As GenAI technologies become more pervasive in higher education (HE), scholars call for guidance on AI governance. To meet this need, a Delphi technique and collective writing was used in gathering expert perspectives from across 22 countries/locations and six continents. This resulted in the development of a HE GenAI policy/guidelines framework with eight core areas: (1) academic integrity, (2) ethical use and responsible use, (3) privacy and protection, (4) equitable access, (5) GenAI literacy, (6) integration strategy, (7) human oversight and accountability, and (8) institutional support and infrastructure. In addition, a six-part framework was developed to ensure that policies remain current and relevant: (1) creating a dedicated GenAI Committee, (2) conducting regularly scheduled policy reviews, (3) providing ongoing professional development and support, (4) communicating with all stakeholders, (5) evaluating the effectiveness and impact of GenAI, and 6) monitoring external developments. By providing a robust, eight-part framework for policy and guidelines, alongside a six-part mechanism for continued review, this study offers faculty, students, administrators, educational leaders, policymakers, and funders a responsible, adaptable, and consensus-driven blueprint for navigating the integration of GenAI in HE, ensuring that technological innovation serves pedagogical excellence.

The integration of artificial intelligence (AI) tools into educational settings is reshaping how students learn, create, and engage with digital technologies, particularly in fields such as technology and design education where virtual simulations and AI-assisted workflows are increasingly prevalent. Despite this momentum, student adoption of AI tools remains inconsistent, especially in developing regions where technological readiness and trust in emerging technologies vary widely. This study investigates factors influencing AI tool acceptance among 493 industrial technology students from a public university in Central Visayas, Philippines. Using structural equation modeling (SEM), the research examined the relationships among perceived usefulness, perceived ease of use, perceived risk, and behavioral intention to adopt AI-driven virtual simulation applications. Findings confirm that perceived usefulness and ease of use remain strong drivers of adoption intention, aligning with the Technology Acceptance Model (TAM). However, perceived risk demonstrated a significant negative influence on both perceptions and intentions, highlighting the impact of concerns related to data privacy, algorithmic transparency, and ethical implications of AI in design-oriented learning environments. Integrating perceived risk into TAM expands the explanatory power of the model in technical drafting and design education, offering a more comprehensive picture of how students evaluate and adopt AI-driven virtual simulation apps. This enriched perspective highlights the necessity for institutional strategies that build AI literacy, reinforce data governance mechanisms, and foster responsible engagement with emerging technologies. The results provide valuable insights for educators, curriculum designers, and policymakers working to advance AI-supported learning, particularly within resource-constrained and rapidly developing educational contexts.

Renewable energy (RE) applications in agriculture, such as solar irrigation, agrivoltaics, floating solar, biogas systems, and rural microgrids, can enhance resilience, reduce emissions, and support rural livelihoods. However, agricultural and land governance policies designed to protect food production and tenure can constrain renewable deployment, creating trade-offs between food security and energy transition goals. This study integrates international lessons with a stakeholder-based assessment of six Philippine agriculture and land governance policies. Based on a structured survey of 36 experts from academia, industry, government, and civil society, results show that the Department of Agriculture and Department of Energy - Renewable Energy Program for the Agri-Fishery Sector and the 2021 Department of Agrarian Reform amendment on land conversion are perceived as enabling, while the Agriculture and Fisheries Modernization Act, Comprehensive Agrarian Reform Law, Comprehensive Agrarian Reform Program Extension with Reforms, and the 2002 conversion rules are viewed as restrictive. The findings suggest that policies prioritizing agricultural land protection and exclusive land use may be less flexible for RE integration, while more adaptive and coordinated frameworks may better support dual-use systems and decentralized deployment. Divergent perceptions reflect competing priorities of food security, tenure protection, and investment feasibility. These findings also reveal significant heterogeneity across stakeholder groups, with government respondents generally supporting existing regulatory frameworks, while NGOs and other stakeholders emphasize the need for reform and greater flexibility. The paper identifies reform pathways for synergistic approach focused on recognizing agrivoltaics within agricultural zoning, streamlining permitting procedures, and linking solar irrigation to groundwater and equity safeguards to better align food, land, and energy policy.

