Chinese literati gatherings (Wenren Yaji), as a situated form of Chinese traditional culture, remain underexplored in depth. Although generative AI supports powerful multimodal generation, current cultural applications largely emphasize aesthetic reproduction and struggle to convey the deeper meanings of cultural rituals and social frameworks. Based on embodied cognition, we propose an AI-driven dual-path framework for cultural understanding, which we instantiate through GatheringSense, a literati-gathering experience. We conduct a mixed-methods study (N=48) to compare how AI-generated multimodal content and embodied participation complement each other in supporting the understanding of literati gatherings and fostering cultural resonance. Our results show that AI-generated content effectively improves the readability of cultural symbols and initial emotional attraction, yet limitations in physical coherence and micro-level credibility may affect users' satisfaction. In contrast, embodied experience significantly deepens participants' understanding of ritual rules and social roles, and increases their psychological closeness and presence. Based on these findings, we offer empirical evidence and five transferable design implications for generative experience in cultural heritage.

翻译：作为中国传统文化的一种情境化形式，文人雅集在深度探索方面仍显不足。尽管生成式人工智能支持强大的多模态生成，当前的文化应用大多侧重于美学再现，难以传达文化仪式与社会框架的深层意义。基于具身认知理论，我们提出了一种人工智能驱动的文化理解双路径框架，并通过名为GatheringSense的文人雅集体验系统进行实例化。我们采用混合方法研究（N=48），比较了AI生成的多模态内容与具身参与如何相互补充，以支持对文人雅集的理解并促进文化共鸣。研究结果表明，AI生成的内容有效提升了文化符号的可读性与初始情感吸引力，但物理连贯性与微观可信度方面的局限可能影响用户满意度。相比之下，具身体验显著加深了参与者对仪式规则与社会角色的理解，并增强了其心理亲近感与临场感。基于这些发现，我们为文化遗产的生成式体验提供了实证依据及五项可迁移的设计启示。