Research on the Optimization of Smart Teaching Resource Allocation in Higher Education Based on PSO Algorithm
Year: 2025
Type: conference-paper
Abstract: Information technology has been growing faster recently, smart education advanced to be the trend in the present day, particularly in higher education. The issue of how to effectively allocate teaching resources to improve education quality has become decisive in the context of higher education. In this paper, the application of the particle swarm optimization (PSO) in higher education was proposed as a teaching resource optimization model. The PSO algorithm is used to satisfy the crucial features of the resource optimization model created from the difficulties and demands in the current higher education resource allocation. The practical results of the experimental part show that the PSO-based optimization scheme is a good way to make both teaching and research resource allocation, more efficient in institutions of higher education, its main advantage is that it reduces resource waste, and it is really a fresh way for the process of resource optimization in higher education. This study demonstrates that PSO-based optimization for smart education resources offers multiple feasible application scenarios, but still exhibits limitations in convergence speed and scalability. Accordingly, this paper discusses both the key advances enabled by PSO in smart teaching and its inherent challenges, and outlines directions for future hybrid and data-driven extensions.
Authors: Wenqing Li, Xinyu Fu
Institutions: Fujian Business University, Fujian Normal University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: gold
OpenAlex ID: W4414425279