AI 中文总结
该研究提出SiMUSation交互式框架，通过LLM驱动的角色模拟，支持博物馆展览早期设计，经12人用户研究验证其可辅助设计反思优化。
AI 中文摘要
理解不同受众如何参与叙事与内容是展览设计的核心，但设计师往往依赖直觉。现有体验评估方法通常是回顾性的、成本高昂，且难以获取参观者的内部状态，阻碍了早期迭代优化。我们不仅依赖于使用真实参观者的实施后评估，而是探索大语言模型（LLM）驱动的角色模拟作为早期设计的参考。基于此，我们提出SiMUSation，一种用于支持早期展览设计的交互式框架。SiMUSation对不同参观者角色进行建模，并通过双层表示模拟其展览体验，该表示将可观测行为（如移动和注视）与相应的内部反应（如困惑和叙事参与）耦合。设计师可引导模拟、检查模拟参观的反馈，并迭代修改布局、内容和叙事流程，以进一步探究变更如何重塑参观者体验。我们实现了一个原型，并通过用户研究（N=12）对其进行评估，结果表明SiMUSation为早期展览设计的反思与优化提供了洞见。我们的发现进一步凸显了角色驱动模拟在跨设计任务中支持受众知情评估与迭代决策的潜力。
英文摘要
Understanding how diverse audiences engage with narratives and content is central to exhibition design, yet designers often rely on intuition. Existing experience evaluation methods are typically retrospective, costly, and offer limited access to visitors' internal states, hindering early-stage iterative refinement. Rather than relying only on post-implementation evaluation with real visitors, we explore LLM-driven persona simulation as a reference for early-stage design. Following this idea, we present SiMUSation, an interactive framework designed to support early-stage exhibition design. SiMUSation models diverse visitor personas and simulates their exhibition experiences through a dual-layer representation that couples observable behaviors, such as movement and gaze, with corresponding internal responses, such as confusion and narrative engagement. Designers can steer simulations, inspect feedback from simulated visits, and iteratively revise layouts, content, and narrative flow to further examine how changes reshape visitor experience. We implemented a prototype and evaluated it through a user study (N=12), showing that SiMUSation provides insights for reflection and refinement in early-stage exhibition design. Our findings further highlight the potential of persona-driven simulation to support audience-informed evaluation and iterative decision-making across design tasks.
Comments15 pages, 7 figures, 3 tables, Accepted by ACM UIST 2026
