Abstract: Supported by Generative Artificial Intelligence (GenAI) technology, this paper explores the design rationale and implementation pathway of virtual teaching experiments on the basic deformation of materials. An instructional experimental framework is constructed around the workflow of “knowledge extraction—interface generation—parameter embedding—interaction design—report output.” based upon a virtual simulation platform for basic deformation. By integrating mechanical relationships with a material parameter database, the dynamic simulation and visual representation of basic deformation processes—including tension, compression, torsion, and bending—are realized. The study demonstrates that the deep integration of generative artificial intelligence with virtual teaching experiments helps enhance the intuitiveness, interactivity, and personalization of experimental instruction, providing a reference for the digital and intelligent reform of experimental teaching in engineering mechanics.