ANN-Based Predictive Modeling of Co-Intelligent Learning Transformation in Philippine Higher Education
Year: 2026
Type: conference-paper
Study design: Observational Study
Abstract: 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$\mathbf{R}^{\mathbf{2}}$, RMSE, MAE, and MAPE. Results show that co-intelligent learning transformation is shaped by technological, pedagogical, ethical, governance, and learner-level determinants.
Language: English
Subfield: Education
Field: Social Sciences
Domain: Social Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W7202355045