Artificial Intelligence-Driven Construction of Virtual Experimental Environments for Information Technology Teaching

Abstract

In information technology experimental teaching, traditional virtual simulation environments face limitations such as contextual rigidity, monotonous interaction, and cognitive load mismatch. To break through these bottlenecks, we propose a framework for constructing an artificial intelligence-driven virtual experimental environment, which is developed from three aspects: intelligent educational forms, multi-agent computational models, and self-evolving semantic associations. First, we establish virtualized representations and multimodal interaction modalities, and clarify the generation mechanism of dynamic experimental scenarios and the spatial logic for adaptive regulation of cognitive load; second, we construct a computational model based on multi-agent collaboration to achieve behavioral modeling of experimental objects, state graph evolution driven by heterogeneous data, and a reinforcement learning framework for intention recognition and feedback generation; finally, we design the self-evolution mechanism of the virtual experimental environment, including dynamic resource organization guided by domain knowledge graphs, adaptation of virtual instrument operation logic via generative adversarial networks, and semantic annotation and path optimization of multimodal interaction trajectories. This framework provides a systematic theoretical model and technical pathway for virtual experimental environments in information technology teaching.