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.
@article{arxiv.2602.10054,
title = {AIDED: Augmenting Interior Design with Human Experience Data for Designer-AI Co-Design},
author = {Yang Chen Lin and Chen-Ying Chen and Kai-Hsin Hou and Hung-Yu Chen and Po-Chih Kuo},
journal= {arXiv preprint arXiv:2602.10054},
year = {2026}
}