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<p>Conceptual engineering system design faces challenges from traditional methods and emerging AI tools to fully address its inherently complex, dynamic, and creativity-driven demands. iDesignGPT is a framework that integrates large language models with established design methodologies to enable dynamic multi-agent collaboration for problem refinement, information gathering, design space exploration, and evaluation. By incorporating design metrics such as coverage, diversity, and novelty, iDesignGPT provides quantitative insights for early-stage conceptual design. Performance evaluations across six public design challenges show that iDesignGPT achieves competitive novelty and consistently higher originality and modularity than GPT-4o zero-shot, GPT-4o chain-of-thought and Deepseek-r1, based on metrics and expert assessments. Two controlled user studies show positive reception across profiles and, for novice designers, lower mental demand than human-only design and clearer design flow with iDesignGPT. These results establish iDesignGPT as a practical framework for integrating language-model agents with established engineering design methods, enabling metrics-driven support for conceptual design by both expert and novice designers.</p>

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iDesignGPT enhances conceptual design via large language model agentic workflows

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月24日

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  • Songkai Liu,
  • Yanqing Shen,
  • Yilun Zhang,
  • Zhangli Hou,
  • Xin Wang,
  • Jianxi Luo,
  • Zhinan Zhang

摘要

Conceptual engineering system design faces challenges from traditional methods and emerging AI tools to fully address its inherently complex, dynamic, and creativity-driven demands. iDesignGPT is a framework that integrates large language models with established design methodologies to enable dynamic multi-agent collaboration for problem refinement, information gathering, design space exploration, and evaluation. By incorporating design metrics such as coverage, diversity, and novelty, iDesignGPT provides quantitative insights for early-stage conceptual design. Performance evaluations across six public design challenges show that iDesignGPT achieves competitive novelty and consistently higher originality and modularity than GPT-4o zero-shot, GPT-4o chain-of-thought and Deepseek-r1, based on metrics and expert assessments. Two controlled user studies show positive reception across profiles and, for novice designers, lower mental demand than human-only design and clearer design flow with iDesignGPT. These results establish iDesignGPT as a practical framework for integrating language-model agents with established engineering design methods, enabling metrics-driven support for conceptual design by both expert and novice designers.