用多模态大模型把抽象人物画像转化为可落地的产品设计
Personagram: Bridging Personas and Product Design for Creative Ideation with Multimodal LLMs
- 通过多模态大模型将人口统计数据转化为具体产品特征
- 设计师使用该系统后对人物画像的参与度和满意度显著提升
- 适合需要快速生成用户导向设计创意的产品团队
产品设计常始于手工制作的人物画像，但这些画像往往抽象、成本高，难以转化为具体设计特征，易沦为静态参考。为此，我们构建了Personagram——一个基于多模态大语言模型的交互式系统，帮助设计师探索基于普查数据的人物画像，从中提取隐含的产品特征，并针对特定客户群体进行重组。在12位专业设计师的实验中，相较于基于聊天的基线方法，Personagram能更有效引导从人物属性到产品设计特征的多模态思维，显著提升对人物画像的参与度、感知透明度与满意度。
原文摘要 · Abstract (English)
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.
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