Using Generative AI in Participatory Backcasting for Interior and Architectural Design

Generative AI technologies have been recognised as transformative tools. Their impact on the future of architecture and interior design is seen as inevitable as designers increasingly integrate these tools into various stages of the design process. Research has demonstrated that collaboration between the design team and decision-makers using Generative AI yields better results than a fully automated design process..
Backcasting is a planning method that involves envisioning a desired future and then working backwards to plan actions in the present to achieve that future. In a participatory backcasting approach, all stakeholders participate in workshops to develop scenarios and visions that guide present actions, using various tools—such as design, participatory, analytical, and organisational tools—to reach the desired design targets.
By employing Generative AI models as one of these planning tools during the participatory backcasting process, stakeholders can collaborate, explore multiple design iterations, save significant time, and allow designers to communicate their ideas to participants from different sectors. As a result, designers channel their efforts on innovation and critical problem-solving.
This study used two fine-tuned models based on Stable Difusion and conditional controling neural network called ControlNet to provide additional input and support the designer in achieving their vision. Industry design experts have been asked to evaluate the outputs from these two models and the used input guide of human sketches. This study investigates the integration of AI technologies—such as Stable Diffusion and ControlNet—with the Scribble model in participatory Backcasting in design, aiming to enhance collaborative problem-solving and foster innovative design strategies by combining AI-driven visual synthesis with structured future-oriented planning. Results of this study showed a comprehensive comparison of the two models across three domains was provided, and a framework integrating generative AI into participatory Backcasting workflows was devised to assist designers in various Backcasting approaches.