Distributed Multi-Agent Generative AI for Information Verification: A Controlled Comparison of Single-Agent and Multi-Agent Serious Games

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 configurations used the same cases, total information, verification resources, GPT-5.1 model, generation settings, scoring rules, and final task; the principal difference was the information-access architecture and its associated interaction demands. All conditions produced substantial digital-literacy knowledge gains, with no significant differences in transfer accuracy. The multi-agent condition outperformed classroom instruction in source evaluation, evidence integration, and reasoning quality, and significantly exceeded the single-agent condition only in evidence integration. It also showed more frequent investigative actions. These findings suggest that controlled information distribution across generative-AI agents can influence information-seeking and evidence integration even when the underlying model and available information are held constant, supporting bounded agent states and controlled information asymmetry as interaction-design mechanisms for information verification.