Research on the integration platform of international trade virtual simulation and teaching practice based on AI

In order to break the bottleneck of traditional international trade teaching being out of touch with industrial demand, high training cost and single evaluation dimension, this paper proposes and constructs an AI-based international trade virtual simulation and teaching practice integration platform. The platform takes the ternary interactive model of "human teacher-AI assistant-virtual market" as the core, promotes AI from "tool" to "cognitive subject", and realizes the triple logical coupling of technology, education and industry. Research the integration of multi-source heterogeneous data such as WTO statistics, customs clearance, news and public opinion, and construct a knowledge graph that can be updated in real time; Design a small sample policy mutation market dynamic prediction model based on Meta RL (reinforcement learning), with an average adaptation time <3 hours and a MAE (mean absolute error) ≤150000 yuan; Develop a three-stage teaching process of "stress test-resumption deduction-personality guidance", and use deep reinforcement learning to compare and evaluate students' decision-making logic and dynamically generate personalized learning paths. The control experiment (n=120) shows that the knowledge test of the experimental group is 2.1 times higher than that of the control group, the number of high-profit people is increased by 155%, the bankruptcy rate is reduced from 18.3% to 3.3%, and the complex skills such as risk identification and emergency treatment are significantly enhanced. The platform is highly recognized by more than 85% students in terms of simulation authenticity, effectiveness of AI guidance and learning effect. The research provides an extensible and transferable AI+ education solution for the cultivation of compound talents in digital trade, and also provides a new paradigm for the deepening application of constructivism learning theory in complex skills training.