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.

翻译：设计包容性自行车基础设施需要平衡不同用户群体的竞争性需求，然而设计者往往难以预判不同骑行者对同一街道的体验差异。本研究探讨基于人设的多智能体评估如何通过显化体验冲突来支持包容性设计。我们提出StreetDesignAI交互系统，使设计者能够：（1）通过影像与地图数据建立街道情境化评估基准；（2）获取从自信型到谨慎型骑行者人设的并行反馈；（3）在迭代修改设计时显化跨视角冲突。对26位交通专业人士开展的组内研究表明，结构化多视角反馈显著提升了设计者对多元用户视角的理解能力、对人设需求的识别能力，以及将其转化为设计决策的信心，同时获得更高满意度与更强的专业采纳意愿。定性分析揭示冲突显化机制如何将设计探索从单视角优化转向审慎的权衡推理。最后，我们讨论了以分歧作为交互原语、通过AI工具支撑包容性设计的重要意义。