Interior design often struggles to capture the subtleties of client experience, leaving gaps between what clients feel and what designers can act upon. We present AIDED, a designer-AI co-design workflow that integrates multimodal client data into generative AI (GAI) design processes. In a within-subjects study with twelve professional designers, we compared four modalities: baseline briefs, gaze heatmaps, questionnaire visualizations, and AI-predicted overlays. Results show that questionnaire data were trusted, creativity-enhancing, and satisfying; gaze heatmaps increased cognitive load; and AI-predicted overlays improved GAI communication but required natural language mediation to establish trust. Interviews confirmed that an authenticity-interpretability trade-off is central to balancing client voices with professional control. Our contributions are: (1) a system that incorporates experiential client signals into GAI design workflows; (2) empirical evidence of how different modalities affect design outcomes; and (3) implications for future AI tools that support human-data interaction in creative practice.

翻译：室内设计往往难以捕捉客户体验的细微差别，导致客户感受与设计师可执行方案之间存在鸿沟。我们提出了AIDED——一种将多模态客户数据整合到生成式人工智能（GAI）设计流程中的设计师-AI协同设计工作流。通过对12位专业设计师开展被试内研究，我们比较了四种数据呈现模态：基线设计简报、注视热力图、问卷可视化图表以及AI预测叠加层。结果显示：问卷数据可信度高、能提升创造力且令人满意；注视热力图会增加认知负荷；AI预测叠加层改善了GAI沟通效果，但需通过自然语言调解建立信任。访谈证实，真实性-可解释性权衡是平衡客户意见与专业控制权的核心问题。我们的贡献包括：（1）将客户体验信号融入GAI设计工作流的系统；（2）不同模态如何影响设计成果的实证依据；（3）对未来支持创意实践中人机数据交互的AI工具的启示。