AI-empowered international finance course: Embedding paths, teaching effects, and optimization strategies
AI赋能国际金融课程：嵌入路径、教学效果与优化策略

摘要

目的 探讨人工智能技术对金融知识形成与传播的深刻影响，针对当前国际金融课程中存在的课程内容更新滞后、学生迁移能力不足等结构性问题，探索有效的教学改革路径。方法 以“系统化教学—课程思政—前沿引导”三位一体思路为指导，在金融工程、经济学专业大二学生的相关课程中，将“AI辅助实时案例审读”与“AI驱动政策模拟推演”作为两条嵌入路径，并通过课堂观察与前后测数据分析教学效果。结果 课堂观察与前后测描述性数据提示，以AI输出为批判对象而非知识来源的教学设计，可能有助于激活学生的审读意识；约14.7%的学生表现出工具依赖倾向，提示浅层应用AI可能存在削弱自主思维的风险。上述发现为探索性观察，尚需统计检验与对照设计加以验证。结论 基于上述发现，本文提出从“考知识”转向“考判断”的评价优化策略。

Abstract

Objective To explore the profound impact of artificial intelligence technology on the formation and dissemination of financial knowledge, and to seek effective teaching reform paths in response to the structural problems existing in current international finance courses, such as lagging course content updates and insufficient student transfer ability.
Methods Guided by the trinity approach of “systematic teaching—curriculum-based ideological and political education—frontier guidance,” two embedded pathways— “AI-assisted real-time case reading and analysis” and “AI-driven policy simulation and deduction”—were incorporated into relevant courses for sophomore students majoring in financial engineering and economics. Teaching effectiveness was analyzed through classroom observation and pre- and post-test data.
Results Descriptive data from classroom observations and pre- and post-tests suggest that instructional designs treating AI output as an object of critique rather than a source of knowledge can effectively activate students’ critical reading awareness; Approximately 14.7% of students showed a tendency of tool dependence, suggesting that shallow application of AI may carry the risk of weakening independent thinking. The above findings are exploratory observations and still need to be verified through statistical tests and controlled designs.
Conclusion   Based on the above findings, this paper proposes an evaluation optimization strategy that shifts from “testing knowledge” to “testing judgment”.