知识图谱与AI融合驱动下仪器分析实验课程的教学改革与实践

Teaching Reform and Practice of Instrumental Analysis Experiment Course Driven by the Integration of Knowledge Graph and AI

摘要: 仪器分析实验课程是化学类专业的一门重要实践课程，本文针对传统教学中仪器分析实验存在知识点多而杂，理论与实践脱节等问题，构建了KG-AI双驱动的教学模式；通过构建课程知识图谱为学生建立系统性的认知框架；同时借助AI技术为实验教学全过程中提供智能辅助。该模式有效缩短实验准备时间，增强了课堂容量，并有利于学生主动思考与规范操作。

Abstract: Instrumental Analysis Experiment is a crucial practical course for chemistry majors. To address the traditional issues of fragmented knowledge and the theory-practice gap, this paper constructs a KG-AI dual-driven teaching model. By building a course knowledge graph, students are provided with a systematic cognitive framework, while AI technology offers intelligent assistance throughout the entire experimental teaching process. This model effectively shortens experiment preparation time, increases classroom capacity, and encourages students to think actively and operate in a standardized manner.