Exploration on the efficient management mode and innovation path of large-scale instruments and equipment in local universities empowered by artificial intelligence
Wei Song, Chaoqing Cao, Kailong Zhang, Caisheng Wang · Frontiers in Education · 2026
To address low management efficiency of large-scale instruments in local undergraduate universities and respond to the strategic demand for education digital transformation, this study uses the College of Biology and the Environment at Zhejiang Wanli University as a representative case. Based on three years of practical exploration, we construct a research framework of “problem diagnosis–path construction–effectiveness evaluation–theoretical refinement.” We analyze four core dilemmas in equipment management: resource allocation, operating modes, sharing mechanisms, and operation-and-maintenance support. In response, we establish an AI-enabled intelligent management platform with a four-layer collaborative architecture of “perception-network-platform-application,” and implement supporting measures including intelligent scheduling, predictive maintenance, upgraded safety control, and an institutional and talent assurance system. Practical results indicate that the proposed mode significantly improves utilization efficiency, with the proportion of effective operation time increasing from 18.6% to 35.2%. It also reduces operation and maintenance (O&M) costs, with maintenance expenses decreasing by 40%, and extends equipment service life by 15%–20%, while promoting substantial growth in research output and social service capacity. Overall, the study suggests that AI-enabled digital transformation is an effective pathway to modernize equipment management in local universities, and that coordinated innovation in technology, governance, and talent is essential. However, as a single-case study, broader scalability and long-term effects require further validation.