AI Volunteer Assistant for College Entrance Exam Using the agentUniverse Multi‑Agent Framework

Recently, community contributor Yuan Junfeng built an “AI Volunteer Assistant” for Chinese college‑entrance exam (Gaokao) volunteer selection on top of Ant Group’s multi‑agent framework, agentUniverse, claiming higher professionalism and guidance value compared with other market products.

Filling Gaokao volunteers is a complex process involving scores, rankings, interests, career plans, and historical admission data; traditional methods rely on cumbersome manuals, personal experience, or costly consulting, which often fail to achieve comprehensive and accurate matching.

The AI Volunteer Assistant demonstrates superior performance in same‑question Q&A comparisons, offering precise recommendations for target schools and specialties, and integrating the latest employment market trends and professional characteristics to optimize both educational pathways and future career prospects.

The system workflow begins with users entering candidate information (province, subject stream, score rank, interests, strengths). A Filtering Agent validates relevance, then a Professional Selection Agent narrows broad fields, followed by a School Selection Agent that matches specific schools and majors based on score range. A Planning Agent raises questions about employment outlook and resources, an Executing Agent uses the KFind search tool to answer them, and finally an Expressing Agent synthesizes the recommendations into a complete volunteer plan.

The AI algorithms rapidly process massive historical data—such as past admission scores, enrollment plans, and major popularity trends—to predict admission probabilities and provide data‑driven suggestions.

Most existing AI applications lack a generic SOP for volunteer selection, missing coordinated multi‑agent mechanisms and thus cannot deliver personalized, actionable advice that balances student preferences with future development plans.

By embedding a domain‑specific SOP, leveraging multi‑agent collaboration, and employing the PEER paradigm, the agentUniverse‑based AI Volunteer Assistant achieves multi‑step problem decomposition, iterative self‑improvement, and a more scientific, convenient, and individualized Gaokao volunteer planning experience. Developers are invited to try the assistant, explore more intelligent agents, and contribute to the open‑source agentUniverse project.