Journal of Higher Education Research

Driven by both the digital transformation of higher education and the growing demand for refined human resource management, traditional university human resource management models face problems such as data fragmentation, strong subjectivity in decision-making, and low process efficiency. Centered on generative AI technology and combined with core business areas of university human resource management — including position recruitment, performance evaluation, training and development, and staffing optimization — this paper designs an intelligent decision-making system for university human resources. By constructing a multi-source data integration framework and optimizing generative AI models based on the Transformer architecture, the study enables deep mining, intelligent analysis, and decision recommendation generation for human resource data. The system adopts a layered architecture encompassing the data layer, technology layer, application layer, and decision layer, and simultaneously develops core modules such as intelligent recruitment matching, dynamic performance evaluation, and training program generation, along with supporting data processing and decision execution workflows. This study aims to address the challenge of transforming data value in university human resource management and to enhance the scientific rigor and precision of decision-making.