Development and Teaching Practice of Graduate Course Resources for “AI + Finance”: A Case Study of Generative Artificial Intelligence and Financial Applications
DOI:
https://doi.org/10.54097/vdqpax52Keywords:
AI + Finance, Generative Artificial Intelligence, Graduate EducationAbstract
This paper discusses the development of course resources and teaching practice for the graduate course Generative Artificial Intelligence and Financial Applications. Built on earlier work in financial data analysis and Python-based teaching, the course responds to the growing need for graduate students to use generative artificial intelligence in annual report reading, policy analysis, financial statement interpretation, financial news assessment, and research report writing. It develops a resource system composed of conceptual lectures, case materials, experimental task packages, reading assignments, process portfolios, verification logs, and assessment rubrics. The course organizes model use around authentic financial materials and disciplinary problems, with emphasis on source verification, evidence-based interpretation, and normative reflection. It guides students to form financial analysis paths that can be traced, explained, and questioned under model-assisted conditions. The paper focuses on the course resource framework, teaching process design, key risk responses, and future evaluation plans. Quantitative assessment of teaching effects will be conducted in later teaching cycles.
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