分布式多代理生成AI在信息验证中的应用

Generative-AI learning systems typically centralize conversational information in one agent or use multiple agents primarily as tutors or collaborators. This study presents a distributed multi-agent generative-AI architecture in which role-specific AI non-player characters (AI-NPCs) act as bounded information sources with private evidence states. The architecture was implemented in an information-verification serious game and evaluated with 90 first-year undergraduates randomly assigned to classroom instruction, a single-agent AI game, or a distributed multi-agent AI game. The two AI configura