人工智能技术已上升为国家战略支柱,其基础性课程人工智能导论不仅作为AI人才培育的启蒙环节,而且也是教育部建立虚拟教研室的首批核心课程,面临教学内容知识体系庞杂、概念抽象等问题,对教学设计与学习策略提出新要求。本研究立足知识图谱的语义网络特性,聚焦该课程构建多维度知识表征体系：基于知识图谱结构化、动态化知识组织优势,系统梳理课程核心知识单元及关联网络、阐释其核心架构,重点突破传统教学模式中知识离散化、认知浅表化等瓶颈问题。在这样的背景下,本研究成果将为数据科学与大数据技术专业课程建设体系优化与教育数字化转型提供创新性的方法论支撑。

Artificial intelligence technology has risen to become a national strategic pillar. Its foundational course Introduction to Artificial Intelligence not only serves as an enlightening link in AI talent cultivation, but also is one of the first core courses for the establishment of virtual teaching and research offices by the Ministry of Education. Faced with issues such as a complex knowledge system in teaching content and abstract concepts, new requirements are placed on teaching design and learning strategies. Based on the semantic network characteristics of knowledge graphs, this study focuses on constructing a multi-dimensional knowledge representation system for this course. Leveraging the structural and dynamic knowledge organization advantages of knowledge graphs, it systematically organizes the core knowledge units and associated networks of the course, explains its core architecture, and focuses on breaking through bottlenecks such as knowledge discretization and cognitive superficiality in traditional teaching modes. Against this backdrop, the research results will provide innovative methodological support for the optimization of the curriculum construction system for data science and big data technology majors and the digital transformation of education.