SiMUSation: An Interactive Visitor Experience Simulation Framework to Support Museum Exhibition Design
Huanchen Wang, Qiuming Chen, Zhonghao Ji, Ruqi Sun, Zhichao Lu, Yuxin Ma
Abstract
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.
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