Personagram: Bridging Personas and Product Design for Creative Ideation with Multimodal LLMs
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866914308769710080 |
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| author | Kim, Taewook Hong, Matthew K. Chen, Yan-Ying Li, Jonathan Q. Van, Monica P Hakimi, Shabnam Kay, Matthew Klenk, Matthew |
| author_facet | Kim, Taewook Hong, Matthew K. Chen, Yan-Ying Li, Jonathan Q. Van, Monica P Hakimi, Shabnam Kay, Matthew Klenk, Matthew |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_06197 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Personagram: Bridging Personas and Product Design for Creative Ideation with Multimodal LLMs Kim, Taewook Hong, Matthew K. Chen, Yan-Ying Li, Jonathan Q. Van, Monica P Hakimi, Shabnam Kay, Matthew Klenk, Matthew Human-Computer Interaction Artificial Intelligence 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. |
| title | Personagram: Bridging Personas and Product Design for Creative Ideation with Multimodal LLMs |
| topic | Human-Computer Interaction Artificial Intelligence |
| url | https://arxiv.org/abs/2602.06197 |