Bridging theory and intelligent practice: An AI-enhanced virtual-real simulation software and its synergistic pedagogy

The rapid infusion of Artificial Intelligence (AI) into industrial robotics has fundamentally altered the competency requirements for mechanical and automation engineers. Foundational robotics courses, however, often remain anchored in classical, model-based pedagogies, creating a critical skills gap. This paper presents a holistic, integrated approach to modernizing robotics education by synergistically co-developing an AI-enhanced virtual-real simulation software and a corresponding AI-infused pedagogical framework. First, we detail the development of a novel simulation platform that embeds accessible AI modules—including genetic algorithms for trajectory optimization, neural networks for inverse kinematics, and a fine-tuned large language model (LLM) for code assistance—into a seamless MATLAB/Simulink to hardware-in-the-loop (HIL) workflow. Second, we describe how this technical tool is the cornerstone of a broader pedagogical model characterized by an AI-augmented, industry-immersed curriculum and an AI-informed, multi-stakeholder assessment system. This dual-pronged approach ensures that students not only use AI tools but also understand their underlying principles and can critically evaluate their application in authentic industrial contexts. Deployed across multiple cohorts (total n > 800 students), the integrated model has yielded significant improvements in AI literacy, with a 28% increase in students’ ability to apply AI optimization to industrial tasks, a 15.7% improvement in project performance, and validation from industry partners regarding graduates’ enhanced readiness for intelligent manufacturing roles. This work demonstrates that the co-evolution of accessible AI-enabled tools and supportive, industry-engaged pedagogy is essential for cultivating the next generation of AI-ready engineers.