Mental health concerns are on the rise among college students in the Philippines, where academic stress and limited access to counseling services continue to pose serious challenges. With mobile technology becoming more integrated into daily life, it offers a practical opportunity to support student well-being through accessible, self-help tools. This study presents the design and evaluation of a mobile application that combines art therapy and sound therapy to help reduce stress and promote relaxation among students in higher education. Guided by the Technology Acceptance Model (TAM), the research explored how users perceived the app's usefulness, ease of use, and overall experience. The app was developed using a blended Agile approach and tested by 50 purposively selected college students experiencing academic stress. Results showed strong user acceptance, with high ratings in ease of use (x=4.56), satisfaction (x=4.36), and intention to use (x=3.96). Perceived usefulness was strongly correlated with both satisfaction (r=0.73) and continued use (r=0.78), indicating that the app effectively supported stress relief and user engagement. This study contributes practical insights for integrating mobile wellness solutions in Philippine education, particularly in settings where traditional mental health support remains limited. It encourages the adoption of simple, evidencebased digital tools that promote emotional well-being and help bridge gaps in student support systems.

As social engineering threats continue to evolve, equipping learners with the skills to recognize and respond to digital manipulation has become a critical component of digital citizenship education. This paper explores the use of Microsoft Copilot—an AI-powered assistant—as a tool for generating instructional content that enhances awareness of social engineering tactics within digital learning environments. By integrating Copilot into the instructional design process, educators can rapidly produce scenario-based learning modules, phishing simulations, ethical discussion prompts, and adaptive assessments tailored to diverse learner profiles. The study presents a framework for human-AI collaboration in content creation, emphasizing pedagogical alignment, personalization, and scalability. Through case examples and prototype lesson plans, the paper demonstrates how Copilot can support educators in developing engaging, context-aware materials that foster critical thinking and digital resilience. This approach not only streamlines curriculum development but also positions generative AI as a strategic partner in advancing cybersecurity education across formal and informal learning contexts).

The rapid advancement of large language models (LLMs) such as ChatGPT, Claude, and Gemini presents new opportunities for personalized education. These AI systems are capable of tailoring learning experiences, providing instant feedback, and supporting self-paced study. However, questions remain regarding their pedagogical effectiveness, ethical implications, and alignment with educational goals. This study explores the opportunities and challenges of integrating LLMs into personalized learning environments in higher education. Using a mixed-method approach that combines experimental use of LLM-based tutoring with surveys and interviews of students and educators, the research evaluates their impact on engagement, learning outcomes, and inclusivity. Findings are expected to contribute to the discourse on AI in education while supporting the United Nations’ Sustainable Development Goal 4 (Quality Education), particularly in promoting inclusive and equitable access to learning opportunities through emerging technologies.

Cybersecurity literacy is increasingly vital in higher education, yet traditional teaching methods often fail to sustain student engagement. This paper introduces Aegis Academy, an AI-driven gamified learning platform that uniquely integrates adaptive feedback mechanisms with game-based elements to enhance cybersecurity awareness and skills. Unlike existing systems, Aegis Academy combines real-time personalization, rolebased analytics, and modular gamification tailored for academic settings. A pilot study involving 100 students and 20 instructors demonstrated significant improvements in phishing awareness (+22 %), overall scores (+18 %), and module completion rates (87% vs. 64%). Students rated the platform highly for engagement and learning impact, while instructors reported strong pedagogical alignment and usability. The platform supports Sustainable Development Goal 4 (SDG 4) by promoting inclusive, equitable, and engaging digital education. These findings suggest that Aegis Academy offers a scalable and effective model for cybersecurity instruction in higher education.

Allie’s Misadventures: Policies Sound Ridiculously Close to Fallacies” is a 3D single-player actionadventure game designed to combat misinformation by teaching players how to detect logical fallacies. Players assume the role of Allie, a bioroid journalist investigating media broadcasts and statements in a world dominated by disinformation. By infiltrating media towers under the King’s control, players learn to cross-examine and challenge deceptive arguments. The game aims to enhance critical thinking skills and reduce susceptibility to false information.

In the digital age, cybersecurity awareness is increasingly vital, especially for information technology (IT) students who will eventually design and manage secure systems. This paper presents CyberSAFE, a gamified web-based platform developed to enhance cybersecurity literacy through interactive modules, quizzes, and analytics dashboards. By integrating gamification with digital pedagogy principles, the platform promotes engagement, tracks learning progress, and identifies knowledge gaps in real time. A developmental-evaluative research methodology was employed, using pre- and post-tests, survey data, and system-generated analytics. Results indicate that students demonstrated measurable gains in cybersecurity knowledge, supported by positive perceptions of the platform’s usability, content relevance, and motivational impact. On a 5-point Likert scale, mean survey ratings exceeded 4.5, reflecting high acceptance and perceived effectiveness. These findings suggest that CyberSAFE can improve cybersecurity literacy while contributing to the Sustainable Development Goals (SDG 4: Quality Education and SDG 9: Industry, Innovation, and Infrastructure).

