Generative Artificial Intelligence (OpenAI) Empowering Intelligent Finance Classroom Development: A Case Study of Xi'an University of Finance and Economics
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".