Research on Artificial Intelligence Education Evaluation System Based on Improved PSO Algorithm

• Published in: 2024 7th International Conference on Education, Network and Information Technology (ICENIT) pp. 81 - 85
• Main Author: Ouyang, Jing
• Format: Conference Proceeding
• Language: English
• Published: IEEE 16.08.2024
• Subjects: AHP; Analytic hierarchy process; Artificial intelligence; Economics; Education; Educational evaluation; Heuristic algorithms; Information technology; Particle swarm optimization; Planning; PSO; Support vector machines; Tomography; Weight
• DOI: 10.1109/ICENIT61951.2024.00022

As the rapid advancement of artificial intelligence, the popularization of knowledge in this area has gradually increased in basic education, but artificial intelligence education is still in the initial exploration stage. The research aims to establish a scientific artificial intelligence education evaluation system to scientifically evaluate the quality of teaching. The study adopts the Analytic Hierarchy Process to establish an indicator system and comprehensive evaluation method for artificial intelligence education evaluation, and introduces an improved particle swarm optimization algorithm to solve the consistency indicator function in the tomographic analysis method. By optimizing the particle swarm optimization algorithm to obtain the optimal weight of the Analytic Hierarchy Process indicators, an artificial intelligence education evaluation system is constructed. The results indicate that the average consistency ratio of the secondary indicators of the research system is 0.029, and all indicator values are less than 0.1, indicating high consistency and superior to other systems. The education evaluation system constructed through research can provide a decision-making basis for the planning of artificial intelligence education.