StreetDesignAI: A Multi-Persona Evaluation System for Inclusive Infrastructure Design
This addresses the problem of inclusive infrastructure design for transportation professionals, offering a novel tool to make experiential conflicts explicit, though it is incremental in applying persona-based methods to this domain.
The paper tackles the challenge of designing inclusive cycling infrastructure by balancing diverse user needs, presenting StreetDesignAI, a system that uses multi-agent evaluation to provide parallel feedback from different cyclist personas, resulting in improved understanding of user perspectives and higher satisfaction among 26 transportation professionals.
Designing inclusive cycling infrastructure requires balancing competing needs of diverse user groups, yet designers often struggle to anticipate how different cyclists experience the same street. We investigate how persona-based multi-agent evaluation can support inclusive design by making experiential conflicts explicit. We present StreetDesignAI, an interactive system that enables designers to (1) ground evaluation in street context through imagery and map data, (2) receive parallel feedback from cyclist personas spanning confident to cautious users, and (3) iteratively modify designs while surfacing conflicts across perspectives. A within-subjects study with 26 transportation professionals demonstrates that structured multi-perspective feedback significantly improves designers' understanding of diverse user perspectives, ability to identify persona needs, and confidence in translating them into design decisions, with higher satisfaction and stronger intention for professional adoption. Qualitative findings reveal how conflict surfacing transforms design exploration from single-perspective optimization toward deliberate trade-off reasoning. We discuss implications for AI tools that scaffold inclusive design through disagreement as an interaction primitive.