Abstract: Health education,as an essential component of patient-centered nursing services,spans the entire care continuum including patient visits, hospitalization, and post-discharge follow-up. However, constrained by limited nursing staff and increasing workloads, traditional approaches often face clear limitations such as insufficient personalization,low implementation efficiency,and limited patient engagement. In recent years,artificial intelligence—particularly multi-agent systems based on large language models—has demonstrated unique advantages in natural language interaction,autonomous perception,and reasoning-based decision-making,offering new opportunities for the intelligent transformation of nursing health education. This paper focuses on the applications and practical challenges of multi-agent systems in nursing health education. From the perspectives of precision, adaptability, workload reduction, interactivity, and scalability,it outlines innovative pathways through which multi-agent systems can empower health education. Furthermore, based on different clinical application scenarios and practice functions, the paper identifies potential configurations of multi-agent systems and their collaborative modes that may support health education. The aim is to provide insights for advancing the practical implementation of multi-agent systems in nursing practice.