Search Results (872)

The integration of sustainability in higher education institutions (HEIs) is critical but often hindered by the limitations of existing sustainability assessment tools (SATs), which are complex, rigid, and not sufficiently adaptable to specific organizational and socio-economic or local contexts. This study presents the Sustainability Implementation Reflective Assessment Framework (SIRAF), a meta-framework designed to assist HEIs in developing their own reflective, flexible, and user-friendly tools. The SIRAF taxonomy was developed through the findings of: a. a systematic literature review retrieved in authors’ previous research, b. a comparative analysis and synthesis of 12 SATs, as well as c. a theory-building process. It features a taxonomy of six core indicators with multiple sub-indicators. Its “pick-and-mix” approach enables institutions to customize assessments to align with their distinct needs, objectives, and resources. The SIRAF model was assessed in eight Greek universities offering tourism studies programs. The assessment incorporated data from institutional websites and a qualitative analysis. An evaluation of three fundamental indicators—curriculum, research, and institutional identity—disclosed a paucity of sustainability integration in curricula and governance, notwithstanding the augmentation of sustainability-related research activity. The findings underscore the significance of meticulously designed yet user-centred tools that facilitate evaluation, organizational learning, and strategic planning. As SIRAF shifts its paradigm of sustainability reporting from external compliance to internal improvement, it concomitantly reduces technical barriers and fosters institutional change. Though initially implemented in tourism and higher education, its inherent flexibility suggests the potential for broader applications, while future enhancements could include weighted scoring and wider empirical validation. Full article

In this paper, by means of a comprehensive analysis of the statutory and non-statutory documents that govern its preschool provision, we examine how early childhood education and care (ECEC), particularly in relation to mathematics, is conceptualised by the English educational authorities. Situated within international debates about economic (school-readiness, accountability-driven) versus social (holistic, play-based, rights-oriented) models of ECEC, the study explores how curriculum expectations, assessment practices and didactical guidance collectively frame young children’s learning opportunities. Drawing on a document-based analytic approach, and guided by six literature-derived questions, the analysis reveals significant inconsistencies both within and between documents, including conflicting messages about the purpose of preschool, an uneven emphasis on school readiness, and ambivalent statements regarding the role of play, instruction and practitioner agency, as well as contradictory and shifting expectations surrounding the scope, status and pedagogical treatment of early mathematics. While statutory materials frequently privilege school readiness and narrowly defined number outcomes, non-statutory guidance promotes broader mathematical thinking, exploratory play and child-initiated reasoning. Overall, the findings demonstrate limited coherence across the English authorities’ ECEC expectations and highlight the interpretive and professional challenges faced by practitioners expected to implement this fragmented early years mathematics policy landscape. Full article

The rapid transition to Industry 4.0/5.0 has widened the gap between graduates’ skill sets and labor market expectations; this study aimed to profile student competencies and align academic pathways with inclusive and adaptive AI-driven learning. A quantitative design was applied: an online survey of n = 126 students (engineering and economics, February–March 2025), expert evaluations from 5 faculty and 5 employers on a 5-point scale, framed by T-shaped competencies, 4C skills, and Bloom’s taxonomy. Analysis was performed in Python 3.11; future demand until 2035 was forecasted using ARIMA and Prophet models trained on publicly available labor market data (OECD, WEF, Eurostat 2015–2024); competency prioritization employed K-Means clustering and Random Forest models. Strengths included cooperation 4.2, critical thinking 3.9, communication 3.8, and creativity 3.6. Deficits were programming 2.8, project management 3.2, and solution development 3.2; employers rated programming at 2.5 (−0.7 compared to faculty). Forecast 2025–2035 showed growth in demand for programming +56% (3.2 → 5.0), data analytics +39% (3.6 → 5.0), project management +34% (3.2 → 4.3), digital literacy +30% (3.7 → 4.8), and critical thinking +15% (3.9 → 4.5). Clustering identified critical (programming, analytics, project management), supporting (creativity, communication, teamwork), and optional (narrow theoretical depth) competencies. Curriculum adjustment with practice-oriented modules, AI-enabled adaptive learning, and systematic university–employer feedback is essential; the proposed AI-supported profiling model is scalable and enhances inclusiveness. Full article

Understanding and developing habits in complex systems thinking using STEM-integrated perspectives is essential in addressing education and workforce needs in society. In this study, we investigated a learning intervention that incorporated multiple models designed to improve engineering students’ understanding of complex systems through investigating the mechanobiology of the Venus flytrap. Mechanobiology is a transdisciplinary field that integrates biology, engineering, chemistry, and physics to explore how cells and tissues sense and respond to forces in their environment. We used an exploratory, mixed-methods approach to examine the impact of this new curriculum on investigating flytrap closure and prey digestion. We then evaluated students’ understanding of complex systems characteristics (i.e., many interacting parts, decentralization, non-linear interactions, emergence, and adaptation) and in their ability to transfer these principles to other systems. Qualitative analyses demonstrate that students articulated key systems principles in relation to their understanding of flytrap mechanobiology, while descriptive summaries of pre- and post-surveys suggest broader conceptual gains. Furthermore, students demonstrated the transfer of systems thinking to other contexts and reported an enhanced understanding of real-world STEM research. Full article

