关键词: 本科教学评估, 大语言模型, 智能体, 数智化, 教育信息化

Abstract: As a critical component of higher education quality assurance, the scientific and rational implementation of undergraduate teaching audit and evaluation directly impacts the level of talent cultivation in universities. However, traditional manual review models are inefficient and subjective when faced with massive heterogeneous data, making it difficult to meet the demands for accuracy and standardization in undergraduate teaching evaluation. To address this, an intelligent undergraduate teaching evaluation system based on large language models and a multi-agent architecture—SmartEval was proposed. The system parses input content through a semantic understanding module, decomposes and schedules tasks using a planner, and integrates a retrieval-augmented generation module with three types of agents (question-answering, summarization, and diagnostics) to automate the entire process of "data collection—indicator analysis—decision support." Experimental validation based on the "1+3+3" series reports from the 2023 undergraduate teaching evaluation of selected universities demonstrates that SmartEval significantly outperforms existing mainstream large language models, such as GLM-4 and qwen2.5, in metrics such as question-answering accuracy, Rouge-L score for summarization, and F1 score for diagnostics. Furthermore, consistency tests with expert groups further validate the reliability of its results.

Key words: undergraduate teaching evaluation, large language model, agent, digital intelligence, educational informatization