FEU Institute of Technology, also known simply as FEU Tech, is a coeducational, non-sectarian, private institution in Manila, Philippines. It offers courses in the fields of Engineering and Computer Studies. Established in 1992 as East Asia Computer Center, Inc.(EACCI), an educational partnership of Asia Pacific Computer Technology Center, Intelligent Wave Inc., and Far Eastern University.FEU Tech is an Institute of Technology. It is cited by the Commission on Higher Education as a Center of Development in IT Education (CoD).FEU Tech operates under the aegis of the Far Eastern University; it follows and is directly connected to the University. It is headed by Dr. Michael M. Alba as University President and Dr. Benson T. Tan as Senior Executive Director.Like its sister schools, FEU Diliman and FEU Alabang, the college runs on a trimester academic system. An academic year starts in the second week of August and ends every June.
The integration of artificial intelligence (AI) tools into educational settings is reshaping how students learn, create, and engage with digital technologies, particularly in fields such as technology and design education where virtual simulations and AI-assisted workflows are increasingly prevalent. Despite this momentum, student adoption of AI tools remains inconsistent, especially in developing regions where technological readiness and trust in emerging technologies vary widely. This study investigates factors influencing AI tool acceptance among 493 industrial technology students from a public university in Central Visayas, Philippines. Using structural equation modeling (SEM), the research examined the relationships among perceived usefulness, perceived ease of use, perceived risk, and behavioral intention to adopt AI-driven virtual simulation applications. Findings confirm that perceived usefulness and ease of use remain strong drivers of adoption intention, aligning with the Technology Acceptance Model (TAM). However, perceived risk demonstrated a significant negative influence on both perceptions and intentions, highlighting the impact of concerns related to data privacy, algorithmic transparency, and ethical implications of AI in design-oriented learning environments. Integrating perceived risk into TAM expands the explanatory power of the model in technical drafting and design education, offering a more comprehensive picture of how students evaluate and adopt AI-driven virtual simulation apps. This enriched perspective highlights the necessity for institutional strategies that build AI literacy, reinforce data governance mechanisms, and foster responsible engagement with emerging technologies. The results provide valuable insights for educators, curriculum designers, and policymakers working to advance AI-supported learning, particularly within resource-constrained and rapidly developing educational contexts.
As GenAI technologies become more pervasive in higher education (HE), scholars call for guidance on AI governance. To meet this need, a Delphi technique and collective writing was used in gathering expert perspectives from across 22 countries/locations and six continents. This resulted in the development of a HE GenAI policy/guidelines framework with eight core areas: (1) academic integrity, (2) ethical use and responsible use, (3) privacy and protection, (4) equitable access, (5) GenAI literacy, (6) integration strategy, (7) human oversight and accountability, and (8) institutional support and infrastructure. In addition, a six-part framework was developed to ensure that policies remain current and relevant: (1) creating a dedicated GenAI Committee, (2) conducting regularly scheduled policy reviews, (3) providing ongoing professional development and support, (4) communicating with all stakeholders, (5) evaluating the effectiveness and impact of GenAI, and 6) monitoring external developments. By providing a robust, eight-part framework for policy and guidelines, alongside a six-part mechanism for continued review, this study offers faculty, students, administrators, educational leaders, policymakers, and funders a responsible, adaptable, and consensus-driven blueprint for navigating the integration of GenAI in HE, ensuring that technological innovation serves pedagogical excellence.
Multiple sequence alignment (MSA) is a fundamental technique in computational biology that compares protein, DNA, or RNA sequences to identify regions of similarity reflecting functional, structural, or evolutionary relationships. This systematic literature review examines the diverse land-scape of multiple sequence alignment algorithms, categorizing them based on their underlying approaches and analyzing their strengths, limitations, and applications. We explore seven major categories of alignment methods: dynamic programming, progressive alignment, iterative refinement, Hidden Markov Model-based, consistency-based, structure-based, and machine learning-based approaches. Through comprehensive analysis of recent benchmarks and literature, we identify key innovations, performance characteristics, and emerging trends in the field. This review provides a detailed overview of the evolution of multiple sequence alignment algorithms and their applications in modern bioinformatics.
Gamification has come up as a powerful tool in increasing learning engagement and motivation in online education. As a proposal in this article, the designing and developing of AI-powered game modules in GameMaker for STEM education with adaptive learning capabilities in a gamified setup is ideal. The proposed model can regulate AI in a controlled manner to personalize learning experiences based on game gameplay with adaptive difficulty levels, immediate feedback, and analysis of performance data of learners. Based on developing a game using GameMaker, this research utilizes AI analytical capabilities for achieving learning outcomes in STEM education by aligning game targets with learning in STEM education. A quasi-experimental design was utilized to assign 60 purposively sampled STEM students into experimental and control groups in equal measures. The data gathering methods used in this study include pre and post-tests, in-game engagement metrics and usability surveys based on SUS and TAM. Salient findings show that the AI-enhanced module produced significantly greater learning gains $(M=19.80)$ than the non-adaptive module $(M$ =11.10). Improvements within both groups were significant (p < 0.001), while between-group comparison favored the experimental group $(\mathbf{p}=\mathbf{0. 0 0 2})$. Engagement metrics further revealed that the AI-adaptive group exhibited longer time-on-task, higher level-completion rates, and more active use of hints-indicating sustained engagement and effective utilization of adaptive support.
The Philippine energy sector is increasingly challenged by rising electricity demand, climate vulnerability, and persistent supply instability. Despite its long-standing reliance on geothermal and hydropower, the country remains largely dependent on fossil fuels, and energy access disparities persist, particularly in rural and low-income communities. This study presents a systems-based policy and technical assessment of renewable energy deployment in the Philippines, integrating energy infrastructure analysis, climate risk considerations, and circular resource utilization. A mixed-method approach is employed, combining secondary data analysis from national and international energy databases with comparative benchmarking against ASEAN and global renewable energy trends. The study incorporates conceptual systems and simulation-informed evaluation of decentralized renewable energy solutions, including rooftop solar systems and community-scale innovations supported by recovered materials. The result indicate that while the Philippines has achieved approximately 8.2 GW of installed clean energy capacity, this remains insufficient relative to the national target of 52.8 GW by 2040. At this point in time, the renewable energy mix is heavily concentrated in geothermal and hydropower, contributing around 18.5% of electricity generation, whereas solar and wind technologies remain underdeveloped at around 3.5%. The analysis also demonstrates that rooftop photovoltaic systems provide dual benefits by generating source of electricity and decreasing building heat gain, thereby lowering cooling demand in tropical environments, particularly through junk shop networks. This offers a viable pathway for low-cost renewable energy prototyping and education-driven innovation. This paper concludes that achieving a resilient and sustainable energy transition requires a multi-level strategy that integrates large-scale renewable deployment, decentralized energy systems, and circular economy principles, supported by policy alignment and capacity development.
