Design Activity Simulation: Opportunities and Challenges in Using Multiple Communicative AI Agents to Tackle Design Problems.

Large Language Models (LLMs) can enhance structured design thinking, yet existing copilot approaches integrate them into human workflows rather than exploring their autonomous potential. This paper investigates how LLM-based communicative AI agents can independently tackle open-ended design problems and how their strengths and limitations inform human-AI collaboration. We iteratively design a system where AI agents play different roles and simulate human design activity through conversational turns. The agents investigate user needs, identify design constraints, and explore the design space, with useful insights emerging from their interactions. To assess reasoning quality, we conducted a human jury evaluation with five HCI researchers and explored potential applications through a contextual inquiry with seven professionals. Our findings demonstrate that integrating human design thinking techniques enhances AI reasoning. AI agents effectively tackle design problems, generating low-novelty yet well-grounded and practical solutions that meet key design requirements.