Optimizing Real-Time Classroom Resource Allocation Using Artificial Immune Systems

Abstract

Effective allocation of limited classroom resources—such as instructor attention, digital devices, and learning space—presents a dynamic optimization challenge. Drawing inspiration from the human immune system’s distributed pattern recognition and adaptive responses, this research introduces an Artificial Immune System (AIS) framework to manage real-time classroom resource allocation. Simulated classrooms and live pilot studies (N=90 teacher–student pairs) demonstrate that the AIS outperforms baseline scheduling rules by improving response time to student queries by 35%, balancing attention distribution (Gini leq 0.12), and reducing device idle time by 28%. These results suggest AIS offers a sustainable and responsive method for classroom management.