人工智能赋能《分析化学》智慧课程建设实践与范式启示

Practice and Paradigm Implications of AI-Empowered Smart Course Construction in Analytical Chemistry

摘要: 在教育数字化转型与人工智能深度融入高等教育的背景下，《分析化学》AI智慧课程以“能力–问题–知识”三层知识图谱为骨架，以课程智能体与AI工具箱为引擎，以全过程学情数据为脉络，探索了一条“图谱驱动、智能协同、数据评价、混合育人”的课程建设路径。文章从顶层设计、五大应用场景、试运行成效、特色成果与范式启示五个维度，系统梳理该课程在资源建设、智慧助教、智慧助学、虚拟学习社区、智能管评等方面的实践进程，分析其“课前图谱预习–课中灵动互动–课后项目实践”的混合式教学闭环，并对AI智慧课程的可持续建设提出思考。

Abstract: In the context of the digital transformation of education and the deep integration of artificial intelligence into higher education, the AI-empowered smart course of Analytical Chemistry takes a three-tier knowledge graph of “competency - problem - knowledge” as its backbone, course agents and an AI toolbox as its engines, and full-process learning data as its thread. It explores a course-construction pathway characterized by “graph-driven design, intelligent collaboration, data-based evaluation, and blended education”. From five dimensions—top-level design, five application scenarios, trial-run outcomes, distinctive achievements, and paradigmatic implications—this study systematically reviews the course’s implementation process in resource development, intelligent teaching assistance, intelligent learning assistance, virtual learning communities, and intelligent management and evaluation. It further analyzes the blended teaching loop of “pre-class knowledge-graph preview - in-class dynamic interaction - post-class project practice”, and offers reflections on the sustainable development of AI-empowered smart courses.