The adoption of innovative technologies is critical for improving teaching practices and student learning outcomes. Among these, artificial intelligence (AI) is emerging as a transformative tool capable of reshaping traditional educational paradigms. ChatGPT, a sophisticated language model developed by OpenAI, offers numerous opportunities for educators to enhance pedagogical effectiveness and streamline lesson preparation processes. This study explores the efficacy of ChatGPT in lesson preparation by surveying and interviewing teachers at Dr. Josefa Jara Martinez High School in the Philippines. It aims to understand their attitudes towards and experiences with integrating ChatGPT into their teaching practices. Despite the promising potential of AI in education, the adoption of such technologies in the Philippines faces significant barriers, including limited awareness, access issues, and concerns about technology integration. The findings reveal that while teachers recognize the benefits of using ChatGPT, such as improved efficiency and personalized instruction, challenges like lack of training and ethical concerns remain prevalent. The study underscores the need for comprehensive professional development programs and robust ethical guidelines to support the effective and responsible use of AI tools in education. The results show that teachers have a wide range of opinions, but many of them agree that ChatGPT has the potential to make lesson planning easier, offer individualized learning resources, and keep students interested in class. On the other hand, issues with consistency with curriculum requirements, dependability, and general efficacy were also apparent. The study sheds light on the challenges associated with integrating AI into education and makes recommendations for professional development, focused assistance, and ethical considerations to help high schools adopt AI technologies responsibly. Teachers can optimize learning experiences, improve teaching effectiveness, and give students the tools they need to succeed in the digital age by tackling these issues and utilizing AI's transformative potential.

This project was pushed through to engage people towards proper waste collection, through the utilization of mobile devices of the communities in different municipalities. The study aims to develop and implement a sustainable and efficient waste management collection system by informing the residents of the garbage truck collection schedule available on their mobile devices. Additionally, the platform utilizes optimization and decision support algorithms, including queuing algorithms, to receive and review complaints efficiently. The researcher employed an incremental software development methodology, allowing the software to be developed and tested even when requirements are still evolving. The study is descriptive-correlational, as it involves evaluating the developed system based on feedback from expert respondents. The evaluation yielded an overall mean performance score of 4.75, interpreted as “Strongly Agree,” indicating that the system is well-prepared for deployment.

This study enabled the researcher to create a graduate tracer monitoring platform. It aimed to provide a centralized channel to monitor institutions' graduates in terms of their job employment, to assess academic programs using modified instruments so necessary interventions may be provided, and to provide a matching algorithm that can be used both by industry partners and respective alumni. This study employed a Decision Support System and mapping recommendation analysis using a rule-based algorithm to evaluate the results of alumni program evaluation on five areas or dimensions, namely curriculum, faculty, facility, laboratory, and student services. It sets the threshold to determine if the results of the areas mentioned above are beyond the passing rate and implements the interventions for each area. The content management system was also used in this study to change the contents of the Alumni Program Evaluation, the interventions, the threshold, and many more. Based on the results, no intervention must be implemented in all areas/dimensions since the mean and the composite mean were more than the 4.0 threshold that was set in the proposed system. The overall rating of the respondents using the technology acceptance model numerical rating is 4.42 with an interpretation of “Agree.” As observed all criteria are rated either agree or strongly agree which indicates a high standard has been set in the development of the system. This means that the system is ready for deployment.

This study explores the beneficial uses of Microsoft Copilot as a support tool for Baliwag Polytechnic College instructors' lesson planning and activity design. Researchers evaluate the influence of Copilot on the creation of instructional content by examining the experiences and opinions of educators. The study demonstrates the advantages, difficulties, and opportunities for customization that come with incorporating Copilot into the curriculum. The results indicate that Copilot can significantly improve the effectiveness and caliber of lesson design, but also highlight certain implementation issues. This research offers insights into the future of technology-enhanced education and contributes to the expanding body of research on AI-assisted teaching strategies.

To ensure students achieve timely and satisfactory graduation, it's essential to assess their future performance based on ongoing academic records and implement instructional interventions. Within the educational context, students fall into two categories: regular and irregular, each governed by distinct academic regulations. Regular students follow a predetermined curriculum, which provides a clear path to graduation and enhanced access to required courses, facilitating efficient progress toward degree completion. On the other hand, irregular students encounter challenges such as disruptions and delays, necessitating additional time and support to fulfill degree requirements. Guiding both regular and irregular students and improving their study plans require appropriate guidance and academic intervention. To address the existing research gap, this study presents a Criteria-based Recommender Platform for Achieving Optimal Time-to-Graduation Utilizing a Backward Chaining Algorithm. This platform automatically generates a personalized study plan by considering predefined criteria and parameters, enabling students to evaluate the timeline for completing their degree program. By leveraging the backward chaining algorithm, the platform's predictive model captures intricate relationships and dependencies within the data, providing valuable insights and predictions. This adaptive approach continuously refines predictions based on new data, enhancing accuracy and utility in guiding decision-making processes related to study plan generation.