Article Details

Abstract

This study addresses the challenges in higher education faculty performance evaluation, where existing indicator systems are often overly subjective, data-driven insights are difficult to operationalize, and current solutions lack adaptability for non-technical managers. Building upon a digital indicator framework that integrates both objective and subjective dimensions, an intelligent analysis system driven by locally deployed Large Language Models (LLMs) and autonomous agents is developed. The core of the system leverages Retrieval-Augmented Generation (RAG) technology to dynamically link private faculty data with a local knowledge base, enabling natural language queries and automated analyses through the agent. By integrating data querying, intelligent alerting, and interactive visualization into a unified platform, the system addresses the high costs and security concerns associated with traditional tools. Empirical applications demonstrate that the system significantly lowers the technical barrier to system use, allowing administrators without technical backgrounds to complete the entire workflow—from data processing to insight generation—within a single interface. This provides a practical, deployable solution for the digital transformation of faculty team management in higher education.

Keywords

faculty performance; indicator system; agent; workflow; Retrieval-Augmented Generation

References

1. [1] Yin Kui Research on the Path of Building Physics Teaching Staff in Local Universities under the Background of "Double First Class" [J]. Physics Teachers, 2020, 41 (11): 95-97
2. [2] Xia Xi Construction of Evaluation System for the Construction of University Teaching Staff under the Background of "Double First Class" [J]. Human Resources, 2025 (6): 23-25
3. [3] Tang Yunmei, Zhang Ye, Zhuang Li Construction of Evaluation System for the Construction of Teaching Staff in Applied Undergraduate Universities under the Background of "Double First Class": A Case Study of Jiangsu Province [J]. Literature and Education Materials, 2023 (2): 183-186
4. [4] Chen Yan, Ren Chao, Wang Qisi, etc Exploring the Evaluation of Subject Teaching Staff under the Background of "Double First Class" Construction [J]. Degree and Graduate Education, 2017 (10): 55-58
5. [5] Lin Sike, Xu Mengyi, Huang Sihua Construction of Performance Evaluation Index System for College Teachers: Analysis Based on Multivariate Statistical Methods [J]. Education and Teaching Forum, 2021 (52): 17-20
6. [6] Wang Guangyan, Li Yuanyuan, Qiu Xueqing, etc Empirical Study and Reflection on the Performance Evaluation Index System for College Teachers [J]. China Higher Education Research, 2008 (2): 46-49
7. [7] Song Zhuoran, Tan Lebin, Li Xin, etc Empirical Study on the Construction of Performance Evaluation Index System for College Teachers [J]. Exam Weekly, 2015 (80): 152
8. [8] Wang Danhui Exploration of the Performance Evaluation System and Practical Effectiveness of University Teachers [J]. Higher Education Forum, 2016 (3): 99-101106
9. [9] Lin Jieqin Research on the Reform of Performance Based Wages in Vocational Colleges in the New Era [J]. Journal of Vocational Colleges, 2021 (4): 101-103121
10. [10] Li Qin Review and Prospect of Performance Appraisal Research for Vocational College Teachers [J]. Journal of Guangdong Light Industry Vocational and Technical College, 2021, 20 (2): 19-23
11. [11] Jiang Jinyun Thoughts on the Construction of Performance Evaluation Index System for Vocational College Project Funds [J]. China Management Informatization, 2023, 26 (18): 28-30

Funding

Zhejiang Province College Students Innovation and Entrepreneurship Training Program Funding Project (S202610347085); Zhejiang Province College Student Science and Technology Innovation Activity Plan (New Talent Plan) Project (2026R446B0162026R446A008)

About the authors

Zhang Ruiqi (2005-), female, Han ethnicity, born in Hangzhou, Zhejiang Province, is currently pursuing a bachelor's degree with a research focus on data science and artificial intelligence applications; Corresponding author: Song Tao (1980-), male, Han ethnicity, born in Jurong, Jiangsu, lecturer, doctoral student, research direction: complex system modeling and big data analysis.