Generative AI offers promising potential to promote environmental sustainability through personalized recommendations that influence individual behavior. This study examines the factors influencing the adoption and actual use of generative AI recommendations for environmental sustainability among Gen Z users in the Philippines by integrating the Theory of Planned Behavior (TPB) and the Technology–Environmental, Economic, and Social Sustainability Theory (T-EESST) with key generative AI attributes, together with trust and perceived risk. Survey data were collected from 531 Gen Z users in higher education institutions in the National Capital Region (NCR), Philippines, and analyzed using a hybrid SEM and ANN approach. Results from SEM indicate that key AI attributes, namely perceived anthropomorphism, perceived intelligence, and perceived animacy, significantly influenced users’ attitude towards generative AI recommendations. Attitude, perceived behavioral control, and trust emerged as significant predictors of behavioral intention, which have an eventual positive relation to actual use and environmental sustainability outcomes. In contrast, subjective norms and perceived risk did not significantly affect behavioral intention, which may suggest that Gen Z users’ engagement with generative AI for environmental sustainability is primarily driven by internal evaluations, perceived capability, and trust rather than social pressure or risk concerns. Complementing these findings, the ANN analysis identified perceived behavioral control, attitude, and trust as the most important factors, reinforcing the robustness of the SEM results. Overall, this study integrates existing sustainability and technology-adoption literature by demonstrating how generative AI recommendations can support environmental sustainability among Gen Z users by combining behavioral theory, sustainability theory, and AI attributes through a hybrid SEM–ANN approach in the context of a developing country.

Solar energy deployment increasingly competes with prime agricultural lands, creating conflicts between energy goals and food security. To resolve these competing demands, our study identified where agrivoltaic systems—combining solar energy and agricultural production on the same land—should be strategically deployed across the Philippines. Using geospatial analysis which integrates terrain suitability, solar photovoltaic (PV) potential, and crop compatibility with shade-tolerant crops, we identified 10.09 million has of cropland suitable for agrivoltaics, representing 81.8% of the nation's agricultural land. Regions in the Mindanao island emerged as premier agrivoltaic deployment zones, combining maximum crop compatibility (15 shade-tolerant crops), high solar PV potential (683-687 MW), and substantial suitable areas (587,000-715,000 has). These findings provide actionable recommendations for strategic agrivoltaic deployment that advances both food security and renewable energy goals in the Philippines simultaneously.

Introduction: As digital platforms increasingly mediate language learning, the challenge is no longer simply how to deliver content online but how to design environments that cultivate authentic multilingual practice. While multilingualism has long been linked to enhanced metalinguistic awareness and domain-general cognitive flexibility, the role of multimodal digital environments in fostering these outcomes remains underexplored.

Purpose: Grounded in sociocognitive and multimodal interactionist perspectives, this study examines how a cross-device metaverse platform can support multilingual development through spatially organized, task-based, and avatar-mediated interaction. Specifically, it investigates whether multilingual engagement in language-zoned virtual spaces improves learners' communicative performance, affective engagement, and cognitive control compared to conventional instruction.

Methodology: Using a quasi-experimental cluster-assigned pretest-posttest control group design, learners engaged in communicative scenarios across English, Filipino, and Mandarin within language-zoned virtual spaces that cued role-appropriate language use. Data were collected using performance-based role-play assessments (code-switching accuracy, communicative competence), oral fluency measures (WPM), motivation and anxiety questionnaires, and a Stroop interference task to assess cognitive flexibility.

Findings: Compared to peers in a control condition, learners in the metaverse environment demonstrated significantly greater gains in code-switching accuracy, spoken fluency, motivational engagement, and cognitive control. Specifically, experimental participants showed improved context-appropriate language selection and reduced cross-language interference when shifting between English, Filipino, and Mandarin during task-based role-play scenarios. They also produced more fluent spoken output and demonstrated stronger communicative competence ratings in completing real-world interaction tasks. In addition, learners reported higher motivational engagement and cognitive results, further revealing improvements in inhibitory control and attentional regulation. Collectively, these outcomes suggest that spatially cued multilingual interaction in the metaverse supports integrated gains in linguistic performance and executive functioning.

Originality/Value: This study provides empirical evidence that multilingual development is shaped not only by linguistic input but by how digital learning ecologies choreograph spatial, social, and multimodal cues into context-responsive language use. By operationalizing multilingual interaction through spatial language zoning, avatar-mediated tasks, and AI-supported multilingual dialogue, the study positions the metaverse as a semiotically rich pedagogical ecology that can simultaneously foster code-switching competence, oral fluency, motivational engagement, and domain-general executive control. The findings advance multimodal multilingual education theory by demonstrating how context-sensitive interaction design can generate co-emergent communicative, affective, and cognitive benefits in multilingual learners.

The Neighborhood Median Pixel Method (NMPM) classifies land cover by summing per-band scores across 10 input features from Landsat-8 Operational Land Imager (OLI) data – bands 1 through 7 alongside three widely used index images: NDVI, NDWI, and NDBI. These features are typically treated equally within the classification framework, assuming uniform informational value across all bands and indices. However, indices such as NDVI, NDWI, and NDBI are specifically designed to highlight spatial properties of their respective land cover classes—particularly in urban settings—and are therefore expected to carry more relevant information for specific classification tasks. In this study, we experimented with different weighting schemes and found that giving greater emphasis to the indices led to a modest increase in overall classification accuracy, achieving an average overall accuracy of 0.9475 compared to 0.94 of the original implementations for the equal-weight baseline and other weighting strategies with a 9x9 neighborhood size. The results demonstrate how a targeted methodological innovation in image processing—assigning greater weight to highly relevant features—can enhance the reliable and efficient classification performance of the NMPM. This contributes to more accurate land cover mapping, particularly in complex urban environments, and supports data-driven development planning and resource management.