Machine Learning Tools for Teaching Cost Accounting ScenariosMachine Learning Tools for Teaching Cost Accounting Scenarios

Abstract

Cost accounting education traditionally emphasizes manual calculations, cost classifications, and variance analysis. However, modern cost structures are increasingly dynamic, data-driven, and influenced by real-time production analytics. This study explores the use of machine learning (ML) tools to teach cost accounting scenarios by enabling predictive cost modeling, variance forecasting, activity-based costing simulations, and anomaly detection in budgetary performance. Using survey data, curriculum analysis, and experimental teaching models, the study concludes that integrating ML tools such as Python, Scikit-Learn, TensorFlow, R, and Power BI helps students develop analytical reasoning, enhance computational accuracy, and model complex cost behaviors. The paper proposes an ML-integrated pedagogical framework for undergraduate and postgraduate accounting programs.