Anchored in the strategic framework of Vision 2030, the research departs from anecdotal or survey-based approaches by exclusively leveraging publicly available, auditable data from national ministries, international university rankings, and scholarly publication databases. An original Integrated Green Transformation Framework (IGTF) is operationalized through fixed-effects regression modeling, longitudinal policy document analysis, and cross-sectional benchmarking of sustainability performance indicators across twelve Saudi universities. The findings demonstrate a statistically significant and temporally coherent association between national green policy milestones, such as the Saudi Green Initiative and the National Renewable Energy Program 2018, and measurable improvements in university-level sustainability strategies, operational efficiency, and research output. The average share of renewable energy utilization across sampled institutions increased from 2.1 percent in 2016 to 18.7 percent in 2023, representing substantial progress yet remaining below the Vision 2030 national target of 50%, while per-student water consumption declined by 34 percent over the same period. Scholarly publications in green economy domains rose by 638 percent, with a strong positive correlation (r = 0.76, p < 0.001) between research intensity and curriculum integration of sustainability content. Despite these advances, persistent disparities exist in resource allocation and implementation depth, particularly between historically endowed universities and newer regional institutions, highlighting a “sustainability divide” that requires targeted policy intervention. Full article

The European Green Deal is Europe’s ambitious and multi-layered response to climate change. Translating its objectives into action for a green transition has created a need for new skills and competencies. Vocational and Education Training (VET) systems are uniquely positioned to equip learners with these emerging green and transversal competences through their dual focus on knowledge dissemination and applied practice. However, current VET curricula remain oriented towards traditional occupations and are not adequately aligned with the sustainability and skills needs of the agri-food sector. This study, as part of a joint European-funded project (2023-1-IE01-KA220-VET-00156916: Train to Sustain), aimed to: (1) identify practical strategies for integrating sustainability concepts and innovative pedagogy into VET programs, and (2) gather multi-stakeholder perspectives on how VET agri-food education can be adapted for greater alignment with the green skills required by the sector. Following ethical approval, data were collected through semi-structured focus groups involving key agri-food stakeholder groups across Ireland, Slovenia, Poland and Italy. The data were qualitatively analysed using Reflexive Thematic Analysis (RTA). Five themes were identified: (1) Innovative and Sustainable Practices in Agri-Food systems, (2) Education, Awareness and Consumer Engagement, (3) Institutional and Structural Approaches, (4) Community and Localised Responses, and (5) Barriers, Opportunities and Future Directions. The findings highlight the significant potential VET offers in preparing a workforce with the cross-cutting sustainability competences and sector-specific skills needed to drive the innovation and growth of the agri-food sector. However, achieving this requires institutional change, strengthened collaboration, and a shift from traditional technical training toward curriculum models that embed sustainability principles across diverse local and regional contexts. Full article

The Wallonia-Brussels Federation is transforming through its education through the ‘Pact for an Education of Excellence’, notably by redefining the post-common core stage. This perspective article proposes a curricular paradigm that reconciles the specialisation of pathways with a shared foundation ensuring informed citizenship for all students. Based on a conceptual analysis of existing literature, we advocate for integrating Socially Acute Questions (SAQs) as a transversal axis of the post-common core curriculum. This shift the system from a ‘retrocognitive’ model—where knowledge is accumulated for uncertain future application—to a ‘procognitive’ model inspired by Chevallard’s ‘questioning of the world’. We outline seven pedagogical approaches to support this: controversy mapping, case studies, reasoned debate, the problematic matrix, researcher–student encounters, moral dilemmas, and role-play simulations. However, implementation faces barriers, including the rigidity of school structures, disciplinary compartmentalisation, teachers’ epistemic vulnerability, and challenges surrounding neutrality when addressing sensitive subjects. Success depends on transforming teaching professionalism through collaborative and interdisciplinary work, adopting ‘committed impartiality’ or ‘active neutrality’, and developing assessment tools that capture complex competencies. This proposal aligns with global curricular renewal movements and suggests a model where common ground rests not on contents, but on a competency to navigate the uncertainty and complexity. Full article

