国内首个可重构算力驱动AI实训模型库发布！清微智能携手灵书AI共筑智慧职教新范式

Core Insights - The "MetaSight Experimental Training Model Library" was launched at the "2025 AI Empowerment New Dual High Construction Innovation Application Expert Seminar" in Beijing, marking a significant step in the integration of AI and vocational education [1] - The model library aims to address long-standing issues in vocational education, such as lack of evaluation, management difficulties, and delayed feedback, indicating a shift towards standardized and scalable applications of AI in education [1][3] Group 1: Challenges in Vocational Education - The quality of practical teaching is crucial for assessing vocational education levels, with approximately 1,400 universities offering programs requiring extensive experimental training, serving around 3 million students, leading to a market for intelligent teaching tools worth 1 to 1.5 billion [3] - There exists a significant gap between the high demand for skilled talent and the current application of educational technologies, with traditional training processes often described as "black boxes" where teachers rely on experience for evaluations [3][4] Group 2: Solutions and Innovations - The "MetaSight Experimental Training Model Library" and its underlying technology provide a systematic, domestically developed solution to convert policy blueprints into practical classroom applications [5] - The core engine of this solution is a reconfigurable computing chip (RPU) from Qingwei Intelligent, which allows for dynamic reconfiguration of hardware resources based on different teaching tasks, enhancing flexibility and efficiency [5][7] - The platform integrates deeply with Lingbook AI's model library, forming a complete "cloud-edge-end" collaborative system, already implemented in over ten "dual high" vocational colleges across China [7] Group 3: Transformative Impact on Teaching - The new model aims to significantly enhance the scientific and precise nature of practical training through real-time analysis of operational processes, thereby improving educational outcomes [8] - In practical applications, students receive immediate feedback on their performance, transforming the traditional feedback loop and enhancing the overall learning experience [10] - The open platform based on reconfigurable computing allows for the replication of high-quality training projects into digital models, facilitating the sharing and evolution of teaching wisdom across institutions [10]