The presented research explores the integration of Generative Artificial Intelligence (AI) tools into the Sustainable Aided Design (SADE) framework and assesses their potential and challenges in addressing actual issues within sustainable design. SADE is a methodology focused on enhancing project integration within the context. The research questions whether generative AI can assist architects in designing more sustainable buildings by providing early-stage control over sustainability aspects. The methodology involves the use of two approaches such as text-toimage and chatbot to assess the quality of responses regarding specific instructions for high-rise and low-rise residential projects based on environmental and bioclimatic design parameters. The focus is on passive strategies that are crucial in the ultra-early stage of design processes. The research transforms textual instructions from chatbots into prompts for text-to-image processes, refining design concepts based on the correspondence between chatbot suggestions and generated images through 3D models. In conclusion, the research proposes a systemic framework to evaluate the support that AI tools can provide in terms of visual inspiration, textual instructions, and programmatic outputs for architectural design within the context of SADE. It calls for systematic scientific research to assess the opportunities and challenges these tools pose in enhancing environmental sustainability.

Generative Artificial Intelligence (AI) and Sustainable-Aided Design. Opportunities and Challenges / Figliola, Angelo; Barberio, Maurizio; Del Razo Montiel, Arturo; Rienzo, Pasquale. - (2025), pp. 409-419. (Intervento presentato al convegno 1st International Conference on Creativity, Technology, and Sustainability tenutosi a Jeddah, Saudi Arabia) [10.1007/978-981-97-8588-9_39].

Generative Artificial Intelligence (AI) and Sustainable-Aided Design. Opportunities and Challenges

Figliola, Angelo
Primo
Methodology
;
2025

Abstract

The presented research explores the integration of Generative Artificial Intelligence (AI) tools into the Sustainable Aided Design (SADE) framework and assesses their potential and challenges in addressing actual issues within sustainable design. SADE is a methodology focused on enhancing project integration within the context. The research questions whether generative AI can assist architects in designing more sustainable buildings by providing early-stage control over sustainability aspects. The methodology involves the use of two approaches such as text-toimage and chatbot to assess the quality of responses regarding specific instructions for high-rise and low-rise residential projects based on environmental and bioclimatic design parameters. The focus is on passive strategies that are crucial in the ultra-early stage of design processes. The research transforms textual instructions from chatbots into prompts for text-to-image processes, refining design concepts based on the correspondence between chatbot suggestions and generated images through 3D models. In conclusion, the research proposes a systemic framework to evaluate the support that AI tools can provide in terms of visual inspiration, textual instructions, and programmatic outputs for architectural design within the context of SADE. It calls for systematic scientific research to assess the opportunities and challenges these tools pose in enhancing environmental sustainability.
2025
1st International Conference on Creativity, Technology, and Sustainability
generative artificial intelligence; sustainable-aided design; text-to-images; data-driven design
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Generative Artificial Intelligence (AI) and Sustainable-Aided Design. Opportunities and Challenges / Figliola, Angelo; Barberio, Maurizio; Del Razo Montiel, Arturo; Rienzo, Pasquale. - (2025), pp. 409-419. (Intervento presentato al convegno 1st International Conference on Creativity, Technology, and Sustainability tenutosi a Jeddah, Saudi Arabia) [10.1007/978-981-97-8588-9_39].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1744519
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