“数量缺”与“对不上” 产业数字化人才培养亟需破局

Group 1 - The core issue in talent development is the structural mismatch between the skills of graduates and the requirements of industries in the "cloud + AI" era [2][3] - Traditional talent evaluation and training systems are lagging behind the rapid iteration of AI technologies, leading to a disconnect between educational institutions and industry needs [2][3] - There is a pressing need for educational institutions to integrate AI into all engineering disciplines rather than confining it to computer science, to better prepare students for industry demands [3] Group 2 - Experts emphasize the importance of creating a collaborative ecosystem between universities and industries to bridge the gap between technical knowledge and business understanding [3] - The establishment of a "translator" mechanism is crucial, where industry professionals can effectively communicate AI applications within their specific fields [3] - A feedback loop in the certification system is necessary to ensure that training standards align with real-world industry needs, enhancing the adaptability of talent to the market [3]