Author(s)

Qingjiao Rong, Huili Zhao

Corresponding Author

Qingjiao Rong

Abstract

This study proposes an AI-enhanced blended learning model for Internal Control education to address three pedagogical challenges: (1) overreliance on abstract frameworks that hinder practical application; (2) a disconnect between theory and practice; (3) homogenized instruction that neglects individual disparities. The model integrates intelligent diagnostics, dynamic content generation, and virtual simulations across pre-class, in-class, and post-class phases. Key innovations include a "diagnosis-update-simulation" framework converting theories (e.g., risk matrices) into immersive scenarios (e.g., supply chain disruptions), and a three-tier analytics system for personalized interventions. AI synchronizes curricula with real-time regulations (e.g., Data Security Law) and industry risks, while virtual labs enable decision-making practice with real-time feedback. Theoretically, the model extends blended learning into practice-intensive fields through AI adaptability. Practically, it offers a blueprint for curriculum modernization. Future work will validate scalability and expand simulations to organizational-level dynamics.

Keywords

Artificial Intelligence, Blended Teaching, Internal Control Education, Virtual Simulation, Personalized Learning