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| Main Authors: | , , , , |
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| Format: | Preprint |
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2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.06774 |
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| _version_ | 1866914245672697856 |
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| author | Yuan, Xiangzhe Wang, Jiajun Wang, Huanchen Wan, Qian Hu, Siying |
| author_facet | Yuan, Xiangzhe Wang, Jiajun Wang, Huanchen Wan, Qian Hu, Siying |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_06774 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ImmuniFraug: A Metacognitive Intervention Anti-Fraud Approach to Enhance Undergraduate Students' Cyber Fraud Awareness Yuan, Xiangzhe Wang, Jiajun Wang, Huanchen Wan, Qian Hu, Siying Human-Computer Interaction 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. |
| title | ImmuniFraug: A Metacognitive Intervention Anti-Fraud Approach to Enhance Undergraduate Students' Cyber Fraud Awareness |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2601.06774 |