Artificial Intelligence (AI) is reshaping teacher education by providing intelligent tools that enhance pedagogy, student learning, and accessibility. This study explores how Microsoft Copilot, an AI-powered learning assistant, supports both teachers and students in integrating accessibility-focused coding into IT education. By assisting with debugging, code recommendations, and inclusive design principles, Copilot strengthens teachers' digital competence and helps students gain confidence in developing equitable digital solutions. A quantitative survey of Filipino IT instructors and students assessed awareness, adoption, and confidence in using Copilot for accessibility-oriented coding. Statistical analyses, including correlation and confidence interval testing, validated the results. Findings suggest that Copilot not only improves coding efficiency but also supports teachers in embedding accessibility concepts into their instruction—contributing to inclusive and innovative teaching models.

This paper investigates the effects of facilitation approaches of teachers on the engagement of the students in the asynchronous learning environment mediated by the AI chatbots. Though the chatbots provide the benefits of immediate feedback, personality-based feedback, and constant interaction, the outcome of the educational technology is largely dependent on the facilitation approach of the teachers. With the mixed-methods correlational approach, the study collected the data related to the usage logs of the chatbots, sentiment, scores of the quiz, and facilitation inputs with 100 undergraduate IT majors and 20 teachers. The results show that the engagement of the chatbot (r=.74,β=.45), emotional sentiment (r=.66,β=.33), and facilitation inputs of the teachers (r=.61,β=.29) are all reliable predictors of academic performance, together explaining the collective variance of approximately 65% for the scores of the quiz. The results of the study provide evidence on the complementary effects of human facilitation to optimize the effects of AI-facilitated learning. Furthermore, this study also promotes Sustainable Development Goal 4 (SDG4) to provide inclusive, effective, and quality learning for the users through the collaboration of human and AI resources.

Insulation coordination and surge protection devices (SPDs) are fundamental to the safety and reliability of modern power systems, especially in renewable energyintegrated grids. These systems protect critical electrical infrastructure from transient overvoltages, ensuring stable and sustainable operation. However, traditional methods, such as simulation-based analyses and manual fault detection, face challenges in scalability, adaptability, and efficiency, particularly in dynamic energy environments. Advancements in artificial intelligence (AI) and Internet of Things (IoT) technologies have introduced transformative capabilities for insulation coordination and SPDs. AI techniques, such as machine learning and neural networks, enable precise fault prediction, real-time monitoring, and adaptive control, significantly enhancing grid reliability. IoT-enabled SPDs further improve operational efficiency through predictive maintenance and continuous performance monitoring, aligning with sustainable energy goals. These innovations also address the needs of resilient infrastructure development, smart grid implementation, and urban sustainability. This paper explores the evolution of these systems, emphasizing the shift from traditional to AI-driven and hybrid approaches. By integrating advanced technologies, power systems can achieve enhanced reliability, efficiency, and resilience.

Part of speech tagging (POS) is crucial in natural language processing, identifying the grammatical categories of words in sentences. This research highlights the lack of focus on POS tagging for Asian languages, particularly Cebuano. Inadequate research on Cebuano religious text has hindered linguistic documentation and understanding its grammar and vocabulary. This study introduces a Parts-of-Speech Tagging for Cebuano utilizing a Modified Viterbi Algorithm. The researchers also apply a method for handling unfamiliar words. Results indicate that the algorithm performs exceptionally well on a religious text corpus comprising 50,000 datasets, achieving an accuracy of93%,precision of90%, recall of 90. 52%, and an F1-score of92%. These results highlight the algorithm's effectiveness in tackling language challenges within specific genres. Furthermore, the research supports the Sustainable Development Goals (SDGs) by promoting linguistic diversity and advancing inclusive language technologies. The study also provides valuable insights into Cebuano's linguistic characteristics and grammatical structures, laying a solid foundation for future research in natural language processing.

Cavite is a highly urbanized province situated near Metro Manila and has the highest population growth rate in the country. Water resource management and water-related risk mitigation is one of the major challenges the province faces. Cañas River Watershed is one of the major river systems in the province which covers major cities and municipalities. Effective streamflow monitoring in this watershed has not been achieved due to the inadequacy of monitoring stations around the province. This study aimed to develop an LSTM model to predict the streamflow in Cañas River Watershed at the Panaysanayan river gauge using the available weather parameters in two weather stations in the province, namely: Sangley Point Synoptic Station and Cavite State University (CvSU) Agrometeorological Station. Using the short-term data dated from 2014 to 2019 obtained from the stations and the river gage, the Long Short-Term Memory (LSTM) model successfully predicted the streamflow. Based on the model performance evaluation the values of Nash-Sutcliffe Efficiency (NSE) for the training and test were 0.90-0.91 and 0.87-0.89, respectively which indicates a high predictive accuracy. On the other hand, the Percent Bias (PBIAS) results in training and testing ranges 0.60% -8.04% and 1.92% -8.32%, respectively, which indicates a low bias prediction. The model tends to underestimate values, especially high magnitude flows. The RMSE-to-Standard Deviation Ration (RSR) results in training and testing ranges from 0.30-0.31 and 0.34-0.35, respectively, which indicates a good predictive power. The model results also show a good performance in developing a flow duration curve in the river to determine its dependable flow. The R2-value between the observed and predicted flow at different probability of exceedance is 0.9938. The dependable flow of Cañas River Watershed at Panaysanayan river gauge was 60 liters per second based on the observed flows and 61.12 liters per second based on the predicted flows.

