Application of AI optimization algorithms in dynamic allocation of educational resources

Abstract

Addressing the core contradiction of resource starvation and idle computing power in current smart education systems under massive heterogeneous concurrent requests, this paper proposes a dynamic resource allocation model integrating spatiotemporal graph perception and multi-agent reinforcement learning. By constructing a multi-dimensional tensor state space and a nonlinear truncated joint reward function, a cloud-edge collaborative architecture is used to decouple the globally optimal scheduling strategy for computing and bandwidth resources. Experimental results demonstrate that this model achieves significant technological breakthroughs in a high-concurrency scenario with 5000 terminals. Compared to the current best benchmark algorithm, the system's average throughput is significantly increased by 35.04%, end-to-end response latency is sharply reduced by 50.84%, and overall physical resource utilization steadily exceeds 95%, completely reshaping the resource pooling and adaptive allocation mechanism under high-fluctuation loads.