Regional normal universities play unique roles in local socioeconomic development. Unlike highly-mobile elite graduates, a large share of their alumni settle in hometown regions, take positions within local organizations, and strength the social-economic vitality of small and medium sized cities and towns. Such local-rooted talent matters greatly for the finance sector. Local banks, local foreign-trade enterprises, small cross-border payment providers and risk management teams demand professionals familiar with regional industries, indigenous credit cultures and “on-the-ground business” realities. But our International Finance Classes, a core course for finance majors, still frequently relies on traditional assessment arrangements. These reward rote memorization and formula application rather than analytical judgement of students, practical competencies and value-driven commitment. Drawing on our real practice at Huanggang Normal University, this paper presents a design-oriented reform of AI-enabled multi-dimensional assessment for International Finance. The reform integrates simulated foreign-exchange trading logs, exchange-rate forecasting reports, case debates, student reflective journals, a revised final exam and regular course participation. With general-purpose large-language models, teachers obtain prompt-driven, no-code and low-cost formative feedback. The system also generates observable evidence for five dimensions of curriculum-based ideological and political education，national sentiment, professional integrity, risk accountability, institutional confidence and global awareness. 

Wen Chen, Xue Yang. AI-Assisted Teaching in International Finance: A Design-Oriented Study of Multi-Assessment and Curriculum-Based Ideological and Political Education. International Journal of Business Management and Economics and Trade (2026), Vol. 7, Issue 3: 56-70. https://doi.org/10.38007/IJBMET.2026.070306.

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