PosterMate: Audience-driven Collaborative Persona Agents for Poster Design
Donghoon Shin, Daniel Lee, Gary Hsieh, Gromit Yeuk-Yin Chan

TL;DR
PosterMate is a collaborative poster design tool that uses AI-generated audience personas to gather diverse feedback and facilitate consensus, improving design relevance and capturing overlooked viewpoints.
Contribution
It introduces a novel AI-driven system that creates audience personas from marketing documents to enhance collaborative poster design and feedback synthesis.
Findings
Effective in capturing overlooked viewpoints
Facilitates consensus through discussion among personas
Proven to be an efficient prototyping tool
Abstract
Poster designing can benefit from synchronous feedback from target audiences. However, gathering audiences with diverse perspectives and reconciling them on design edits can be challenging. Recent generative AI models present opportunities to simulate human-like interactions, but it is unclear how they may be used for feedback processes in design. We introduce PosterMate, a poster design assistant that facilitates collaboration by creating audience-driven persona agents constructed from marketing documents. PosterMate gathers feedback from each persona agent regarding poster components, and stimulates discussion with the help of a moderator to reach a conclusion. These agreed-upon edits can then be directly integrated into the poster design. Through our user study (N=12), we identified the potential of PosterMate to capture overlooked viewpoints, while serving as an effective…
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