ANN-Based Predictive Modeling of Co-Intelligent Learning Transformation in Philippine Higher Education
2026 9th International Conference on Artificial Intelligence and Big Data (ICAIBD), (2026), pp. 730-735
Dante L. Silva
a
,
Kevin Lawrence M. De Jesus
b
,
Jimmy G. Catanes
c
,
Nenita B. Nagarit
d
,
Crispin S. Lictaoa
e
,
Meriam P. Leopoldo
f
a School of Civil, Environmental and Geological Engineering, Mapúa University, Manila, Philippines
b Department of Civil Engineering, FEU Institute of Technology, Manila, Philippines
c Commission on Higher Education, Manila, Philippines
d Philippine Association of Colleges and University, Commission on Accreditation Manila, Philippines
e Civil Engineering Department, Adamson University, Manila, Philippines
f College of Engineering and Architecture, Mapúa Malayan Colleges Mindanao, Davao City, Philippines
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 R2, RMSE, MAE, and MAPE. Results show that co-intelligent learning transformation is shaped by technological, pedagogical, ethical, governance, and learner-level determinants.