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.

The Digital Academic Information System is an integrated platform designed to support and manage core academic functions, including research, extension, and instruction. The system streamlines research management by tracking proposals, publications, and collaborations; facilitates extension services by organizing community engagement programs and reporting outcomes; and enhances instruction through tools for course management, faculty workload tracking, and student performance monitoring. By centralizing these features, the system promotes efficiency, transparency, and improved decision-making within academic institutions. This study focuses on the evaluation of the developed digital academic system that focuses on research, instruction and extension integration which processes essentials to state universities and colleges. The researcher used the descriptive- developmental type of research. The system provides a real-time overview of the status of the performance and accomplishment of the academic institution in the mentioned areas. The evaluation of a Digital Academic Information System (DAIS) using Agile methodology and software quality model assessment provides a dynamic and structured approach to system development and analysis. Agile enables iterative development with continuous stakeholder feedback, ensuring that evolving academic requirements are met efficiently. By integrating a software quality model—such as ISO/IEC 25010—the evaluation further assesses critical attributes like functionality, usability, reliability, and maintainability The system obtained the overall weighted mean of 3.73 which interpret as Excellent in terms of Product Quality evaluated by the IT Experts. This means that the system is approved by the IT Experts and highly recommended to use.

Effective teaching forms the bedrock of education, directly influencing program accreditation and institutional performance. A competitive and supportive learning environment, fostered by strong faculty performance, is crucial for an academic institution to achieve its vision and mission. This study incorporates Sustainable Development Goals (SDGs) principles, ensuring that faculty performance evaluation contributes to long-term educational sustainability. Addressing the pressing need for robust faculty performance assessment, data mining algorithms are employed to extract insightful information regarding effective instruction, utilizing both structured and unstructured data. The developed system aims to empower institutions to identify their strengths, address areas for improvement, and cultivate continuous growth in teaching and learning processes by discerning trends within faculty data. Furthermore, sentiment analysis methods are utilized to evaluate qualitative input, with Laravel 8.0 serving as the framework for algorithm implementation. Expert evaluations of the system yielded a grand mean score of 4.38, deemed 'Very Acceptable,' thereby affirming its reliability and efficacy in supporting faculty performance reviews and advancing SDG objectives.

In an era of accelerating digital transformation, optimizing resource management through effective data governance has become vital for local governments in developing nations such as the Philippines. This study introduces a data-driven governance platform designed to enhance resource allocation and decision-making in healthcare services through integrated forecasting algorithms and data governance principles. Anchored on a comprehensive framework for local data governance, the system centralizes, analyzes, and forecasts health-related data to support evidence-based planning and resource distribution. Employing both descriptive and developmental research designs, the study developed and tested the DALAY system using forecasting techniques such as exponential smoothing to predict medical supply needs and service demand. The findings demonstrate that integrating forecasting algorithms within a structured data governance framework can significantly improve resource efficiency, transparency, and responsiveness in local government operations. The system thus provides a replicable model for strengthening data-driven governance and optimizing community resource management in the Philippines. The findings indicate that robust data governance can lead to improved operational effectiveness, enhanced accountability, and ultimately better outcomes for citizens of the Philippines. This study aligns with SDG 9 that highlights the role of ICT in modernizing governance, fostering innovation, and improving data-driven decision making.

