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

2026-02-10

AI Summary

This study addresses the challenge that conventional interior design processes often fail to effectively capture the nuanced aspects of client experience, leading to a misalignment between design decisions and actual user needs. To bridge this gap, the authors propose a novel collaborative workflow integrating generative AI with human designers, which systematically incorporates multimodal client experience data—including survey responses, eye-tracking heatmaps, and AI-generated predictive overlays—to inform and drive the design generation process. The research further investigates how different data modalities influence designers’ trust, creativity, and cognitive load: survey data most effectively fostered trust and inspired creative ideation, eye-tracking heatmaps increased cognitive burden, and AI predictions required natural language explanations to enhance perceived credibility. These findings underscore the critical role of human-centered data in creative AI tools and propose a trade-off mechanism between authenticity and interpretability.

Technology Category

• Humans and AI: User Experience and Usability
• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Computer Vision: Multi-modal Vision

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• interior design
• client experience
• designer-AI co-design
• human-data interaction
• generative AI

Innovation

• designer-AI co-design
• generative AI
• multimodal client data
• human-data interaction
• experience-driven design