Project description
This project introduces an innovative vertical large language model (LLM) application designed to reconstruct practical training workflows in vocational education. Developed within the field of smart transportation, the initiative directly addresses critical instructional challenges, including delayed feedback in traditional one-to-many teaching models, shifting industry knowledge standards, and visual or verbal communication limitations during hands-on hardware operations.
At its core, the project establishes a specialized “Smart Transportation Curriculum Agent Platform” powered by localized foundation models and high-quality professional knowledge vector databases. By utilizing retrieval-augmented generation (RAG) technology across structured course content, the system deploys intelligent learning assistant agents to guide students with precise, automated troubleshooting and professional guidance. In addition, the platform integrates advanced vision and voice multimodal AI capabilities at physical training stations, enabling edge devices to recognize real-time student equipment status and provide immediate, hands-free technical support.
Serving vocational students, educators, and enterprise partners, this project transforms industrial talent cultivation by bridging the gap between classroom knowledge and rapidly evolving engineering practices. Ultimately, this comprehensive educational framework serves as an exemplary model for regional industrial-educational integration, significantly boosting technological support and providing an adaptable, scalable template for generative AI integration across global vocational training programmes.