Air quality monitoring has become increasingly critical in urban environments, particularly in densely populated megacities like Manila, Philippines. This research presents the design and conceptual framework for a comprehensive web-based air quality monitoring and mapping system that leverages fuzzy logic algorithms to provide intelligent, real-time assessment of atmospheric conditions across Metro Manila. The proposed system addresses the inherent uncertainties and complexities associated with environmental data by implementing a sophisticated fuzzy inference system specifically calibrated for Manila's unique atmospheric conditions, pollution sources, and regulatory requirements. The research encompasses a thorough analysis of Manila's current air quality challenges, including the identification of primary pollutants such as particulate matter (PM2.5 and PM10), carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ground level ozone (O3). The proposed system architecture integrates multiple technological components including a distributed sensor network, centralized data processing infrastructure, fuzzy logic engine, web-based visualization platform, and real-time mapping capabilities. The fuzzy inference system is specifically designed to accommodate Manila's tropical climate conditions, high population density, and diverse pollution sources ranging from vehicular emissions to industrial activities. The methodology incorporates adaptive membership functions that adjust to seasonal variations and local environmental patterns, ensuring accurate and contextually relevant air quality assessments. The system design emphasizes scalability, real-time processing capabilities, and user accessibility through responsive web interfaces optimized for both desktop and mobile platforms. The technical implementation framework encompasses comprehensive hardware specifications for sensor deployment, software architecture for data processing and visualization, database design for efficient time-series data management, and API development for system integration and third-party access. Expected outcomes of this research include improved public awareness of air quality conditions, enhanced decision-making capabilities for environmental authorities, and the establishment of a robust foundation for future environmental monitoring initiatives in Manila and similar urban environments. The fuzzy logic approach provides a more nuanced and human-interpretable assessment of air quality compared to traditional crisp methodologies, enabling better communication of environmental risks to diverse stakeholder groups. This comprehensive study contributes to the growing knowledge in environmental informatics and smart city technologies, demonstrating the practical application of artificial intelligence techniques in addressing real-world environmental challenges. The research provides a detailed roadmap for implementing intelligent air quality monitoring systems in developing urban environments, with particular emphasis on cost-effectiveness, technological accessibility, and community engagement.

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.

Part-of-speech tagging (POS) is a vital process in natural language processing, enabling the identification of grammatical categories within sentences. This research emphasizes the lack of attention given to POS tagging for Asian languages, particularly Waray-waray. Limited studies on Waraywaray religious texts have hindered linguistic documentation and the deeper understanding of its grammar and vocabulary. To address this gap, the study introduces a POS tagging system for Waray-waray utilizing a Modified Viterbi Algorithm, which also incorporates a strategy for handling unfamiliar words. Evaluated on a corpus of 50,000 religious text datasets, the algorithm demonstrates outstanding performance-achieving an accuracy of 93%, precision of 90%, recall of 90.52%, and an F 1 score of 92%. These results underscore the algorithm's effectiveness in navigating linguistic challenges across specialized genres. Beyond technical contributions, the study promotes linguistic diversity and fosters inclusive language technologies, advancing the goals of the Sustainable Development Goals (SDGs). Specifically, it enhances language learning and literacy among Waray-waray speakers, supports inclusive education through computational tools for minority languages, and aligns with SDG 4 by providing foundational resources for mother-tongue instruction and educational content development. Additionally, it offers new insights into Waray-waray's grammatical structures, laying a robust groundwork for future linguistic and computational research. Beyond technical contributions, the study promotes linguistic diversity and fosters inclusive language technologies, advancing the goals of the Sustainable Development Goals (SDGs). Specifically, it enhances language learning and literacy among Waray-waray speakers, supports inclusive education through computational tools for minority languages, and aligns with SDG 4 by providing foundational resources for mother-tongue instruction and educational content development. Additionally, it offers new insights into Waray-waray's grammatical structures, laying a robust groundwork for future linguistic and computational research.

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.

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.

Generative AI tools are increasingly transforming multimedia arts courses by streamlining creative processes and expanding the possibilities for artistic expression. The integration of these tools into creative platforms has enabled students to enhance their productivity and experiment with innovative techniques, fostering an environment where digital artistry can thrive. Despite these benefits, the use of AI raises significant concerns regarding originality, artistic authenticity, and the potential erosion of traditional skills. Analysis using the technology acceptance model (TAM) reveals that students broadly accept these tools due to their perceived utility and ease of use, yet they remain cautious about their long-term impact on creative development. A word cloud analysis of student feedback illustrates a diverse array of sentiments, combining enthusiasm for the technological advancements with caution and ethical deliberation about the role of AI in art. Sentiment analysis further indicates an overall optimistic view toward the integration of generative AI, while also uncovering persistent concerns about ethical issues, such as copyright and the fair attribution of creative work. These findings highlight the critical need for educational strategies that balance the benefits of AI-driven efficiency and innovation with the preservation of traditional artistic practices. Future research should explore methods to integrate AI into multimedia arts curricula in a way that augments human creativity and safeguards the unique qualities of handcrafted art, ensuring that technological progress enhances rather than compromises artistic integrity.