虚拟仿真融合人工智能技术赋能新工科教学模式创新探索

Exploring the Innovation of New Engineering Teaching Mode Empowered by Virtual Simulation and Artificial Intelligence Technology

摘要: 随着新工科建设的深入推进，如何培养具备跨学科知识整合能力、创新实践能力和复杂工程问题解决能力的高素质人才成为关键议题。本文旨在探索虚拟仿真(VS)与人工智能(AI)技术深度融合，赋能新工科教学模式创新的路径与方法。文章构建了“VS-AI深度融合”的新工科教学模式框架，该框架以项目制学习为核心，融合情境认知、最近发展区等教育学理论，强调以学生为中心，通过“环境虚实融合、过程数据驱动、评价智能精准、学习个性化自适应”的核心特征，重塑教学流程。结合《虚拟仿真与人工智能》项目制课程、ROS机器人仿真、AI视觉大模型应用等具体案例，详细阐述了该模式在机电系统设计、智能制造产线仿真、机器人智能控制等典型新工科场景下的教学设计、实施流程与潜在成效。进一步分析了当前面临的技术集成、师资能力、教学评价及伦理安全等方面的挑战，并提出了相应的对策建议。初步实践表明，虚拟仿真与人工智能的有机融合，有望缓解传统工科教学中的高成本、高风险、难再现、难评价等瓶颈问题，为新工科人才培养提供了一种可操作的系统性整合框架与实施蓝图，对推动工程教育质量革命具有一定的理论与实践意义。

Abstract: With the deepening of the construction of new engineering disciplines, how to cultivate high-quality talents with interdisciplinary knowledge integration ability, innovative practical ability, and complex engineering problem-solving ability has become a key issue. This article aims to explore the path and methods of deep integration of virtual simulation (VS) and artificial intelligence (AI) technologies to empower innovation in new engineering teaching models. The article constructs a new engineering teaching model framework of “VS-AI deep integration”, which is centered on project-based learning and integrates educational theories such as situated cognition and the Zone of Proximal Development, emphasizing a student-centered approach and reshaping the teaching process through the core features of “environment virtual real integration, process data-driven, intelligent and accurate evaluation, and personalized adaptive learning”. Based on specific cases such as the project-based course “Virtual Simulation and Artificial Intelligence”, ROS robot simulation, and the application of AI vision big models, this paper elaborates in detail on the teaching design, implementation process, and potential effectiveness of this mode in typical new engineering scenarios such as electromechanical system design, intelligent manufacturing production line simulation, and robot intelligent control. Further analysis was conducted on the challenges currently faced in terms of technology integration, faculty capacity, teaching evaluation, and ethical safety, and corresponding countermeasures and suggestions were proposed. Preliminary practice shows that the organic integration of virtual simulation and artificial intelligence offers potential to address the bottleneck problems of high cost, high risk, difficulty in reproduction, and difficulty in evaluation in traditional engineering teaching, providing a replicable and scalable systematic integration framework and implementation blueprint for the cultivation of new engineering talents. It holds certain theoretical and practical significance for promoting the quality revolution of engineering education.