Generative Artificial Intelligence (OpenAI) Empowering Intelligent Finance Classroom Development: A Case Study of Xi'an University of Finance and Economics

This study focuses on the empowerment of intelligent finance classroom construction through generative artificial intelligence (exemplified by OpenAI), with Xi'an University of Finance and Economics as the empirical research subject. Utilizing a quasi-experimental design and multidimensional data analysis, the research explores the application potential and efficacy of this technology in financial education. Instructors from eight finance classes were divided into two groups: those using OpenAI and those not using it. Data were collected through semi-structured interviews, classroom observations, and performance metrics to comprehensively evaluate practical outcomes across dimensions such as lesson preparation time, instructional efficiency, and student learning achievements. Results demonstrate that instructors using OpenAI exhibited significantly lower mean preparation time (5.5 hours) compared to non-users (10.57 hours). Students in AI-assisted classrooms showed superior performance in academic scores (81.5 vs. 72.3), case analysis accuracy (78.9% vs. 65.2%), and resource access frequency (45.3 vs. 12.4 counts). The study analyzes the internal mechanisms through which OpenAI enhances intelligent classroom development in finance education, confirming its effectiveness in optimizing teaching workflows, reducing faculty workload, and improving educational quality. These findings provide robust support for educational digital transformation and offer critical insights into the future development of intelligent pedagogy.