从工具到思维：AI时代研究生《应用数理统计》课程内容重构探索

From Tools to Thinking: Reconstructing the Content of Applied Mathematical Statistics for Graduate Students in the AI Era

摘要: 针对AI时代统计课应当教什么这一根本问题，本文在厘清现行课程时代差距的基础上，提出课程目标应完成从掌握计算方法到培养与AI协同的统计思维的理念转型，并构建问题转化、统计推理与元认知、批判性评估、人机协作四维能力框架。依托该框架，设计了教学内容的“加减法”重构策略，明确了削减标准与增加模块，并以一元线性回归分析为例提供了可操作的课程大纲示例；同时设计了与之配套的多元考核方案，从师资、资源、学生基础三个维度分析了实施挑战与应对策略，使研究结论兼具理论依据与操作参考。

Abstract: Addressing the fundamental question of what statistics courses should teach in the AI era, this paper first identifies the generational gap in the current curriculum regarding its objectives and content organization. It then proposes a conceptual shift in course objectives—from mastering statistical computation methods to cultivating statistical thinking in collaboration with AI—and constructs a four-dimensional competency framework comprising problem transformation, statistical reasoning and metacognition, critical evaluation, and human-AI collaboration. Based on this framework, a “reduction and addition” strategy for teaching content is designed, specifying what to reduce and what to add, with a teachable syllabus example provided using the chapter on simple linear regression analysis. A corresponding diversified assessment scheme is also developed. Furthermore, the paper analyzes the implementation challenges and coping strategies from three dimensions—faculty, teaching resources, and student diversity—so as to render the research conclusions both theoretically grounded and practically applicable.