Public trust in generative artificial intelligence exhibits increasingly divergent patterns across national contexts, yet prevailing research largely overlooks the macro-structural forces underlying this divergence. This study argues that trust in AI is not merely a technical response to performance but a product of institutional refraction. We propose an ``Institutional Prism'' framework to demonstrate how institutional trust shapes user trust in domestic (DeepSeek) and global (ChatGPT) large language models. Drawing on Cognitive-Affective Trust Theory, we distinguish between cognitive and affective dimensions of trust and analyze survey data from 405 Chinese users. The findings show that higher institutional trust is positively associated with stronger affective trust in domestic AI models and shifts cognitive evaluations in a more favorable direction. While under lower institutional trust, this domestic advantage weakens. These findings reveal that institutional trust has emerged as a core dimension of AI trust formation. By linking micro-level psychological judgments with macro-level governance, this research contributes a new perspective to human-machine communication.

翻译：公众对生成式人工智能的信任呈现日益明显的跨国分化模式，然而现有研究大多忽视了导致这种分化的宏观结构性力量。本研究提出，对AI的信任不仅是基于技术表现的反应，更是制度折射的产物。我们构建了“制度棱镜”理论框架，用以阐释制度信任如何塑造用户对国内（DeepSeek）与全球（ChatGPT）大型语言模型的信任差异。基于认知-情感信任理论，我们区分了信任的认知维度和情感维度，并分析了405名中国用户的调查数据。研究发现：较高的制度信任与对国内AI模型更强的情感信任呈正相关，并推动认知评价向更有利的方向转变；而在制度信任较低时，这种国内优势则会减弱。这些发现揭示了制度信任已成为AI信任形成的核心维度。通过将微观层面的心理判断与宏观层面的治理机制相联系，本研究为人机传播研究提供了新的理论视角。