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Ruqi Sun

Publications and source records attributed to Ruqi Sun.

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SiMUSation: An Interactive Visitor Experience Simulation Framework to Support Museum Exhibition Design

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.

cs.HC

VeriForge: Mitigating Latent Knowledge Gaps in Narrative Drafting via Mixed-Initiative Scaffolding

Great fiction earns its verisimilitude through precise details, from how a longsword is gripped to pierce armor gaps to why a bleeding corpse cannot yet smell of decay, weaving domain expertise into the fabric of invented worlds. Current AI writing tools offer limited support for discovering and integrating unfamiliar domain knowledge into narrative. They require explicit queries that authors cannot formulate, generate finished prose that risks homogenizing voice, or assist only within the boundaries of what authors already know. We argue that AI should reveal latent knowledge gaps to writers while preserving their agency to transform discovered knowledge into authentic prose. Grounded in formative interviews with 9 fiction writers, we present VeriForge, a mixed-initiative writing system that divides cognitive labor so that the system assumes initiative over domain discovery while the author retains full initiative over narrative synthesis. VeriForge realizes this through three complementary mechanisms. Proactive inline highlighting flags potential knowledge gaps as authors draft. Dual-stream querying pairs conversational responses with source-anchored Knowledge Cards for direct fact extraction. A spatial Knowledge Canvas allows authors to organize and connect discovered knowledge across their writing. These mechanisms are powered by a graph-based retrieval-augmented generation pipeline grounded in domain-specific source materials. A within-subjects user study (N=12) provides preliminary evidence that this paradigm helps authors recognize previously overlooked knowledge gaps, supports creative exploration, and is perceived by expert raters to produce passages with stronger domain grounding in a controlled cold-start writing task.

cs.HC