Artificial Intelligence Technology Enabling the Construction of Employment Nurturing System in Colleges and Universities

Employment forms the cornerstone of societal welfare. Within the rapidly evolving landscape of artificial intelligence technology, the challenge for higher education institutions is to leverage these advancements to forge a robust employment and educational framework. This study integrates personalized recommendation algorithms to address the prevalent issue of accuracy deficits in the employment education systems of colleges and universities. Utilizing both content-based and collaborative filtering algorithms predicated on latent factor decomposition, this approach enables the precise tailoring of job recommendations that align closely with individual student preferences. This targeted recommendation process not only enhances the quality of student employment outcomes but also addresses the enduring challenge of sustaining effectiveness in the employment education systems within academic institutions. Furthermore, this paper examines the contributory factors to the efficacy of these systems through the structural equation modeling (SEM) of student satisfaction with university employment education systems. The analysis reveals that in the perceived quality of these systems, as assessed by the SEM, the factor loading for the precision of employment recommendation approaches reaches a value of one. This underscores the critical importance of aligning job recommendations with the student's circumstances and preferences, highlighting it as a central concern among students. © 2024 Fengjin Zhou., published by Sciendo.