Mental health concerns among college students continue to rise due to academic pressures, psychosocial stressors, and limited access to mental health services, particularly in the Philippine higher education context. This study presents the design, development, and evaluation of a mobile mental wellness application that integrates art therapy and sound therapy to support stress reduction and emotional well-being among university students. The application provides a multisensory experience through interactive digital coloring activities and customizable ambient soundscapes designed to promote relaxation and mindfulness. A developmental research design was employed, consisting of a development phase and an evaluation phase. The application was developed over 12 months using a blended Agile approach that combined Scrum, Extreme Programming, and Feature-Driven Development to ensure iterative, user-centered design. Evaluation involved purposive sampling of 500 college students experiencing moderate to high academic stress, complemented by expert assessment using the ISO/IEC 25010 Software Quality Model. Quality characteristics assessed included functionality, usability, performance efficiency, compatibility, reliability, maintainability, and portability. Results yielded a grand mean score of 3.66, interpreted as Highly Acceptable, indicating that the application met international software quality standards. Most quality attributes received highly acceptable ratings, demonstrating strong usability, technical performance, and cross-platform reliability. These findings suggest that the application is a viable, scalable, and user-centered digital wellness tool. The study underscores the role of educational technology in addressing mental health gaps in higher education and supports the integration of ISO-aligned mobile wellness applications into student support systems.

This study explores the influence of GAN-generated deepfakes within the creative media industry by applying a hybrid framework that integrates the Naïve Bayes algorithm and the Expectation Confirmation Model (ECM). As deepfake technologies become increasingly prevalent in filmmaking, digital art, and online content, they raise both creative opportunities and ethical concerns. The research employed a qualitative exploratory design involving 25 purposively selected participants from the Philippine creative sector, all of whom had experience with digital content production. Using a Naïve Bayes classifier, participant responses were analyzed for sentiment trends, revealing a majority of positive or neutral perceptions when deepfakes were applied ethically and creatively. The ECM component provided behavioral insights, demonstrating that user satisfaction was highest when the perceived performance of deepfake content met or exceeded initial expectations. Conversely, negative disconfirmation, often linked to ethical discomfort or perceived misuse, led to dissatisfaction and rejection of the technology. The findings emphasize the importance of managing audience expectations and embedding ethical standards into creative workflows involving synthetic media. By combining computational sentiment analysis with user-centric evaluation, this study contributes a scalable, interdisciplinary framework for assessing the reception and impact of AI-driven media innovations.

This study explores the impact of deepfake media on the creative and entertainment industry, with insights drawn from GAN-based deep learning models. Deepfakes offer powerful creative potential, enabling digital resurrection of performers, virtual concerts, and enhanced storytelling techniques. At the same time, their hyperrealism raises ethical concerns around consent, authorship, and audience transparency. Using the Diffusion of Innovations theory, the research finds that deepfakes are transitioning from early adoption to broader use among content creators and media professionals, while public acceptance remains cautious due to issues of trust and misuse. The same qualities that make deepfakes compelling in entertainment also make them dangerous tools for misinformation, fraud, and non-consensual content. As such, the study highlights the need for industry guidelines, public education, and updated legal frameworks to balance innovation with protection. Ultimately, the future of deepfake media in entertainment hinges on responsible integration and proactive safeguards to ensure its creative promise does not outweigh its societal risks.

This study developed the Digital Banks PH Notebook, a mobile application designed to support financial literacy among Filipinos by optimizing savings through high-yield digital banking platforms. The application featured a savings portfolio tracker, interest forecasting calculator, and savings goal management to address gaps in financial planning and savings behavior. Development followed a blended Agile methodology integrating Scrum, Extreme Programming, and Feature-Driven Development, ensuring iterative improvements aligned with user needs. Software quality was assessed using the ISO/IEC 25010 model, while qualitative feedback was analyzed through word cloud visualization to capture user sentiment and key focus areas. Findings indicated that the application effectively enhanced users' understanding of savings strategies and promoted responsible saving practices. The tool successfully connected the opportunities presented by digital banking with the practical requirements of financial education, providing users with actionable insights to manage their savings more strategically. By leveraging agile development practices and rigorous evaluation frameworks, the project demonstrated that technology-driven solutions can play a significant role in advancing financial literacy and supporting sustainable financial behaviors in an evolving digital economy.

An AI-driven academic path forecasting system is proposed to support data-informed advising and early academic intervention in higher education. In the Philippine context, where delayed graduation, student dropouts and lack of personalized academic guidance persist, machine learning in education offers a scalable and intelligent solution. The system combines three educational data mining techniques: a Long Short-Term Memory (LSTM) network for course sequence prediction, a decision tree classifier for student progress classification as regular or irregular and a K-Means clustering algorithm for grouping students based on academic trajectories. These models are developed in TensorFlow and deployed on a web platform built with CodeIgniter, enabling functionalities such as academic path forecasting, curriculum tracking and real-time risk alerts. Evaluation shows that the LSTM model achieves strong precision and recall in predicting next-term courses, while the decision tree classifier accurately detects off-track students with interpretable decision rules. K-Means clustering reveals meaningful groupings aligned with academic outcomes, further supporting early identification of at-risk learners. Confusion matrix analysis confirms high model accuracy across tasks. By integrating AI into higher education through course prediction, student classification and cluster-based insights, the system offers a practical framework for enhancing student success through targeted academic support.