[Purpose/Significance] This study focuses on the application effect of large language models in the field of fact-checking, aiming to systematically explore their impact on users' ability to distinguish the authenticity of information and examine the moderating effect of demographic variables, providing empirical support for social platforms to combat false information. [Method/Process] This study used health-related information as the specific context and experimental material, adopting an inter-group control experiment design based on the dual-process theory. A total of 142 participants were recruited, and an experimental group using LLM for fact-checking and a control group relying on their own knowledge for judgment were established. Through one-way ANOVA and moderation effect analysis, the impact of LLM on users' ability to distinguish the authenticity of information and the moderating effects of gender, age, and education level were explored. [Result/Conclusion] LLM-assisted fact-checking significantly improves users' ability to distinguish the authenticity of information, with a significant increase in the accuracy of identifying false information, but the improvement in the accuracy of identifying true information does not reach a significant level. Further analysis indicates that users with lower educational attainment show a more pronounced improvement in their ability to distinguish the authenticity of information when assisted by LLMs in fact-checking, while gender and age variables do not exhibit significant moderating effects.