The proposed study investigates the key factors influencing UAV adoption and proposes an integrated educational–operational framework to enhance implementation in agricultural practice. A case study in Sibiu County, Romania, combined survey-based empirical analysis (n = 80), strategic environmental assessment and the deployment of a demonstration aerial crop monitoring system for educational purposes (ACMS-E). We integrated the Technology Acceptance Model (TAM) and Theory of Planned Behavior (TPB) to examine adoption intentions, revealing perceived usefulness (β = 0.355, p = 0.021) and positive attitudes (β = 0.382, p = 0.005) as the strongest predictors, explaining 44.1% of variance. Based on these findings, a modular training curriculum was designed, combining theoretical instruction, flight operation exercises, remote sensing techniques, data analytics and farm-management integration. ACMS-E provides hands-on training and promotes capacity-building, bridging the gap between technological availability and real-world adoption. By linking technological capabilities with structured training, ACMS-E bridges the gap between UAV availability and effective implementation, offering a scalable model for precision agriculture. This framework provides a pathway to accelerate UAV adoption, optimize field-level monitoring and support evidence-based, resource-efficient farm management in emerging and developed agricultural contexts. Full article

In response to problems inherent in conventional hydraulic engineering education including compartmentalized courses, fragmented knowledge delivery, overlapping and omitted content, and insufficient development of students’ integrated practical competencies this study develops an instructional model for a coordinated curriculum group based on the OBE-BOPPPS teaching theory. The curriculum cluster model aims to integrate interdisciplinary course content, restructure curriculum structure hierarchy, eliminate disciplinary barriers, and establish clear stratified and interrelated knowledge relationships. The model centers on competency development, constructing a three-dimensional “agent–objective” system that connects “teacher–student–curriculum” with “knowledge–competency–literacy.” It further establishes a multi-indicator evaluation system encompassing teachers, students, and courses. The comprehensive evaluation employing Principal Component Analysis, Entropy Weight Method, and CRITIC method demonstrates that the curriculum group teaching model significantly outperforms traditional course-based instruction in transcending disciplinary boundaries, enhancing knowledge systematicity, improving teaching precision, and strengthening knowledge acquisition as well as students’ comprehensive competencies. This approach achieves dynamic optimization and precision feedback in the teaching process, effectively facilitating the systematic transfer of knowledge and the holistic development of students’ innovative practical abilities. It thereby provides a scientific pathway and empirical support for the reform of hydraulic engineering education and the cultivation of high-quality talent. Full article

Green AI contributes to digital sustainability in higher education by encouraging computationally efficient technologies and responsible digital practices. Despite growing interest in sustainable AI, empirical evidence remains limited on how Gen Z students develop socially responsible intentions toward the use of sustainability-aligned AI, particularly within a single host-country higher-education context. This study examines these intentions among students enrolled in Saudi Arabia, using a culturally diverse sample of Saudi and international students while treating national origin as a demographic characteristic rather than a basis for cross-national comparison. The research also addresses emerging concerns related to AI hallucinations and ethical governance in educational settings. An integrated framework is employed that combines the instrumental appraisal logic of UTAUT with responsibility-oriented constructs. The model includes Sustainable Performance Value (SPV), Responsible Use Ease (RUE), Ethical Social Norms (ESN), Institutional Ethical Support (IES), Responsible AI Competence (RAC), AI Hallucination Awareness (AHA), and Green Digital Responsibility (GDR) as predictors of Socially Responsible Intentions (SRI). Data were collected through an anonymous survey of 1159 higher-education students residing and studying within the Saudi higher-education system. The study design reflects one institutional context rather than a multi-country comparison. The findings show strong explanatory and predictive capability (R² = 0.64; Q² = 0.43). SPV, RAC, AHA, and GDR are the strongest predictors of SRI, while RUE shows a moderate association and IES provides contextual support; ESN is not significant. The results highlight the importance of values, competence, and risk awareness in shaping the responsible use of AI. Implications focus on governance and curriculum strategies that support sustainability-aligned engagement with AI in higher education. Full article

Urban commercial restructuring, driven by the closure of traditional supermarkets and the expansion of new-format superstores, creates a large-scale spatial reallocation challenge requiring scientific location-allocation methods. Traditional heuristic algorithms such as Genetic Algorithm (GA) struggle with discrete spatial optimization under 400+ candidate sites and complex geographic mask constraints: they converge slowly and easily fall into local optima. This study proposes a Deep Reinforcement Learning (DRL) framework named GeoPPO (Geospatial Proximal Policy Optimization) to address this gap. Using Xi’an’s retail restructuring as a case setting—427 candidate locations and multidimensional geographic features—the approach models spatial constraints via a gridded environment encoded as a five-channel state tensor. Key innovations include a dynamic action-constraint mechanism that masks invalid actions based on boundary rules and competition avoidance, and a curriculum learning strategy that enables stable convergence. The framework fills the need for methods that handle hard spatial constraints in large-scale location-allocation. Tests demonstrate rapid convergence within 1,000 epochs, achieving 75% average demand coverage—2.7% and 5.5% higher than GA and Particle Swarm Optimization (PSO), respectively. Ablation experiments confirm that Vanilla PPO without dynamic action masking fails to produce feasible solutions. The framework offers a feasible technical path for handling highly dynamic urban facility spatial configuration with geographic mask constraints. Full article

