人工智能背景下应用型高校本科计量经济学课程教学改革探析

A Probe into the Teaching Reform of Undergraduate Econometrics Course in Application-Oriented Universities under the Background of Artificial Intelligence

摘要: 针对人工智能背景下应用型高校本科计量经济学课程理论与实验课时配比失衡、教学内容滞后于真实经济场景、考核偏重记忆与推导等问题，依据《“人工智能+教育”行动计划》关于“科学设计‘人工智能+’专业体系、课程体系、教学体系”的要求，本文提出“实验为体、理论为用、AI为器”的改革思路，从课时结构、教学内容、教学与考核、师资建设四个维度设计改革路径：构建“理论线上前置、课堂全程实践”的翻转课堂混合模式；以真实经济数据与AI工具链重组实验项目；以“AI做算、人做判”的原则组织人机协同课堂；构建过程与结果并重的多元评价体系；以教师AI素养培训与校企双向流动支撑实施。研究认为，人工智能的价值在于促使课程回归以真实问题为中心、以数据、模型、解释为闭环的应用属性，可为应用型高校经管类方法课程改革提供参考。

Abstract: In response to the imbalanced allocation of theoretical and experimental class hours, outdated teaching content relative to real-world economic scenarios, and assessment methods overly focused on memorization and derivation in undergraduate econometrics courses at applied-oriented universities under the context of artificial intelligence (AI), this paper proposes a reform framework guided by the “Artificial Intelligence + Education” Action Plan’s call for “scientific design of AI-integrated professional, curriculum, and teaching systems”. The proposed approach emphasizes “experimentation as the core, theory as application, and AI as the tool”, and outlines reform pathways across four dimensions: course hour structure, teaching content, instruction and assessment, and faculty development. Specifically, it advocates a flipped classroom hybrid model featuring online pre-class theory and full-class practice; reconfigures experimental projects using authentic economic data and AI toolchains; organizes human-AI collaborative classrooms based on the principle of “AI for computation, humans for judgment”; establishes a diversified evaluation system that values both process and outcomes; and supports implementation through AI literacy training for instructors and dual-directional faculty exchange between universities and enterprises. The study concludes that the value of AI lies in restoring courses to their application-oriented nature—centered on real-world problems and forming a closed loop of data, modeling, and interpretation—and offers insights for reforming methodology courses in economics and management at applied universities.