This study examines the Canvas’ usability in Learning Management System (LMS) from the perspectives of students and teachers, focusing on experiences across Progressive Web App (PWA) and native mobile platforms. A task-based usability testing approach was employed, combining quantitative measures of task completion and time with qualitative insights from observations and participant feedback. Findings indicate that both platforms supported high task completion, though clear differences emerged in efficiency and feature accessibility. Teachers achieved a 91.7% completion rate on the mobile app compared to 100% on the PWA. The mobile app was faster for grading and assignment creation, while the PWA provided broader feature coverage, particularly for analytics, though some users reported navigation difficulties. For students, performance differences were more pronounced: average task completion time on the PWA was 1.24 minutes compared to 5.72 minutes on the mobile app. Tasks such as replying to announcements and checking grades were completed up to ten times faster on the PWA. Overall, the mobile app demonstrated greater stability and efficiency for routine functions, whereas the PWA offered extended functionality and cross-platform access but with tradeoffs in responsiveness and interface clarity. These results highlight the role of platform choice in shaping user experience and suggest directions for optimizing Canvas LMS for both teaching and learning contexts. By advancing usability in digital learning platforms, this research contributes to Sustainable Development Goal (SDG) 4: Quality Education, while also supporting SDG 9: Industry, Innovation, and Infrastructure through insights on mobile technology design, and SDG 10: Reduced Inequalities by emphasizing accessibility across diverse devices and connectivity conditions.

Despite the potential for e-commerce to boost productivity and market access for farmers, adoption remains low, particularly in rural areas of developing countries. This study addresses the research gap by predicting farmers' adoption of digital platforms in the National Capital Region, Philippines, using the Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM). Based on a survey of 615 farmers and analysis with various machine learning models, with XGBoost as the top performer, the study found that perceived usefulness, trust, and price value are the most significant factors influencing adoption. Social influence and ease of use also play important roles. The findings provide guidance for policymakers and platform developers, highlighting the need to improve digital literacy, build trust, and ensure affordability to accelerate the digital transformation of the agricultural sector.

In the evolving landscape of education, artificial intelligence (AI) is reshaping traditional pedagogical models and redefining the roles of teachers and students. This paper explores the transformative potential of Microsoft Copilot Vision, a multimodal AI tool, in enhancing teacher-student interaction and promoting educational equity. By integrating visual AI capabilities into classroom practices, educators can create more dynamic and adaptive learning environments that can personalize instruction, provide real-time feedback, and support diverse learning needs. The study examines how Copilot Vision facilitates inclusive learning environments, particularly for students with disabilities, language barriers, or limited access to resources. It also investigates the shifting role of teachers-from content deliverers to facilitators of AI-enhanced learning-and the ethical considerations surrounding human-AI collaboration in education. Anchored in the framework of Sustainable Development Goal 4 (SDG4), which advocates for inclusive and equitable quality education, this research highlights the opportunities and challenges of deploying AI vision tools to bridge learning gaps and foster meaningful engagement in the digital classroom setup.

Typhoons remain among the most frequent and destructive natural disasters in the Philippines, underscoring the need for innovative preparedness strategies. This study presents Storm Shield: A 3D Strategic Co-op Game, a local multiplayer educational game that simulates the three phases of a typhoon–Before, During, and After–through mission-based levels emphasizing teamwork, planning, and decision-making. Players assume roles within a disaster response team and perform tasks such as preparing emergency kits, assisting civilians, and managing post-typhoon recovery. The game integrates action, strategy, and educational mechanics, supported by a Content Management System (CMS) website that reinforces learning. Effectiveness was evaluated through post-play surveys using Key Performance Indicators (KPI) covering functionality, entertainment, player experience, and educational value. Results show that Storm Shield engages players while improving knowledge of disaster preparedness and response. By merging learning with entertainment, the project demonstrates the potential of serious games to support disaster risk reduction and contributes to Sustainable Development Goal (SDG) 11 by promoting resilient communities through interactive education.

As generative AI continues to reshape educational landscapes, prompt-to-image technologies offer new possibilities for enhancing visual pedagogy. This study investigates the integration of NanoBanana a lightweight, prompt-driven image generation tool, into higher education settings to support multimodal learning and cognitive scaffolding. Grounded in Dual Coding Theory and Cognitive Load Theory, the research explores how AI-generated visuals derived from student and instructor prompts can improve comprehension, engagement, and retention in complex subjects such as ICT, Game Studies, and Systems Analysis. Using a mixed-methods approach, the study analyzes student performance data, visual rubric evaluations, and qualitative feedback from learners and educators. Findings reveal that NanoBanana-generated images significantly aid in conceptual clarity, reduce extraneous cognitive load, and foster learner autonomy. The paper proposes a practical framework for integrating prompt-to-image tools into curriculum design and instructional workflows, offering actionable insights for educators seeking to advance AI-enhanced teaching practices in line with SDG4 and the evolving demands of the AI era.

