AI for empowering the construction and development of university laboratories in the new era

As a “source of innovation” and “primary base” for cultivating outstanding talents, university laboratories are directly linked to the quality of higher education and the enhancement of national innovation capacity. With the rapid advancement of artificial intelligence (AI) technology, the intelligent upgrading of university laboratories has become a significant concern. Traditional laboratory models are hampered by multiple bottlenecks: inefficient allocation of experimental resources, with high idle rates of large-scale instruments and equipment and a lack of interdisciplinary sharing mechanisms; outdated laboratory management modes characterized by high manual operation and maintenance costs and weak early warning systems for safety hazards; limited experimental teaching functions that rely on fixed processes, making it difficult to cultivate innovative talents; and limited scientific research collaboration capabilities, including a lack of intelligent platforms that facilitate multi-team and cross-regional cooperation. However, the rapid development of AI technology has provided new possibilities for addressing these challenges. This study aims to systematically explore the core logic, practical paths, and value effects of using AI to update university laboratories and construct a theoretical framework for the deep integration of AI and university laboratory development. It also identifies potential application scenarios for AI technologies in laboratory resource management, experimental teaching reforms, and scientific research. The study proposes laboratory construction standards and development strategies that integrate AI, providing theoretical support and practical references for universities to transform their laboratories toward “intelligence, openness, and collaboration”.

To effectively address the challenges facing today’s university laboratories, including talent cultivation, research demands, and management efficiency bottlenecks, this study combines bibliometric analysis, theoretical construction, and case-based empirical research.

The results demonstrate that AI’s three core features of perceptual, cognitive, and decision-making intelligence can be effectively applied in key scenarios such as procuring experimental equipment, constructing smart experimental platforms, and managing laboratories. For example, AI technology could optimize hardware management in laboratories and enable the creation of virtual-reality integrated experimental environments through knowledge graphs and digital twins. Additionally, it could promote scientific research collaboration platforms that can transcend disciplinary boundaries, improve research efficiency, and gradually form a positive cycle of “technology optimizing management—management feeding back into teaching—teaching and research collaborating”. The results also clarify the major challenges in upgrading laboratories, including high technical integration barriers, substantial costs for system upgrades, and shortages of interdisciplinary professional talents. To this end, the study proposes building a collaborative promotion mechanism of “demand-driven—technology adaptation—institutional guarantee”, strengthening talent cultivation, and improving ethical norms to advance laboratories toward higher goals of being “intelligent, open, green, and sustainable”.

This study systematically addresses the critical question of “how AI empowers the construction and development of university laboratories”. The results indicate that AI can effectively support the transformation and upgrading of laboratory operation models from “human-driven” to “intelligence-driven”. This conclusion aligns with the strategic orientation of digital transformation in higher education and provides a Chinese solution for global intelligent laboratory construction. It also holds significant importance for promoting the transition of university laboratories from supporters to leaders.