3D printing, or additive manufacturing, is transforming electrical engineering by driving advancements in sustainable energy systems, urban infrastructure, and industrial innovation. This paper explores its applications in fabricating energy-efficient components, such as photovoltaics, wind turbine parts, and energy storage systems, as well as its role in advanced prototyping and smart grid technologies. The adoption of advanced materials, including conductive polymers and biodegradable composites, supports the development of renewable energy systems and customized solutions for urban and industrial applications. By reducing material waste, lowering production costs, and accelerating innovation cycles, 3D printing fosters sustainable manufacturing practices and resilient infrastructure development. Challenges such as material compatibility, scalability, and costs are discussed, alongside emerging technologies like artificial intelligence (AI) and the Internet of Things (IoT), which enhance optimization and broaden applications. This study highlights the critical role of 3D printing in advancing sustainable energy, urban development, and industrial modernization.

The integration of 3D printing technology into water infrastructure offers innovative solutions to the pressing challenges faced during disaster relief and response. This review paper explores the advancements in 3D printing technology and its applications in enhancing water infrastructure, especially during disaster relief operations. It compiles existing literature to highlight the current state of knowledge, focusing on the potential benefits, challenges, and future potential of 3 D printing for creating essential water infrastructure components. The study emphasizes the technology's capability to produce customized, on-demand solutions that are both cost-effective and efficient. By addressing the critical aspects of 3D printing applications in disaster scenarios, this paper aims to provide a comprehensive understanding of how this technology can revolutionize clean water accessibility and improve quality during emergencies.

In this study, we use various RNN architectures namely, RNN, Bi-LSTM, and GRU — alongside BERT to analyze sentiment across university departments. Our aim is a comparative analysis of these models in sentiment classification within education. We collected and pre-processed textual data from multiple departments for balanced training and validation. Results showed that traditional RNNs achieved 90% accuracy, Bi-LSTM 93%, and GRU 89%. BERT, leveraging its Transformer architecture, outperformed with 94% accuracy. These findings highlight the superiority of BERT in capturing complex language patterns for sentiment analysis. This study underscores the potential of advanced neural network architectures to gain insights into departmental sentiments, informing policy decisions and educational strategies. Aligning with sustainable development goals in education, we aim to use AI models to develop effective, inclusive, and responsive educational strategies, enhancing quality and accessibility.

The factor evaluation for sustainability implementation in post-disaster reconstruction projects was conducted using artificial neural network (ANN)-based sensitivity analysis. The current study involves the utilization of Levenberg-Marquardt algorithm (LMA) for the ANN model development. The findings demonstrated that the governing network structure is 10-3-1 utilizing input parameters (IP) including waste management (Sl), environmental impact management (S2), support of management (S3), construction cost (S4), public health and safety (S5), user security (S6), noise pollution (S7), energy consumption (S8), public services (S9), and recycling (S10). The results revealed that the governing model (GM) has a performance of correlation (R) = 0.99813 and Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) of 0.00110 and 0.4240/0, respectively. Moreover, a sensitivity analysis (SA) using Garson's algorithm (GA) reveals a trend of the relative value (RV) was observed to be construction cost > public services > support of management > user security > environmental impact management> noise pollution> public health and safety> energy consumption > recycling > waste management wherein the construction cost is the most influential parameter to the sustainability rating in post-disaster reconstruction projects (PDRP). The study shows how well ANN can detect the crucial elements that determine a project's sustainability rating in the post-disaster reconstruction projects.

The fast progression of Artificial Intelligence (AI) technology has elicited substantial ethical issues, especially within engineering fields that directly impact society. This study seeks to establish a framework for integrating Ethical AI ideas into engineering curriculum, therefore preparing future engineers to address the moral, social, and legal ramifications of AI. The framework incorporates Ethical AI principles into current course formats, encompassing introductory, enabling, and demonstrative courses, with particular focus on subjects like Science, Technology, and Society, Professional Engineering Ethics, and thesis/capstone projects. The paper recommends a curriculum update that complies with industry norms and equips students to embrace responsible AI practices, based on a thorough analysis of pertinent Commission on Higher Education (CHED) Memorandum Orders (CMOs) and literature. The research also presents evaluation rubrics to gauge students' comprehension and implementation of Ethical AI concepts in their academic projects. The paper suggests that integrating Ethical AI into engineering education enables universities to cultivate engineers who possess both technical proficiency and a robust ethical framework about AI technology.