Research on the Transformation Logic and Implementation Paths of Intelligent Accounting Education

Abstract

Intelligence technology changes the basic logic of accounting work. Automation and machine learning models are gradually replacing accounting tasks with clear rules, shifting the work chain into data ingestion, model processing and result verification. Data governance, model application and anomaly interpretation capabilities have become very valuable strategically but the current accounting education system focuses on memorizing accounting standards and manual bookkeeping leading to an ability gap that does not match industry side's demands. Constructing a transformation framework from three aspects: knowledge reconstruction, paradigm shift, and ecosystem evolution. Elaborate the direction for element reorganization by teaching content, teaching scenarios and evaluation systems respectively. Three implementation paths proposed include modular integration of courses, Reconstruction of experimental teaching for algorithmic thinking, Upgrade faculty data literacy. Aimed at providing theoretical support for accounting education adapting to intelligent work scenarios.