Personagram: 通过多模态大语言模型连接人设与产品设计以促进创意构思
Personagram: Bridging Personas and Product Design for Creative Ideation with Multimodal LLMs
AI总结：
Personagram利用多模态大语言模型帮助设计师探索基于人口普查的人设，提取并重新组合产品特征，提升创意构思的可行性和参与度。
摘要：
产品设计师常常以手工制作的人设作为设计过程的起点。尽管人设旨在使设计决策基于消费者偏好，但在实践中，人设往往因过于抽象、制作成本高且难以转化为具体的设计特征而显得不足。因此，人设可能更多地作为静态参考点而非主动塑造设计成果的工具。为了解决这些挑战，我们构建了Personagram，一个由多模态大语言模型（MLLMs）驱动的交互系统，帮助设计师探索基于人口普查的人设，提取从人设属性推断出的产品特征，并重新组合这些特征以适应特定客户群体。在与12名专业设计师的实验中，我们展示了Personagram通过从人设属性到产品设计特征的多模态思考结构，促进了更具操作性的创意构思流程，实现了比基于聊天的基线更高的参与度、感知透明度和满意度。我们讨论了将AI生成的人设整合到产品设计流程中的影响。
英文摘要：
Product designers often begin their design process with handcrafted personas. While personas are intended to ground design decisions in consumer preferences, they often fall short in practice by remaining abstract, expensive to produce, and difficult to translate into actionable design features. As a result, personas risk serving as static reference points rather than tools that actively shape design outcomes. To address these challenges, we built Personagram, an interactive system powered by multimodal large language models (MLLMs) that helps designers explore detailed census-based personas, extract product features inferred from persona attributes, and recombine them for specific customer segments. In a study with 12 professional designers, we show that Personagram facilitates more actionable ideation workflows by structuring multimodal thinking from persona attributes to product design features, achieving higher engagement with personas, perceived transparency, and satisfaction compared to a chat-based baseline. We discuss implications of integrating AI-generated personas into product design workflows.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
