ImmuniFraug: A Metacognitive Intervention Anti-Fraud Approach to Enhance Undergraduate Students' Cyber Fraud Awareness

2026-01-11

arXiv.org

AI Summary

This study addresses the rising victimization rate of college students in online fraud by proposing a novel metacognitive intervention system powered by large language models. For the first time, it integrates textual, vocal, and visual avatars to create a multimodal, immersive fraud simulation that authentically replicates real-world scam scripts and psychological pressure. Grounded in Protection Motivation Theory, the system delivers reflective feedback following user interaction. In a controlled experiment with 846 Chinese undergraduates, the approach significantly enhanced anti-fraud awareness (p = 0.026), demonstrating incremental efficacy even after controlling for prior educational exposure. Narrative immersion scored 56.95 out of 77, and qualitative interviews identified perceived realism, adaptive deception tactics, and time pressure as key drivers of effectiveness, establishing a new paradigm for proactive, cognitively engaging, and ecologically valid anti-fraud education.

Technology Category

• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Multiagent Systems: Adversarial Agents
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Responsible Web: Mitigating online harms, including harassment, spam, and fake reviews
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

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.

Problem

• cyber fraud
• undergraduate students
• anti-fraud awareness
• metacognitive intervention
• fraud victimization

Innovation

• Large Language Model
• metacognitive intervention
• immersive simulation
• cyber fraud awareness
• multimodal interaction