity prevention. In recent years, increasing attention has been devoted to experiential and interactive nutrition education strategies designed to actively engage children and adolescents in food-related learning processes. These approaches move beyond traditional didactic teaching and include practical and participatory formats, such as cooking activities, school gardening, digital or app-based learning tools, workshops and educational camps, and game-based learning interventions. Objective: This narrative review aims to provide an overview of experiential school-based nutrition education interventions, describing the main types of programs implemented in school settings and summarizing their reported effects on nutrition knowledge, attitudes, and eating behaviors among children and adolescents. Results: Across intervention studies and systematic reviews, hands-on and interactive educational models, including cooking classes, gardening programs, digital learning tools, workshops or camps, and board game-based interventions, frequently report improvements in nutrition knowledge, attitudes toward food, food-related skills, and self-efficacy. These programs seek to strengthen food literacy by combining experiential learning with educational content delivered within the school environment. Evidence regarding changes in dietary intake, diet quality, and anthropometric outcomes is more heterogeneous, with some studies reporting improvements in eating behaviors and others showing more modest or short-term effects. Program outcomes appear to be influenced by several contextual factors, including intervention duration, curriculum integration, teacher involvement, and the availability of resources supporting implementation. Conclusions: Experiential and interactive approaches represent an increasingly adopted strategy in school-based nutrition education. Their effectiveness appears to depend on the quality of implementation, the degree of integration within the school curriculum, and the broader educational context. Future research should further explore how different experiential formats can be optimally integrated into school systems to support the development of food literacy and sustainable healthy eating behaviors among children and adolescents. Full article

With the 2030 deadline for the United Nations Sustainable Development Goals (SDGs) approaching, there is a growing global urgency to identify innovative, scalable, and inclusive AI-based or AI-enabled solutions capable of accelerating progress across sectors. Yet the benefits of AI remain unevenly distributed, particularly in low-resource settings where limited infrastructure, cost barriers, and unequal access to skills constrain adoption. This paper explores how Tiny Machine Learning (TinyML)—a low-power, low-cost edge AI paradigm—offers a concrete technological pathway aligned with the principles of Frugal AI, providing accessible, energy-efficient, and context-adapted tools for sustainable development. We evaluate how participatory citizen science, when combined with TinyML, enables communities to co-create AI applications that address locally defined challenges in environmental monitoring, agriculture, and public health. Drawing on early outcomes from workshops, collaborative projects, and innovation competitions, the paper examines how TinyML-enabled participatory approaches cultivate technical skills, stimulate grassroots entrepreneurship, and generate prototypes suited to low-resource environments. Using a qualitative multiple-case study of 50 participatory TinyML initiatives across 22 countries, we analyse how frugal edge-AI practices support skills formation, prototype development, and early entrepreneurial engagement. The analysis identifies the pedagogical, technical, and institutional frameworks that support successful participatory AI initiatives, emphasizing open educational resources, cross-sector partnerships, and community-driven problem formulation. We introduce the Frugal Edge AI Lean Canvas to help innovators identify novelty, ethical implications, and measurable impact. TinyML-based participatory innovation offers a promising route for accelerating SDG progress by expanding who can create, deploy, and benefit from AI. Full article

One of the major determinants of crop productivity and sustainable agricultural practices is soil fertility. Proper soil assessment helps farmers make informed decisions about nutrients and fertilizers. This study utilizes 16 machine learning classifiers for soil fertility prediction, including learner-based, ensemble-based, instance-based, and probabilistic-based models. The model’s performance is assessed using accuracy, precision, recall, and F1-score. This paper presents a machine learning model for predicting soil fertility based on soil physicochemical characteristics. The data used in the research comprise vital soil parameters: nitrogen, phosphorus, potassium, pH, organic carbon, electrical conductivity, and micronutrients. Missing-value imputation, label encoding, and feature standardization are among the data preprocessing methods used to enhance data quality. Correlation analysis, ANOVA F-score, and mutual information were used to assess feature importance and determine the most significant soil characteristics. The experimental observation reveals that the RF model achieves an accuracy of 90.91% compared to the other models. Additional assessment using multi-class Receiver Operating Characteristic (ROC) and Precision–Recall (PR) curves showed excellent discriminative ability across the dominant soil fertility, which was of high quality. The findings show that machine learning models, especially ensemble-based models, are effective at estimating soil fertility levels. The proposed framework provides a data-driven, reliable decision-support system to assess soil fertility, enabling farmers and agricultural experts to enhance nutrient management and crop production. Full article

