Retrieval-Augmented Generation in Financial Derivatives Education: Prototype Development and Student Perspectives

In recent years, generative artificial intelligence (GenAI) technologies have supported learning in higher education by providing accessible explanations, worked examples, and formative feedback. While students benefit from the efficiency of these GenAI tools, their usefulness in financial derivatives education remains challenging. This study explores our students’ experiences with AI WAN, a retrieval-augmented generation (RAG) platform that uses approved module materials to provide conceptual explanations, worked calculations, examination-style practice, and feedback. The exploratory qualitative pilot included six undergraduate Malaysian finance students from a cohort of 29. Analysis of their open-ended responses showed that they generally viewed the platform positively. Participants appreciated getting explanations when needed and working through complex calculations step by step at their own pace. They also valued its alignment with course content and highlighted the importance of clear responses, accurate information, and ongoing lecturer guidance. These outcomes provide an initial account of how students perceive curriculum-grounded RAG as a learning aid in financial derivatives education. However, the small sample and reliance on students’ experiences mean that the findings reflect perceived usefulness rather than evidence of output accuracy, improved learning, or continued use. Further investigation is needed to assess these aspects and determine whether AI WAN helps students develop independent financial reasoning and reach informed derivatives decisions.

Financial derivatives education, Retrieval-augmented generation, Curriculum-grounded learning, Digital learning, Learning prototype development, Student perceptions

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