AIDED: Augmenting Interior Design with Human Experience Data for Designer-AI Co-Design

Yang Chen Lin; Chen-Ying Chen; Kai-Hsin Hou; Hung-Yu Chen; Po-Chih Kuo

doi:10.1145/3772318.3791378

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arXiv · 2602.10054

AIDED: Augmenting Interior Design with Human Experience Data for Designer-AI Co-Design

Yang Chen Lin·Chen-Ying Chen·Kai-Hsin Hou·Hung-Yu Chen·Po-Chih Kuo

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Abstract

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.

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Yang Chen Lin, Chen-Ying Chen, Kai-Hsin Hou, Hung-Yu Chen, Po-Chih Kuo. 2026-02-10. AIDED: Augmenting Interior Design with Human Experience Data for Designer-AI Co-Design. https://doi.org/10.1145/3772318.3791378

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