Random Forest-Based Innovation and Practice of Generative AI-Empowered Teaching in Digital Economics: A Case Study of Economics and Management Majors in Application-Oriented Universities

Abstract

Generative Artificial Intelligence (AI) is reshaping higher education and has significant implications for the “New Liberal Arts” initiative, particularly in digital economics teaching within application-oriented universities. To investigate teachers’ and students’ perceptions of AI-assisted teaching and the factors driving their acceptance, a structured questionnaire survey was conducted across 12 universities in eastern China, yielding 245 valid responses. Random Forest (RF) was employed as the primary machine learning algorithm to classify support for AI-empowered teaching. The dataset was preprocessed through normalization and train–test splitting, and model performance was evaluated using Accuracy, Precision, Recall, F1-score, and the Area Under the Curve (AUC). The results indicate that participants generally hold positive perceptions of AI-assisted teaching, with an average questionnaire score of 3.5 or higher on a 5-point scale. The Random Forest model achieved excellent classification performance, with an Accuracy of 0.99, a Precision of 0.99, a Recall of 1.00, an F1-score of 0.99, and an AUC of 0.98. To enhance statistical rigor, paired t-tests and 95% confidence intervals were additionally calculated. Although Random Forest outperformed Logistic Regression and SVM, the differences were not statistically significant (p > 0.05), while the confidence intervals confirmed the stability and robustness of the Random Forest results. Feature importance analysis further revealed that learning efficiency, interdisciplinary competence, and personalized support are the most decisive factors influencing acceptance of AI-empowered teaching. These findings provide quantitative evidence for integrating generative AI into digital economics education under the New Liberal Arts framework and demonstrate the practical value of Random Forest in evaluating and optimizing innovative teaching models in application-oriented universities.

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