Biochar has emerged as a sustainable additive in concrete production, offering potential for improved concrete performance and waste valorization. An experimental investigation was conducted using wood waste biochar as a partial cement replacement at 0%, 2%, 4%, and 6% by weight. Compressive strength (CS) and split tensile strength (STS) were determined at 7 and 28 days, while flexural strength (FS) was determined at 28 days. The experimental results demonstrated that 2 to 4% biochar replacement enhanced CS by 9.67% and FS by 15.40%, while STS showed optimal improvement at 2% replacement by 6.24%. To extend these findings across diverse feedstocks and mix designs, a comprehensive database of 318 mixes incorporating 13 biochar types was compiled from literature to develop machine learning (ML) models for predicting all three strength properties simultaneously. Random Forest (RF) and Gradient Boosting (GBR) algorithms were optimized using nested 5-fold cross-validation and compared against a Ridge regression baseline. The optimized RF model (n_estimators = 1000) achieved a nested cross-validated R² of 0.817 ± 0.072 and a 32.5% reduction in RMSE compared to the baseline, with testing R² values of 0.894 for CS, 0.828 for FS, and 0.537 for STS. (SHapley Additive exPlanations) (SHAP) analysis identified cement content, coarse aggregate (CA) content, and biochar dosage as the most influential features. Biochar effect curves, based on the most reliable datasets (rice husk, n = 69; wood, n = 52), demonstrated that rice husk biochar consistently enhanced all three strength properties, while wood biochar showed superior performance for FS and STS. Experimental validation using wood waste biochar confirmed that model predictions closely matched measured strengths, with 90% prediction intervals reliably encompassing experimental values. The developed models offer a practical decision-support tool for sustainable concrete mix design, significantly reducing experimental effort while providing evidence-based guidance for biochar feedstock selection and dosage optimization, keeping the cement usage at a minimum. Full article

Vulnerability management (VM) is becoming increasingly important as organizations face growing cybersecurity threats. This study examines how organizations adapt their vulnerability management routines in response to evolving vulnerability signals through the integration of artificial intelligence (AI) and automation. Drawing on data from an international fast-moving consumer goods (FMCG) company, we investigate how human expertise and AI interact across the full VM process, from triage to remediation. Using Organizational Routine Theory (ORT), we show that AI does not simply automate tasks but acts as a co-performer, influencing how decisions are made, work is coordinated, and actions are adapted. We develop a three-phase model capturing (1) the integration of AI-enabled automation into strained routines, (2) the manifestation of tensions between human expertise and automation as well as between usability and system complexity, and (3) the stabilization of hybrid routines through iterative adaptation and feedback loops. We identify two key tensions in this process: technology versus human expertise, and usability versus the complexity of multi-vendor tools. These tensions create frictions in practice but also open opportunities for learning and improvement. Rather than treating AI as a technical tool, our findings highlight its role as an active routine participant. Importantly, we show that routine evolution enables organizations to improve how vulnerability signals are interpreted and acted upon, thereby supporting more coordinated and adaptive cybersecurity practices. This has both theoretical implications for understanding how routines evolve with technology and practical relevance for improving adaptive cybersecurity practices. By linking micro-level routine dynamics to broader organizational outcomes, this study contributes to explaining how organizations sustain stable and adaptive operations under conditions of continuous cyber threat exposure. Full article

Artificial intelligence (or AI) is rapidly transforming digital learning environments, reshaping how educational processes are organized, how knowledge is produced, and how learning is evaluated. Despite a growing body of research on AI in education, existing studies often examine technological, pedagogical, and ethical dimensions in isolation, leaving a lack of integrative frameworks capable of explaining how AI restructures learning environments as a whole. This study addresses this gap by proposing a three-layer conceptual framework that models AI-mediated learning environments through the interaction of efficiency, pedagogy, and ideology. The framework conceptualizes AI integration as a system of interdependent processes: the efficiency layer captures the optimization of educational activities through automation and data-driven personalization; the pedagogical layer explains how AI reshapes learning processes, feedback cycles, and learner strategies; and the ideological layer examines the normative assumptions embedded within AI systems, including issues of epistemic authority, linguistic norms, and algorithmic bias. Drawing on a structured synthesis of recent empirical research across domains such as generative AI tools, automated feedback systems, intelligent tutoring systems, and AI-supported assessment, the study demonstrates how these dimensions interact to structure contemporary digital learning environments and generate both affordances and tensions. The main theoretical contribution lies in advancing a system-level analytical framework that moves beyond tool-specific approaches and enables a more integrated understanding of AI in education. In practical terms, the framework provides educators and policymakers with a lens to critically evaluate AI integration, supporting more informed decisions on assessment design, sustainable learning practices, and inclusive digital education. Full article

