[目的/意义]大语言模型带来的虚假信息定制化生成与精准推送问题，加剧了事实核查过程中个体认知偏差的固着性与多样性，文章旨在探究大语言模型在人机动态交互中对多类型认知偏差的纠偏效果及其行为影响机制。[方法/过程]采用顺序式混合研究设计：首先基于事实核查人员与大语言模型的交互语料，运用扎根理论归纳事实核查中的常见认知偏差类型并构建实验变量；在此基础上基于前测—后测实验数据，利用非参数检验方法检验大语言模型的动态纠偏交互对不同类型认知偏差及虚假信息参与行为的影响。[结果/结论]事实核查中的认知偏差可归纳为认知加工缺陷、选择性采信与防御性信念三类核心范畴，且呈现多类型交织的动态特征。大语言模型可通过个性化、基于证据的内容交互，有效识别并缓解个体在事实核查过程中呈现出的各类认知偏差，并显著降低事实核查人员对虚假信息的点赞意愿，但对评论纠正等高认知成本行为的促进作用有限。

[Purpose/significance] The customized generation and targeted dissemination of disinformation driven by large language models have intensified the persistence and diversity of individual cognitive biases in fact-checking processes. To address this issue, this article aims to examine the debiasing effects of LLMs in dynamic human-LLMs interactions on multiple types of cognitive biases, as well as their behavioral implications. [Method/process] A sequential mixed-methods design was adopted. Firstly, grounded theory was applied to interaction data between fact-checkers and LLMs to identify common types of cognitive biases and construct experimental variables. Subsequently, based on pretest-posttest experimental data, nonparametric statistical tests were conducted to evaluate the effects of LLMs-driven dynamic debiasing interactions on different types of cognitive biases and disinformation engagement behaviors.[Result/conclusion] Cognitive biases in fact-checking can be categorized into three core dimensions: cognitive processing deficits, selective exposure, and defensive beliefs, which exhibit dynamic and interwoven characteristics.LLMs, through personalized and evidence-based interactive communication, can effectively identify and mitigate various cognitive biases in fact-checking. Moreover, such interactions significantly reduce individuals′ willingness to "like" disinformation, while showing limited impact on promoting high-effort corrective behaviors such as commenting.