Exploring AI-Driven Transformation in Management Paradigms for Recurrent Safety Hazards in University Laboratories

The persistence of recurrent safety noncompliance (RSN) in university laboratories presents a critical challenge to laboratory safety risk management. This paper deconstructs RSN by conducting an in-depth analysis of potential safety risks, their underlying causes, and management obstacles. The research reveals that the phenomenon of RSN is fundamentally the result of the combined effects of complex human factor risks and outdated management methods. At the human factor level, cognitive biases regarding experimental safety risks and negative resistance lead to “habitual violations” of safety regulations. At the management level, routine laboratory safety inspections, requirements for rectifying safety hazards, and commonly adopted punitive measures have proven insufficient to prevent RSN. To address this issue, this study proposes actively leveraging the advantages of artificial intelligence (AI) in dynamic perception and proactive interventions. It advocates for the deep integration of AI technologies into the transformation of the management paradigm for RSN in university laboratories. Furthermore, this study preliminarily explores the application prospects, applicable principles, and scope of application of AI technologies in this context, providing an important reference for enhancing the systematic management of RSN in university laboratories.