Fraud continues to proliferate online, from phishing and ransomware to impersonation scams. Yet automated prevention approaches adapt slowly and may not reliably protect users from falling prey to new scams. To better combat online scams, we developed ScamPilot, a conversational interface that inoculates users against scams through simulation, dynamic interaction, and real-time feedback. ScamPilot simulates scams with two large language model-powered agents: a scammer and a target. Users must help the target defend against the scammer by providing real-time advice. Through a between-subjects study (N=150) with one control and three experimental conditions, we find that blending advice-giving with multiple choice questions significantly increased scam recognition (+8%) without decreasing wariness towards legitimate conversations. Users' response efficacy and change in self-efficacy was also 9% and 19% higher, respectively. Qualitatively, we find that users more frequently provided action-oriented advice over urging caution or providing emotional support. Overall, ScamPilot demonstrates the potential for inter-agent conversational user interfaces to augment learning.

翻译：网络诈骗持续蔓延，从钓鱼攻击、勒索软件到冒充身份诈骗层出不穷。然而自动化防范手段适应缓慢，可能无法可靠保护用户免遭新型诈骗侵害。为更有效应对网络诈骗，我们开发了ScamPilot——一种通过模拟对话、动态交互与实时反馈增强用户防骗能力的对话式界面。该系统采用两个基于大型语言模型的智能体（诈骗者与目标对象）模拟诈骗场景，用户需通过实时提供建议帮助目标对象抵御诈骗者。通过设置对照组与三个实验组的组间研究（N=150），我们发现结合建议提供与多项选择题的交互模式能显著提升诈骗识别率（+8%），且不会降低对正常对话的警惕性。用户响应效能与自我效能感变化分别提升9%和19%。定性分析表明，相较于敦促谨慎或提供情感支持，用户更倾向于提供行动导向的建议。总体而言，ScamPilot展现了多智能体对话式用户界面在增强学习效果方面的潜力。