Insights Informed Generative AI for Design: Incorporating Real-world Data for Text-to-Image Output

Generative AI, specifically text - to - image models, have revolutionized interior architectural design by enabling the rapid translation of conceptual ideas into visual representations f rom simple text prompts . While generative AI can produce visually appealing images they often lack actionable data for designers In this work, we propose a novel pipeline that integrates DALL - E 3 with a materials dataset to enrich AI - generated designs with sustainability metrics and material usage insights. After the model generates an interior design image, a post - processing modul e identifies the top ten materials present and pairs them with carbon dioxide equivalent (CO e) values from a general materials dictionary. This approach allows designers to immediately evaluate environmental impacts and refine prompts accordingly. We eval uate the system through three user tests: (1) no mention of sustainability to the user prior to the prompting process with generative AI, (2) sustainability goals communicated to the user before prompting, and (3) sustainability goals communicated along wi th quantitative CO e data included in the generative AI outputs . Our q ualitative and quantitative analyses reveal that the introduction of sustainability metrics in the third test leads to more informed design decisions, however, it can also trigger decision fatigue and lower overall satisfaction. Nevertheless, the majority % of participants reported incorporating sustainability principles into their workflows in the th ird test, underscoring the potential of integrated metrics to guide more ecologically responsible practices. Our findings showcase the importance of balancing design freedom with practical constraints, offering a clear path toward holistic, data - driven solutions i n AI - assisted architectural design.