Artificial Intelligence (AI) is reshaping higher education by streamlining tasks, supporting personalized learning, and enhancing student engagement. Yet, these benefits coexist with risks of dependence, reduced critical thinking, and inequities-a duality termed the relief paradox. This study examined the perceptions of 138 IT students at FEU Institute of Technology to explore how they experience both the relief and paradoxical burdens of AI in education, with attention to Sustainable Development Goal 4 (SDG 4) on inclusive and equitable quality education. Using a descriptiveanalytic design, validated survey instruments, and both descriptive and inferential statistics, results showed that students generally recognized AI's potential to reduce workload and improve inclusivity, while also expressing concern over ethical issues and overdependence. The study underscores the need for balanced integration of AI in education, offering recommendations for curriculum, policy, and capacity-building to ensure responsible adoption.

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.

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.

Upcycling is a sustainable solution that mitigates environmental changes, focusing on climate action, by extending the lifecycle of products and reducing wastage. This study investigates Filipino consumers’ behavioral intention to use an upcycling e-marketplace, highlighting the intersection of sustainability, consumer psychology, and digital platforms. Despite growing interest in circular economy models, adoption drivers in this domain remain underexplored and are rarely modeled with predictive analytics. To address this gap, the study collected data from 500 Filipino participants capturing environmental knowledge and concern, perceived ease of use and usefulness, attitude toward use, perceived behavioral control, subjective norms, user demand, and intention, then analyzed using multiple machine learning algorithms, namely, Decision Tree, Random Forest, Gradient Boosting, XGBoost, K-Nearest Neighbors, and Support Vector Machine. Among these, the Decision Tree model demonstrated the best balance of predictive accuracy (95%), precision (96%), and recall (91%), suggesting strong classification capability. Analysis of the importance of features revealed that Perceived Usefulness, Attitude Toward Use, and Subjective Norms were the most influential predictors of adoption, outweighing traditional environmental concerns. These findings underscore the importance of designing upcycling platforms that emphasize practical value, user convenience, and social validation. The study concludes that sustainable behavior is more likely when aligned with personal benefits and peer influence, rather than relying solely on environmental appeals.

Marine ecosystems face unprecedented threats from pollution, overfishing, and climate change, creating an urgent need for conservation initiatives. While consumer awareness of ocean degradation is increasing, there remains a persistent gap between environmental concern and actual purchasing behavior toward sustainable products. This study aims to examine public readiness to adopt OceanGuardian, a sustainable e-commerce platform for marine conservation products, by integrating behavioral, technological, and environmental perspectives. Using a modified Extended Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework, survey data from 600 respondents were analyzed with machine learning models, including Support Vector Machines, Random Forests, and XGBoost, to identify key determinants of consumer intention and use behavior. Results indicate that social influence, performance expectancy, affordability, and habit formation significantly predict adoption, with Support Vector Machines achieving the highest predictive accuracy (92.5%). The findings highlight the potential of artificial intelligence to enhance consumer behavior analysis while recognizing challenges such as economic barriers and consumer skepticism. The study offers theoretical contributions by extending UTAUT2 with environmental factors and provides practical insights for policymakers and businesses to design strategies that foster sustainable shopping and strengthen marine conservation efforts.

The accuracy of classification algorithms is significantly influenced by the quality and structure of input data. In this light, effective pre-processing is crucial for boosting the generalization capabilities of supervised machine learning models. This study addresses key challenges in data preparation, including the treatment of continuous attributes, imputation of missing values, and management of high-dimensional features. To overcome these obstacles, we propose an innovative hybrid preprocessing strategy that synthesizes multiple techniques into a unified framework. By tailoring specific methods to the characteristics of diverse datasets, this hybrid approach enhances both the accuracy and robustness of the classification results. Through the promotion of intelligent, data-driven solutions that can be applied in multiple sectors, the findings support the Sustainable Development Goal 9 (SDG 9), which focuses on Industry, Innovation and Infrastructure.

This paper discusses that the Digital marketplaces play a crucial role in connecting startups with potential investors, yet their adoption success depends on understanding the key factors influencing user intention. Predicting adoption behaviors accurately can help improve engagement and ensure platform sustainability. The study applies the Unified Theory of Acceptance and Use of Technology (UTAUT) framework to identify key adoption factors including Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), Trust (TR), and Government Support (GS).and this has been widely applied to study technology adoption, limited research integrates this framework with machine learning models to predict adoption intention in e-marketplaces for startups. This study aims to develop machine learning-based prediction models for StartSmart an e-marketplace linking startups and investors and identify the most influential factors affecting adoption intention based on the UTAUT framework. Data from 542 respondents were analyzed using six machine learning techniques: Decision Trees (DT), Random Forests (RF), Gradient Boosting (GRB), XGBoost (XGB), K-Nearest Neighbors (KNN), and Support Vector Machines (SVM).Results indicate that DT achieved the highest accuracy (0.93) and precision (0.94), while RF obtained the highest AUC-ROC score (0.98). Analysis of feature importance revealed that PE and EE were the most significant predictors of adoption, followed by TR and GS. These findings provide valuable insights for platform developers to prioritize usability and performance improvements, and for policymakers to strengthen trust and government support. The study also highlights the potential of combining UTAUT with machine learning to enhance predictive accuracy in digital adoption research.

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.

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.