Evaluating an AI-Enabled Business Curriculum: Random Forest Regressor and a SHAP-Based Analysis of Student Feedback in Accounting and Finance Education
Unlocking AI Potential in Business Education
This study proposes a three-tiered '1+2+3' AI-enabled curriculum model for business studies, addressing the fragmentation of AI applications in accounting and finance. Utilizing a Random Forest Regressor and SHAP analysis on student feedback, the research empirically validates the model's effectiveness in enhancing AI competence and professional skills. Key findings highlight the significant influence of professional skills, practical course engagement, and AI literacy on student satisfaction, offering a replicable paradigm for AI+Business curriculum reform.
Executive Impact Summary
Key performance indicators from our AI-enabled curriculum evaluation highlight significant improvements and model robustness.
0.8825
R² Score (RFR Model)
0.1446
Mean Absolute Error
5
Most Influential Factors
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Details on the three-tiered '1+2+3' model, integrating AI literacy, tool proficiency, and professional scenario applications.
Explanation of data collection through questionnaires and the application of Random Forest Regressor (RFR) and SHapley Additive exPlanations (SHAP).
Empirical results demonstrating the effectiveness of the curriculum in enhancing AI competence and identifying the most influential factors on student satisfaction.
Discussion of theoretical contributions, practical applications, and future directions for AI-empowered business education.
Enterprise Process Flow
Data collection
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Data preprocessing
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Train-the-model
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Evaluate the model
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Explainable AI
0.8825
R² Score for Random Forest Regressor, indicating strong model fit.
| Model | R² Score | MAE |
|---|---|---|
| Random Forest | 0.8825 | 0.1446 |
| K-Nearest Neighbors | 0.8711 | 0.1684 |
| Support Vector Regressor (SVR) | 0.8202 | 0.2279 |
| Linear Regression | 0.7813 | 0.2559 |
| Decision Tree | 0.7518 | 0.2111 |
Notes: Random Forest Regressor significantly outperforms other models in predicting student satisfaction. |
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Impact of SHAP Analysis on Curriculum Refinement
The SHAP analysis revealed that Professional Skills, Practical Courses, and AI Literacy are the most influential factors for student satisfaction. These insights directly inform iterative curriculum improvements, focusing on experiential learning and foundational AI concepts to maximize student engagement and competency development.
Top 5
Most Influential Features identified by SHAP: Professional Skills, Practical Courses, AI Literacy, Course Integration with Business Scenarios, and Computation and Intelligent Thinking.
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Your AI Implementation Roadmap
A structured approach to integrating AI into your business curriculum, ensuring sustainable growth and skill development.
Phase 1: Foundational AI Literacy
Cultivate computational thinking and AI problem-solving through 'Computing and Intelligent Thinking'.
Phase 2: AI Tool Proficiency
Develop practical skills in Python for financial data analysis and RPA for financial process automation.
Phase 3: AI Integration in Professional Scenarios
Apply AI in accounting, auditing, and financial management through simulated real-world projects and industry internships.
Phase 4: International & Ethical Alignment
Integrate global professional standards and ethical frameworks, comparing regional AI regulations.
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