An Interactive Financial Management Teaching System Based on Generative AI: Framework Design and Effectiveness Validation

Abstract

Financial management education faces challenges such as the disconnect between theory and practice and insufficient personalized learning. This study integrates generative artificial intelligence technology to design and implement an interactive financial management teaching system using a three-layer architecture that incorporates adaptive learning path generation, real-time financial case simulation, and intelligent Q&A functions. A semester-long controlled experiment was conducted with 236 financial management students from two universities, where the experimental group used the system as a learning aid while the control group followed traditional teaching methods. Results showed that students in the experimental group achieved an average improvement of 18.7% in final assessment scores, a 23.4% enhancement in knowledge application ability, and a learning satisfaction rating of 4.62/5.00. The system's effectiveness was validated through structural equation modeling, revealing a significant positive correlation between technology acceptance and learning outcomes (β=0.73, p<0.01). Qualitative analysis found that the dynamic financial scenarios created by the system helped students transform abstract concepts into professional intuition, representing the core mechanism behind performance improvements. This research provides an innovative technical solution for financial management education and establishes a replicable paradigm for applying generative AI in specialized educational fields.