人工智能与情境化仿真驱动的《大地测量学基础》课程数智化教学改革研究

Research on the Digital-Intelligent Teaching Reform of “Fundamentals of Geodesy” Driven by Artificial Intelligence and Contextual Simulation

摘要: 在“新工科”建设和教育数智化转型的背景下，传统《大地测量学基础》课程存在实践环节薄弱、理论内容抽象、学习体验单一等问题。为解决上述痛点，研究提出基于人工智能与情境化仿真的教学改革方案。课程改革以AI智能计算、三维仿真展示和真实测绘任务为核心支撑，构建“知识可视化–过程智能化–实践情境化”的教学体系。通过引入人工智能算法示范(如最小二乘解算、误差传播仿真)、虚拟仿真实验(如椭球几何与重力场可视化)以及真实数据驱动的工程任务教学，实现理论教学与工程实践的深度融合。实践结果表明，该教学模式能显著提升学生对抽象理论的理解能力和数据建模能力，提高学习兴趣，促进创新意识的形成，为测绘类课程的数智化教学改革提供了可推广的范式。

Abstract: Against the backdrop of “New Engineering” construction and the digital-intelligent transformation of education, the traditional “Fundamentals of Geodesy” course faces several challenges, including weak practical components, abstract theoretical content, and a relatively monotonous learning experience. To address these issues, this study proposes a teaching reform scheme based on artificial intelligence and contextual simulation. The reform is supported by AI-based intelligent computation, 3D simulation visualization, and authentic surveying and mapping tasks, and aims to construct a teaching framework characterized by “knowledge visualization, process intelligentization, and practice contextualization”. By introducing AI algorithm demonstrations (such as least-squares adjustment and error-propagation simulation), virtual simulation experiments (such as visualizations of ellipsoidal geometry and gravity fields), and real-data-driven engineering task teaching, the approach achieves deep integration of theoretical instruction and engineering practice. Practical results show that this teaching model significantly enhances students’ understanding of abstract theories and their data-modeling capabilities, increases learning motivation, and fosters innovation awareness, providing a transferable paradigm for the digital-intelligent reform of surveying and mapping courses.