Reimagining Personal Data: Unlocking the Potential of AI-Generated Images in Personal Data Meaning-Making

要旨

Image-generative AI provides new opportunities to transform personal data into alternative visual forms. In this paper, we illustrate the potential of AI-generated images in facilitating meaningful engagement with personal data. In a formative autobiographical design study, we explored the design and use of AI-generated images derived from personal data. Informed by this study, we designed a web-based application as a probe that represents personal data through generative images utilizing Open AI’s GPT-4 model and DALL-E 3. We then conducted a 21-day diary study and interviews using the probe with 16 participants to investigate users’ in-depth experiences with images generated by AI in everyday lives. Our findings reveal new qualities of experiences in users’ engagement with data, highlighting how participants constructed personal meaning from their data through imagination and speculation on AI-generated images. We conclude by discussing the potential and concerns of leveraging image-generative AI for personal data meaning-making.

受賞
Honorable Mention
著者
Soobin Park
KAIST, Daejeon, Korea, Republic of
Hankyung Kim
KAIST, Daejeon, Korea, Republic of
Youn-kyung Lim
KAIST, Daejeon, Korea, Republic of
DOI

10.1145/3706598.3713722

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713722

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Image and AI

G303
7 件の発表
2025-04-28 23:10:00
2025-04-29 00:40:00
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