As artificial intelligence (AI) systems assume greater responsibility in educational assessment, questions surrounding fairness, transparency, and trust have become central to their ethical and pedagogical legitimacy. Yet, little empirical work has examined how specific design features shape students’ trust in AI-driven assessment, particularly in contexts where algorithmic decisions carry meaningful academic consequences. This study examines how transparency, ethical framing, and user agency influence students’ trust in an AI-based assessment platform. Using a 2 × 2 × 2 between-subjects experimental design with 240 undergraduate participants, the study isolates the main and interaction effects of these variables on trust, perceived fairness, perceived control, and adoption intention. Findings indicate that transparency is the most influential predictor of trust, while user agency functions as a compensatory mechanism in low-transparency conditions. Ethical framing, although theoretically salient, showed limited impact once users interacted with the system directly and shifted their attention toward the more concrete procedural cues embedded in the interface. A significant interaction between transparency and agency underscores the importance of aligning epistemic clarity with procedural control to foster behavioral commitment. These results support a multidimensional model of trust that incorporates emotional security, procedural justice, and behavioral intent. Overall, the study underscores that trust in AI assessment is not a byproduct of system accuracy alone but a reflection of students’ perceived legitimacy of the evaluative process.

Phase change material (PCM)-based latent heat storage (LHS) systems help address the mismatch between renewable energy supply and thermal demand. However, their practical implementation is constrained by the strongly nonlinear and multiphysics nature of phase change, which makes high-fidelity simulations and real-time applications computationally expensive. This review examines mathematical reduced-order modeling (ROM) as an effective strategy to overcome this limitation by combining physics-based simplifications, projection methods, interpolation techniques, and data-driven models for PCM-based LHS systems. While physical simplifications (such as dimensional reduction and effective property approximations) represent an important first layer of model reduction, the primary focus of this work is on the mathematical ROM methodologies that operate on the governing equations after such physical simplifications have been applied. The review covers approaches including two-temperature non-equilibrium and analytical thermal-resistance models, Proper Orthogonal Decomposition (POD), CFD-derived look-up tables, kriging and ε-NTU grey/black-box metamodels, and machine-learning methods such as artificial neural networks and gradient-boosted regressors trained from CFD data. These ROM techniques have been applied to packed beds, PCM-integrated heat exchangers, finned enclosures, triplex-tube systems, and solar thermal components, achieving speed-ups from tens to over 80,000 times faster than full CFD simulations while maintaining prediction errors typically below 5% or within sub-Kelvin temperature deviations. A critical comparative analysis exposes the fundamental trade-off between interpretability, data dependence, and computational efficiency, leading to a practical decision-making framework that guides method selection for specific applications such as design optimization, real-time control, and system-level simulation. Remaining challenges—including accurate representation of phase change nonlinearity, moving phase boundaries, multi-timescale dynamics, generalization across geometries, experimental validation, and integration into industrial workflows—motivate a structured roadmap for future hybrid physics–machine learning developments, standardized validation protocols, and pathways toward industrial deployment.

The rapid emergence of generative artificial intelligence (GenAI) has introduced both opportunities and challenges for education systems worldwide. Educational stakeholders are grappling with fundamental questions of how to guide students on whether and when, and in what ways, they should use GenAI. In this study, a framework was developed to guide K–12 policies and guidelines on the use of GenAI. Using the Delphi technique and collective writing, expert perspectives were gathered from participants across 20 countries and six continents. The analysis identified eight key topic areas for K–12 GenAI policy and guideline development: (1) data privacy and security, (2) ethical and responsible use, (3) equitable access, (4) academic integrity, (5) human oversight, (6) GenAI literacy, (7) curriculum integration, and (8) governance and review. A complementary six-part framework was also constructed to support policy relevance and currency through multi-stakeholder governance, continuous review, ongoing training, awareness of external developments, outcome monitoring, and transparent communication. Together, these frameworks advance the scholarly and practical understanding of how GenAI policies can be designed and maintained in schools.

