Research on the Path Innovation and Risk Prevention Mechanism of Artificial Intelligence Empowering University Scientific Research Management

Abstract

With the rapid iteration of AI technologies, their capabilities in natural language processing, knowledge discovery, data analysis, process optimization, and intelligent decision-making continue to strengthen, expanding from industrial scenarios to higher education governance. Existing research suggests that AI and big data are driving scientific research management from an experience-led model toward a data-driven, intelligently collaborative, and dynamically governed one, showing significant potential in project management, outcome evaluation, resource allocation, risk identification, and service platform construction. However, introducing AI into university research management also presents challenges such as data security, algorithmic bias, ethical concerns, inadequate organizational adaptation, and system integration difficulties. Accordingly, this paper follows the thread of “mechanism construction—path innovation—risk prevention”. Based on a review of relevant literature, it analyzes the operational logic of AI-empowered research management across four dimensions: intelligent platform construction, research process optimization, outcome evaluation reconstruction, and behavioral supervision enhancement. It then proposes a tripartite risk prevention framework of “technological prevention—institutional guarantees—organizational synergy.” The study argues that the key to AI empowerment lies not in partial tool substitution, but in the synergistic upgrading of governance concepts, institutional structures, and organizational capabilities. Effective implementation depends on the mutual support of mechanisms including data governance, algorithm review, ethical regulation, platform integration, and talent cultivation.