The accurate estimation of State of Health (SoH) for lithium-ion batteries in real-world electric vehicles (EVs) is critical for ensuring safety, reliability, optimal energy management, and lifecycle sustainability. Unlike laboratory-controlled conditions, real-world EV batteries operate under highly dynamic loads, irregular charging behaviors, diverse environmental conditions, and user-dependent driving patterns. This review provides a comprehensive and structured overview of recent progress in SoH estimation for real-world EV applications. The fundamentals of battery aging mechanisms are summarized, with a clarification of key SoH definitions, metrics, and influencing factors under practical operating conditions. Subsequently, existing methodologies are systematically categorized into physics-based models, data-driven approaches, hybrid/model-assisted frameworks, and uncertainty-aware probabilistic methods, with a focus on their strengths and limitations in real-world deployment. Key challenges, including domain shift, computational constraints, explainability, thermal variability, and data heterogeneity, are critically and systematically analyzed. Finally, future research directions are outlined, emphasizing transfer learning, foundation models, physics-informed AI, self-supervised learning, digital twins, and the need for standardized benchmarks. This review aims to provide researchers and practitioners with a clear roadmap toward reliable, scalable, and trustworthy SoH estimation for next-generation intelligent battery management systems in electric vehicles. 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

This conceptual article introduces knowledge-centric recruitment (KCR) as a distinct dynamic capability that reframes recruitment and post-hire socialization as strategic knowledge-development activities. (1) Background: Unlike conventional vacancy-driven approaches, KCR is a proactive process through which firms deliberately access and import external organizational capabilities embodied in senior professionals—termed knowledge-hires—from rival organizations. These knowledge-hires embody tacit, socio-cognitive building blocks of capabilities developed through involvement in their prior employers’ routines and practices. (2) Methods: This article develops a micro-foundational model of KCR comprising four interrelated processes: external capability scanning and prioritization, identification of target capabilities and knowledge-hires, evaluation through the novel lens of contextual capability fit, and expectations of adaptation during onboarding. (3) Results: Contextual capability fit integrates complementary and supplementary quality with knowledge distance to enable firms to forecast both the strategic value of inbound capabilities and the hire’s expected socialization difficulty. (4) Conclusions: The primary theoretical contribution lies in advancing the learning-by-hiring literature by shifting the focus from passive knowledge diffusion to deliberate, calculative capability acquisition. By integrating insights from the knowledge-based view, person–organization fit, absorptive capacity, and strategic recruitment, the KCR model offers a coherent micro-foundational framework for transforming employee mobility into a source of sustained competitive advantage. Full article

Implementation remains a central challenge in urban policy, yet the knowledge formats designed to bridge the gap between policy goals and on-the-ground delivery remain under-examined. This study treats 250 UN-Habitat Best Practice reports not as proof of effectiveness but as a standardized genre through which local interventions are narrated, compressed, and made portable for replication. We extract three focal sections, namely Results, Lessons Learned, and Transferability, apply systematic thematic coding with 906 open codes consolidated into axial categories, and compute co-occurrence networks using Jaccard similarity and Lift to detect thematic bundles, holes, and silos within and across sections. Three findings emerge. First, the reporting repertoire narrows progressively, as mean thematic richness declines by 28.2% from Results to Transfers while concentration increases 4.2 times, with substantive dimensions such as governance, equity, sustainability, and evidence losing prevalence to circulation-oriented themes. Second, formal bundle detection yields zero qualifying pairs across all six matrices, indicating a loosely coupled reporting grammar anchored by generic silos rather than integrated implementation packages. Third, structural holes concentrate at the pipeline’s end, where infrastructure transfer and sustainability as transferable value are the most systematically disconnected themes. These patterns reveal a portability paradox in which the reporting format achieves institutional legibility, making practices comparable within a shared vocabulary, but progressively filters out the physical, evidentiary, and context-sensitive content that operational reproduction would require. Full article

