PersonaFlow: Designing LLM-Simulated Expert Perspectives for Enhanced Research Ideation

Generating interdisciplinary research ideas requires diverse domain expertise, but access to timely feedback is often limited by the availability of experts. In this paper, we introduce \textit{PersonaFlow}, a novel system designed to provide multiple perspectives by using LLMs to simulate domain-specific experts. Our user studies showed that the new design 1) increased the perceived relevance and creativity of ideated research directions, and 2) promoted users’ critical thinking activities (e.g., \textit{interpretation}, \textit{analysis}, \textit{evaluation}, \textit{inference}, and \textit{self-regulation}), without increasing their perceived cognitive load. Moreover, users’ ability to customize expert profiles significantly improved their sense of agency, which can potentially mitigate their over-reliance on AI. This work contributes to the design of intelligent systems that augment creativity and collaboration, and provides design implications of using customizable AI-simulated personas in domains within and beyond research ideation.