Video-based learning (VBL) is a cornerstone of modern education. With the rise of artificial intelligence (AI), tools such as AI-generated video summaries are increasingly used to enhance learner efficiency and streamline content delivery. However, little is known about how such summaries influence learner behavior, cognitive engagement, and motivation during exposure to instructional materials. The present study examined the impact of AI-generated summaries on engagement, comprehension, and intrinsic motivation in a controlled VBL environment. Sixty participants were randomly assigned to either a control group (video only) or a summary group (video plus AIgenerated summary). Results showed that summary access led to significantly reduced video viewing time, suggesting that learners treated the summary as a substitute rather than a complement. While comprehension scores were only moderately lower in the summary group, deeper analysis revealed underperformance on conceptual transfer items. Intrinsic motivation was also significantly lower, particularly in interest and perceived value. These findings underscore the need for intentional design when integrating AI-generated support, as unstructured summary access may promote shallow engagement and diminish learning outcomes. The study concludes with design implications, recommended strategies for integrating AI-generated supports, and directions for future research in VBL environments.

With the upcoming changes brought about by Artificial Intelligence (AI) to the field of education, teaching AI literacy and digital competency to educators has emerged as an imperative. Based on proposed and empirically-supported research, this paper outlines a multi-level training model that is aligned to Sustainable Development Goal 4: Quality Education and where the AI awareness and pedagogical integration is placed as a basis. Through this proposed model that adopts a multimethodological approach comprising a review of relevant literature and surveys and interviews among educators, major milestones regarding the implementation of vital training competencies among teachers before and after being hired have been discovered. These facts reveal that when it comes to awareness of tools available, high-level awareness has been discovered to exist among them but when it comes to confidence and preparedness in ethical practices, poor awareness has been observed.

Artificial Intelligence (AI) is transforming the prediction and optimization of advanced energy materials by enabling accurate, scalable modeling beyond traditional methods. This review evaluates recent AI applications—including Graph Neural Networks (GNNs), Convolutional and Recurrent Neural Networks (CNNs, RNNs), tree-based ensembles, and Gaussian Process Regression (GPR)—for forecasting performance metrics such as overpotential, conductivity, capacity, and degradation. GNNs achieved R2 > 0.90 in structure-sensitive tasks; LSTM models predicted battery degradation with <10% error; and tree-based models balanced accuracy (MAE < 0.15 V) with interpretability. GPR excelled in low-data regimes via uncertainty quantification. Hybrid and physics-informed models improved generalizability and data efficiency. While challenges remain in data quality and integration with experiments, emerging strategies like autonomous labs and generative design offer promising advances. This review provides comparative benchmarks and highlights pathways for robust AI-driven materials discovery.

High voltage engineering has evolved rapidly, driven by the growing need for efficient energy transmission and the integration of renewable energy into modern power grids, including urban areas. Innovations such as HVDC systems are central to this transformation, ensuring that grids can handle the increasing complexity and demand for sustainable energy. However, challenges remain, especially when it comes to coordinating insulation in hybrid AC/DC systems and maintaining the resilience of the overall infrastructure. This review looks at how Artificial Intelligence (AI) can help tackle these challenges, focusing on its role in fault detection, predictive maintenance, and improving system reliability. By comparing traditional methods with AI-driven solutions, we highlight how AI can enhance the scalability, efficiency, and adaptability of power systems. With AI, utilities can predict and prevent faults, optimize grid performance, and seamlessly integrate renewable energy sources into both rural and urban environments. Our goal is to provide insights for researchers, industry professionals, and policymakers on how AI can be harnessed to build more sustainable, resilient, and reliable energy systems. The insights shared here aim to help shape the future of power grids, positioning AI as a key player in the transition to cleaner, more efficient energy solutions.

The integration of artificial intelligence (AI) into computational materials science (CMS) has introduced powerful approaches for accelerating the discovery and optimization of advanced energy materials. As energy demands shift toward renewable systems, the development of efficient materials for batteries, fuel cells, and electrocatalysts becomes increasingly critical. This paper systematically reviews recent AI methodologies applied within CMS, particularly those leveraging density functional theory (DFT), molecular dynamics (MD), and kinetic Monte Carlo (KMC) simulations. Emphasis is placed on the use of machine learning (ML) models, including supervised learning, deep learning, and hybrid strategies for property prediction, structure optimization, and inverse design. The review categorizes current applications across key energy technologies and discusses how AI is reshaping material screening and development pipelines. It concludes with an outlook on future directions, highlighting the need for standardized datasets, interpretable models, and physics-informed frameworks to improve predictive accuracy and facilitate AI adoption in practical materials research.