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

TL;DR

AIDED introduces a co-design workflow integrating multimodal client data into generative AI for interior design, demonstrating how different data modalities influence trust, creativity, and communication in professional practice.

Contribution

The paper presents a novel system that incorporates experiential client signals into GAI workflows and provides empirical insights into modality effects on design outcomes.

Findings

Questionnaire data increased trust and satisfaction.

Gaze heatmaps raised cognitive load.

AI overlays improved communication but needed language mediation.

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.