Modeling the temporal dynamics of student learning is a central goal in educational data mining. Deep Knowledge Tracing (DKT) has emerged as a key approach, yet existing models are highly sensitive to out-of-distribution (OOD) inputs, such as those arising from curriculum changes, new assessment formats, or behavioral noise, which severely degrade predictive reliability. To address this challenge, we propose Energy-Based Out-of-Distribution Deep Knowledge Tracing (EB-OOD DKT), a unified framework that integrates energy-based uncertainty estimation and contrastive representation learning within a transformer-based DKT architecture. The model computes energy scores via the negative log-sum-exponential of prediction logits, serving as confidence indicators for detecting OOD inputs during inference. Additionally, an InfoNCE-based contrastive loss enhances representation robustness by aligning in-distribution samples and separating OOD cases in latent space. Temporal and behavioral context features, such as normalized response intervals and cumulative attempt counts, are incorporated to enrich cognitive-behavioral modeling. Experiments on four public educational datasets demonstrate consistent improvements in prediction accuracy and OOD detection. EB-OOD DKT provides a promising approach for more reliable student modeling across educational platforms with different content distributions. Full article

Zero-shot generalization to out-of-distribution (OOD) teammates and opponents in multi-agent systems (MASs) remains a fundamental challenge for general-purpose AI, especially in open-ended interaction scenarios. Existing multi-agent reinforcement learning (MARL) paradigms, such as self-play and population-based training, often collapse to a limited subset of Nash equilibria, leaving agents brittle when faced with semantically diverse, unseen behaviors. Recent approaches that invoke Large Language Models (LLMs) at run time can improve adaptability but introduce substantial latency and can become less reliable as task horizons grow; in contrast, LLM-assisted reward-shaping methods remain constrained by the inefficiency of the inner reinforcement-learning loop. To address these limitations, we propose LLM-TOC (LLM-Driven Theory-of-Mind Adversarial Curriculum), which casts generalization as a bi-level Stackelberg game: in the inner loop, a MARL agent (the follower) minimizes regret against a fixed population, while in the outer loop, an LLM serves as a semantic oracle that generates executable adversarial or cooperative strategies in a Turing-complete code space to maximize the agent’s regret. To cope with the absence of gradients in discrete code generation, we introduce Gradient Saliency Feedback, which transforms pixel-level value fluctuations into semantically meaningful causal cues to steer the LLM toward targeted strategy synthesis. We further provide motivating theoretical analysis via the PAC-Bayes framework, showing that LLM-TOC converges at rate $O(1/\sqrt{K})$ and yields a tighter generalization error bound than parameter-space exploration under reasonable preconditions. Experiments on the Melting Pot benchmark demonstrate that, with expected cumulative collective return as the core zero-shot generalization metric, LLM-TOC consistently outperforms self-play baselines (IPPO and MAPPO) and the LLM-inference method Hypothetical Minds across all held-out test scenarios, reaching 75% to 85% of the upper-bound performance of Oracle PPO. Meanwhile, with the number of RL environment interaction steps to reach the target relative performance as the core efficiency metric, our framework reduces the total training computational cost by more than 60% compared with mainstream baselines. Full article

Background: Generative artificial intelligence (AI) tools including ChatGPT have the potential to augment the process of designing examinations and assessments for medical learners, leading to time and resource savings, and the ability to produce large volumes of practice problems tailored to learner-specific strengths and weaknesses. Methods: This study compares the quality of free-text assessment problems and answer keys generated by ChatGPT to those produced by faculty educators for a renal and hematology curriculum subunit. Five expert reviewers reviewed a collection of 21 free-text assessment problems, 9 from a collection of historical assessment problems used in an undergraduate medical program and 12 produced with ChatGPT. Reviewers assigned a score from 1 to 5, reflecting the overall quality. Results: The average quality of problems generated by ChatGPT was greater than that of human-generated problems (4.00 vs. 2.71, p < 0.001). Using ordinal mixed-effect modeling, human-generated problems had significantly lower odds of receiving higher ratings than ChatGPT-generated problems (β = −2.43, 95% confidence interval −3.34 to −1.51, p < 0.001). Conclusions: It is suggested that ChatGPT can assist expert faculty educators in producing assessment tools, with direct benefits to medical learners, although it cannot entirely replace this role in its current state. Full article