Developing highly active electrocatalysts for the oxygen evolution reaction is a pivotal challenge in sustainable water electrolysis. Herein, we report a novel in situ oxidative phase-restructuring strategy to fabricate oxygen vacancy-rich NiFe2O4 (NFO) directly on nickel foam. Distinct from conventional hydrothermal methods that typically yield thermodynamically stable crystals with limited intrinsic defects, our unique one-pot process involves the formation of a reduced metallic intermediate. The subsequent drastic phase transformation from this metallic state to a spinel oxide thermodynamically enforces the generation of abundant oxygen vacancies to relieve lattice stress, resulting in unique polycrystalline nanocrystal assemblies (NFO-1). Electrochemical evaluations reveal that NFO-1 significantly outperforms its thermodynamically equilibrated counterpart (NFO-2), exhibiting a low overpotential of 330 mV at 20 mA cm−2 and a remarkable mass activity of 6.78 A g−1. This superior performance is primarily attributed to intrinsic oxygen vacancies generated during the oxidative phase evolution, which optimize the active-site electronic structure and enhance charge–transfer kinetics. Furthermore, the catalyst demonstrates excellent durability over 1200 cycles. This work highlights oxidative phase restructuring as a powerful pathway to engineer intrinsic defects for high-efficiency energy-conversion applications.

The metaverse and artificial intelligence (AI) are increasingly intersecting in educational contexts, yet limited empirical research has examined how generative AI avatars function within socially interactive virtual environments. This study investigates the deployment of generative AI avatars within a socio-educational metaverse environment. Using a developmental evaluation approach, data were collected through interviews with seven institutional stakeholders, teacher-generated reflections, internal documentation, embedded user feedback captured through in-platform reporting tools, and longitudinal field memos across an iterative deployment cycle. Findings indicate that the transition from scripted NPCs to generative AI avatars recalibrated users’ attribution of agency, intensified dialogic unpredictability, and elevated social realism beyond visual fidelity. Voice-mediated interaction emerged as a threshold mechanism for co-presence, while algorithmic improvisation exposed tensions between pedagogical intent and stochastic response generation. The deployment further revealed affective frictions, expectation misalignments, and the mediating role of AI literacy in shaping trust, participation, and interpretive coherence. Overall, the study advances a sociotechnical understanding of AI avatars as co-constructors of meaning and interaction, offering implications for the design, implementation, and governance of future AI-enhanced metaverse learning environments.

Artificial intelligence (AI) has reshaped various aspects of human lives, particularly through its capabilities to address complex sustainability challenges. Despite the rapid expansion of AI applications, their contribution to cleaner production and sustainable development remains underexplored, especially in developing nations. In Southeast Asia (SEA), where AI adoption has grown substantially across environmental, economic, and social dimensions, research that examines its role in cleaner production outcomes remains fragmented. In view of this gap, this study conducts a systematic literature review (SLR) of AI applications related to cleaner production and sustainable development by examining relevant themes, application areas, and sustainability dimensions addressed by AI, while evaluating the maturity of AI methodologies, alignment with cleaner production outcomes, and integration with circular economy and resource efficiency goals. Moreover, it investigates the barriers and challenges that constrain AI application and offers future research directions to advance AI deployment for cleaner production and sustainable development across SEA countries.

As calls to center lived experience in higher education intensify, so too do concerns about the ethical, emotional, and structural risks involved in integrating real-life narratives into pedagogy. This study introduces Simulated Lived Experience (SLE) as a novel pedagogical modality that leverages the immersive affordances of learning environments like the metaverse to approximate systemic conditions of marginalization without reproducing trauma or requiring emotional labor from marginalized individuals. Drawing on critical pedagogy frameworks and affect theory, the research explores how SLE enables learners to engage with ethical discomfort, narrative complexity, and affective dissonance through the enactment of virtual selves. A qualitative design was employed, with data collected via semi-structured interviews from 12 participants who engaged in metaverse-based simulations portraying exclusionary dynamics related to disability, race, and institutional access. Thematic analysis generated four key findings: (1) virtual simulations evoke affective authenticity but also ethical unease, (2) embodied disorientation fosters structural insight, (3) narrative authorship and representation are ethically contested, and (4) discomfort acts as a catalyst for critical reflection. The study concludes that while SLE cannot replace lived experience, it can function as a powerful epistemic mediator when designed collaboratively, approached reflexively, and grounded in epistemic care.