Cyber fraud now constitutes over half of criminal cases in China, with undergraduate students experiencing a disproportionate rise in victimization. Traditional anti-fraud training remains predominantly passive, yielding limited engagement and retention. This paper introduces ImmuniFraug, a Large Language Model (LLM)-based metacognitive intervention that delivers immersive, multimodal fraud simulations integrating text, voice, and visual avatars across ten prevalent fraud types. Each scenario is designed to replicate real-world persuasion tactics and psychological pressure, while post-interaction debriefs provide grounded feedback in protection motivation theory and reflective prompts to reinforce learning. In a controlled study with 846 Chinese undergraduates, ImmuniFraug was compared to official text-based materials. Linear Mixed-Effects Modeling (LMEM) reveals that the interactive intervention significantly improved fraud awareness (p = 0.026), successfully providing incremental learning value even when controlling for participants' extensive prior exposure to anti-fraud education, alongside high narrative immersion (M = 56.95/77). Thematic analysis of interviews revealed key effectiveness factors: perceived realism, adaptive deception, enforced time pressure, emotional manipulation awareness, and enhanced self-efficacy. Findings demonstrate that by shifting the focus from passive knowledge acquisition to active metacognitive engagement, LLM-based simulations offer a scalable and ecologically valid new paradigm for anti-fraud training and fostering fraud resilience.

翻译：网络诈骗目前占中国刑事案件半数以上，其中本科生受害比例呈异常增长趋势。传统反诈培训仍以被动式教育为主，参与度与知识留存率有限。本文提出ImmuniFraug——一种基于大语言模型（LLM）的元认知干预系统，通过整合文本、语音和视觉化身的沉浸式多模态诈骗模拟，覆盖十种高发诈骗类型。每个场景均模拟真实世界的说服策略与心理压迫，交互后基于保护动机理论的复盘环节提供实证反馈与反思提示以强化学习效果。通过对846名中国本科生开展的对照研究，将ImmuniFraug与官方文本材料进行对比。线性混合效应模型（LMEM）分析表明：在控制参与者既往广泛接受反诈教育的前提下，该交互式干预显著提升了诈骗防范意识（p = 0.026），成功实现了增量学习价值，同时具备高叙事沉浸感（M = 56.95/77）。访谈的主题分析揭示了关键效能因素：感知真实性、适应性欺骗、强制时间压力、情感操纵觉察及自我效能感提升。研究证明，通过将培训重心从被动知识获取转向主动元认知参与，基于LLM的模拟训练为反诈教育及欺诈抵御能力培养提供了可扩展且生态效度高的新范式。