Research on Improving the Quality of Employment Guidance in Local Universities through Artificial Intelligence

Abstract

With the continuous increase in the number of university graduates and the intensification of structural contradictions in the job market, employment guidance in local universities faces significant challenges. Based on the theory of digital transformation in education and the application framework of artificial intelligence (AI), this paper explores pathways for AI to enhance the quality of employment guidance in local universities, using employment data of university graduates in Sichuan Province and relevant policy orientations. Through case analysis, survey data, and theoretical research, the study finds that AI can optimize employment guidance processes and improve job-person matching efficiency through intelligent matching, personalized recommendations, and data-driven decision-making mechanisms, thereby facilitating the alignment between talent cultivation in universities and industry demands. The study proposes the construction of an “AI + Employment” ecosystem, the refinement of intelligent evaluation models, and the deepening of university-enterprise collaboration, providing theoretical foundations and practical references for the reform of local university employment guidance systems.