This study examines the impact of a National Institute of Health (NIH)-funded STEM project on high school students’ STEM self-efficacy and perceptions of future STEM careers across two academic years in Western New York. The intervention engaged students in authentic scientific practices, including environmental sampling, microbial DNA analysis, and presenting research posters at a Capstone event. Pre- and post-surveys were administered to intervention and control groups, measuring STEM self-efficacy and perceived future in STEM. Data from 313 students were analyzed using explanatory factor analysis (EFA), confirmatory factor analysis (CFA), and structural equation modeling (SEM) with multiple imputation. EFA results supported a one-factor structure, which was confirmed by CFA results showing a good model fit for both constructs. SEM findings indicated that program participation significantly improved STEM self-efficacy, while effects on perceived future in STEM were nonsignificant, though potentially moderated by cohort. No race-based interaction effects emerged, suggesting consistent program benefits. The findings imply that schools should incorporate authentic STEM learning experiences to strengthen students’ confidence and broaden equitable engagement in STEM. Limitations include the bias on self-report measures. Future longitudinal and mixed-methods research is needed to examine how early gains in self-efficacy translate into sustained STEM pathways. Full article

Education for Sustainable Development (ESD) has emerged as a key priority in contemporary education systems, emphasizing the need to equip learners with the knowledge and competencies required to address complex environmental and societal challenges. Mathematics education can play an important role in achieving these goals by enabling students to analyse data, interpret real-world problems, and develop critical thinking skills related to sustainability issues. However, despite the growing interest in sustainability-oriented mathematics education, limited empirical evidence exists on how structured mathematical modelling interventions influence pre-service primary teachers’ perceptions, modelling orientation, and confidence in designing sustainability-based mathematics lessons. This study investigates the impact of sustainability-oriented mathematical modelling activities on pre-service primary teachers’ perceptions of integrating sustainability into mathematics education. The study employed a quasi-experimental design involving 68 pre-service primary teachers enrolled in a mathematics education course at a university. Participants engaged in a six-week intervention consisting of modelling activities based on real-world sustainability contexts, including water consumption, energy use, waste management, and sustainable transportation. Data were collected using a pre- and post-intervention questionnaire examining participants’ perceptions of sustainability integration, mathematical modelling, and teaching confidence. Statistical analyses, including reliability analysis, descriptive statistics, paired-sample t-tests, effect size estimates, and correlation analysis, as well as multiple regression analysis, were conducted to examine the impact of the intervention. The results indicate significant improvements in participants’ perceptions of sustainability-oriented mathematics teaching and their confidence in designing modelling-based sustainability activities. The largest improvement was observed in teaching confidence, while mathematical modelling perception emerged as a significant predictor of teaching confidence. The findings suggest that mathematical modelling can serve as an effective pedagogical approach for integrating sustainability topics into mathematics education and preparing future teachers to connect mathematical reasoning with real-world environmental challenges. The study contributes to the growing body of research at the intersection of mathematics education, teacher education, and sustainability education by providing empirical evidence on the potential of modelling-based learning for supporting sustainability-oriented teaching practices. More specifically, it shows how mathematical modelling can function as a concrete pedagogical mechanism for translating Education for Sustainable Development into primary mathematics teacher education. Full article

Accurate, rapid, and consistent evaluation of pavement condition across large-scale road networks is critical for sustainable maintenance and rehabilitation planning. However, conventional approaches largely rely on manual visual inspections, which are time-consuming, subjective, and difficult to implement at the network level. In this study, a semi-automated pavement distress evaluation framework that integrates field-based assessment with computer vision techniques is proposed. The study was conducted on a 3 km roadway network located within the Yıldız Technical University Davutpaşa Campus. Field-based distress observations were used as reference data, while street-level images obtained from the Mapillary platform were analyzed using a deep learning-based YOLOv8 model trained on the RDD2022 dataset, which was specifically developed for road distress detection. The analysis focuses on crack and pothole distress, which have a dominant influence on PCR and are highly distinguishable in image-based approaches. Correlation analyses between automated detection results and field-based data demonstrate a strong agreement, reaching values of approximately $\rho \approx 0.90$ in some routes. These findings indicate that these distress types are effective in representing variations in pavement condition. The results demonstrate that multi-source image data and deep learning-based detection methods can be reliably used for section-level pavement condition assessment. The proposed approach addresses a key gap in the literature by transforming image-level detections into engineering-based decision-support information. Furthermore, by leveraging publicly available data sources, the framework offers a low-cost and scalable solution that enables rapid preliminary assessment over large road networks, thereby providing significant potential for sustainable infrastructure management and the development of data-driven maintenance strategies. Several practical challenges encountered during the detection process—including sensitivity to contrast enhancement parameters, false positives from shadows and surface reflections, heterogeneous image resolution across crowdsourced imagery, and training distribution gaps for locally prevalent infrastructure features—are discussed, and directions for reducing human intervention through adaptive preprocessing and targeted model refinement are identified. Full article