This study investigates the role of neuro-inspired learning technologies in promoting inclusive cognitive learning and digital equity within higher education. Data was obtained from 420 students utilizing a quantitative explanatory design, focusing on their sustained exposure to neuro-inspired digital learning environments. The analysis evaluated the impacts on attention, memory, engagement, and problem-solving, together with dimensions of digital equity, learner diversity, and accessibility. The findings indicated that these technologies significantly enhanced cognitive learning results, with engagement being the most significant factor. Experiential equity served as a significant mediator, highlighting the need for major and equitable learning experience. Moreover, design features focused on diversity and accessibility improved cognitive outcomes and ensured equitable participation. The results indicate that successful adoption relies on technological innovation, neuroinspired design, digital equity, and learner-centered inclusivity.

The competency in Circular Economy (CE) has gained significance in engineering education, however its development is inconsistent among higher education institutions. This study established and validated an evidence-based pedagogical framework to enhance CE competencies in engineering programs in the Philippines. Data from engineering students and faculty were analyzed using a quantitative multiphase approach, employing descriptive statistics, multiple regression, artificial neural networks, and structural equation modeling. The findings indicated that transformative pedagogical approaches and AI-enabled digital learning environments were the most significant predictors of CE competency development, while institutional reputation-related factors were comparatively less influential. The results revealed the Inclusive AI-enabled Transformative Circular Economy Pedagogical framework to facilitate curricular alignment, digital integration, and outcomesbased quality improvement in engineering education.

Predicting renewal demand in old residential areas is critical for effective urban planning, particularly in addressing deteriorating infrastructure and maximizing resource distribution. Conventional statistical models often fail to capture complex and non-linear relationships among socio-economic, institutional, and environmental factors influencing residents' decision-making. This study utilizes an Artificial Neural Network (ANN) to predict renewal demand and determine the most influential factors (MIF), based on a dataset of 800 responses and twelve input factors classified into building involvement (BI), trust in government (TG), and local government image (LGI). The best ANN architecture demonstrated strong prediction performance, evidenced by a correlation coefficient of R=0.9523, Mean Squared Error (MSE) = 0.0192, and Mean Absolute Percentage Error (MAPE) = 3.4837%, signifying good accuracy and generalization capacity. Sensitivity analysis employing Garson's algorithm (GA) indicated that trust in government capability, fairness in execution, and decision transparency are the MIFs, followed by emotional attachment and place identity. The results emphasize that renewal demand is primarily driven by institutional trust and psychological attachment rather than physical conditions. These findings establish a data-driven basis for developing more responsive, inclusive, and sustainable urban renewal initiatives.

The rapid integration of artificial intelligence (AI) in higher education offers opportunities to improve learning, teaching, and institutional decision-making. This study developed an artificial neural network (ANN)-based predictive model to examine nonlinear relationships between institutional determinants and co-intelligent learning outcomes in Philippine higher education institutions. Survey data were collected from 612 stakeholders, including students, faculty members, administrators/quality assurance officers, and IT or educational technology staff. Five determinant clusters served as ANN inputs including AI learning ecosystem readiness, human capability and pedagogical readiness, integrity-by-design and academic integrity climate, governance, ethics and trust, and learner disposition and cognitive management. Three outcome clusters were modeled including AI-enhanced learning performance, self-regulated learning gains, and sustained engagement and higher-order thinking. A 5-12-8-3 multilayer perceptron ANN was trained using the Adam optimizer and evaluated using R2, RMSE, MAE, and MAPE. Results show that co-intelligent learning transformation is shaped by technological, pedagogical, ethical, governance, and learner-level determinants.

Virtual laboratories based on Building Information Modeling (BIM) are increasingly used in civil engineering (CE) education to connect theoretical instruction with industryoriented digital practice. However, limited empirical evidence identifies and prioritizes the factors influencing their effectiveness, particularly from the perspective of engineering educators. This study evaluates the effectiveness of BIM-based virtual laboratories in the Bachelor of Science in Civil Engineering program using Artificial Neural Network (ANN) modeling and sensitivity analysis. Data gathered from engineereducators were grouped into five categories: technological, pedagogical, learner-related, environmental and institutional, and outcome-related factors. The ANN model mapped these factors to perceived overall effectiveness, while sensitivity analysis quantified their relative importance. Results indicated that system usability was the most influential factor, followed by integration with coursework, instructor expertise, system performance and reliability, and student self-efficacy. These findings provide evidence-based guidance for improving technological design, pedagogical alignment, faculty capacity, and sustainable implementation of BIM-enabled virtual laboratories in CE education.