CoDesignAI: An AI-Enabled Multi-Agent, Multi-User System for Collaborative Urban Design at the Conceptual Stage

• URL: http://arxiv.org/abs/2603.16008v1
• Date: Mon, 16 Mar 2026 23:35:56 GMT
• Title: CoDesignAI: An AI-Enabled Multi-Agent, Multi-User System for Collaborative Urban Design at the Conceptual Stage
• Abstract summary: This study explores how large language models (LLMs) can support cooperation among community members in participatory design.<n>We introduce CoDesignAI, a collaborative urban design tool that combines multiple users, representing residents or stakeholders, with multiple AI agents.
• Score: 4.330735964358293
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Public participation has become increasingly important in collaborative urban design; yet, existing processes often face challenges in achieving efficient and scalable citizen engagement. To address this gap, this study explores how large language models (LLMs) can support cooperation among community members in participatory design. We introduce CoDesignAI, a collaborative urban design tool that combines multiple users, representing residents or stakeholders, with multiple AI agents, representing domain experts who provide facilitation and professional knowledge during the conceptual stage of urban design. This paper presents the system architecture and main components of the tool, illustrating how users interact with AI agents within a collaborative and iterative design workflow. Specifically, the system integrates generative AI with spatial mapping services to support street-level visualization of design proposals. AI agents assist users by summarizing discussion content, extracting shared design intentions, and generating prompts for presenting design interventions. The system also enables users to revise and refine their ideas over multiple rounds while documenting the design process. By combining conversational AI, multi-user interaction, and image-based design grounded in real-world urban contexts, this study argues that AI-enabled design systems can help shift urban design from an expert-centered practice to a more open and participatory process. The paper contributes a new web-based platform for AI-assisted collaborative design and offers an early exploration of how AI agents may expand the capacity for public participation in urban design.

Related papers

• WeDesign: Generative AI-Facilitated Community Consultations for Urban Public Space Design [1.3787753513429983]
  This paper examines how generative text-to-image methods, specifically Stable Diffusion XL integrated into a custom platform (WeDesign), may support equitable consultations.
  arXiv  Detail & Related papers  (2025-08-13T16:55:12Z)
• AI Automatons: AI Systems Intended to Imitate Humans [54.19152688545896]
  There is a growing proliferation of AI systems designed to mimic people's behavior, work, abilities, likenesses, or humanness.<n>The research, design, deployment, and availability of such AI systems have prompted growing concerns about a wide range of possible legal, ethical, and other social impacts.
  arXiv  Detail & Related papers  (2025-03-04T03:55:38Z)
• From Fake Perfects to Conversational Imperfects: Exploring Image-Generative AI as a Boundary Object for Participatory Design of Public Spaces [5.968063252533802]
  Image-generative artificial intelligence (IGAI) could support participatory design. We study how IGAI could facilitate participatory processes when designing public spaces. We conducted workshops and IGAI-mediated interviews in a real-world participatory process to upgrade a park in Los Angeles.
  arXiv  Detail & Related papers  (2024-11-01T18:02:46Z)
• Survey of User Interface Design and Interaction Techniques in Generative AI Applications [79.55963742878684]
  We aim to create a compendium of different user-interaction patterns that can be used as a reference for designers and developers alike. We also strive to lower the entry barrier for those attempting to learn more about the design of generative AI applications.
  arXiv  Detail & Related papers  (2024-10-28T23:10:06Z)
• Data Analysis in the Era of Generative AI [56.44807642944589]
  This paper explores the potential of AI-powered tools to reshape data analysis, focusing on design considerations and challenges. We explore how the emergence of large language and multimodal models offers new opportunities to enhance various stages of data analysis workflow. We then examine human-centered design principles that facilitate intuitive interactions, build user trust, and streamline the AI-assisted analysis workflow across multiple apps.
  arXiv  Detail & Related papers  (2024-09-27T06:31:03Z)
• Constraining Participation: Affordances of Feedback Features in Interfaces to Large Language Models [49.266685603250416]
  Large language models (LLMs) are now accessible to anyone with a computer, a web browser, and an internet connection via browser-based interfaces.<n>This article examines how interactive feedback features in ChatGPT's interface afford user participation in LLMs.
  arXiv  Detail & Related papers  (2024-08-27T13:50:37Z)
• Prototyping with Prompts: Emerging Approaches and Challenges in Generative AI Design for Collaborative Software Teams [2.237039275844699]
  Generative AI models are increasingly being integrated into human task, enabling the production of expressive content.<n>Unlike traditional human-AI design methods, the new approach to designing generative capabilities focuses heavily on prompt engineering strategies.<n>Our findings highlight emerging practices and role shifts in AI system prototyping among multistakeholder teams.
  arXiv  Detail & Related papers  (2024-02-27T17:56:10Z)
• DesignGPT: Multi-Agent Collaboration in Design [4.6272626111555955]
  DesignGPT uses artificial intelligence agents to simulate the roles of different positions in the design company and allows human designers to collaborate with them in natural language. Experimental results show that compared with separate AI tools, DesignGPT improves the performance of designers.
  arXiv  Detail & Related papers  (2023-11-20T08:05:52Z)
• Participatory Design Landscape for the Human-Machine Collaboration, Interaction and Automation at the Frontiers of HCI (PDL 2021) [7.305653067711372]
  This workshop will become a venue to share experiences and novel ideas in this area. We welcome a wide scope of contributions in HCI which explore sustainable opportunities for participatory design and development practices.
  arXiv  Detail & Related papers  (2022-07-07T10:44:14Z)
• Investigating Positive and Negative Qualities of Human-in-the-Loop Optimization for Designing Interaction Techniques [55.492211642128446]
  Designers reportedly struggle with design optimization tasks where they are asked to find a combination of design parameters that maximizes a given set of objectives. Model-based computational design algorithms assist designers by generating design examples during design. Black box methods for assistance, on the other hand, can work with any design problem.
  arXiv  Detail & Related papers  (2022-04-15T20:40:43Z)
• Stakeholder Participation in AI: Beyond "Add Diverse Stakeholders and Stir" [76.44130385507894]
  This paper aims to ground what we dub a 'participatory turn' in AI design by synthesizing existing literature on participation and through empirical analysis of its current practices. Based on our literature synthesis and empirical research, this paper presents a conceptual framework for analyzing participatory approaches to AI design.
  arXiv  Detail & Related papers  (2021-11-01T17:57:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.