Research on Artificial Intelligence Technical Support of WSR Methodology in Performance Management of English Teachers in Colleges and Universities

Abstract

Performance management evaluation of university English teachers is of great significance for improving the current status of English teachers' capabilities, and the relative importance of evaluation indicators is a prerequisite for ensuring the accuracy of evaluation results. This article is based on the WSR system methodology, incorporating artificial intelligence technology into its causal framework factors, and utilizing research projects for quantification to construct a performance management model for university English teachers. Based on this, an evaluation indicator system for the performance management of university English teachers was established, and a comprehensive evaluation model for English teacher performance management was developed using entropy weighting and grey relational analysis. To further explore the factors influencing the performance management level of university English teachers, the fsQCA method was introduced to conduct a configurational analysis of English teacher performance management. The study found that the “logic” component in the WSR system methodology has a greater influence (with a weight value of 0.3458), and the comprehensive score for the performance management level of university English teachers in the selected cases fluctuated between 0.32 and 0.87 points. There are two configuration paths to promote high-performance management levels among university English teachers, both of which highlight the importance of research capabilities, specifically the efficient application of artificial intelligence (AI) technology. Therefore, university English teachers need to expand the application of AI technology and accelerate the intelligent reform of English teaching to achieve the development of high-performance management levels.