Soobin Park



I am a design-oriented HCI researcher and a Ph.D. student in Industrial Design at KAIST, advised by Prof. Youn-kyung Lim. My research interests focus on exploring the opportunities and challenges of designing human-AI interactions centered around self-reflection and personal data. I aim to design and study new technologies in everyday life, employing a reflective, human-centered approach.


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Publications
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Daejeon Artience
Human-AI Co-Interpretation Interaction Design
AI as Reflective Resources
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News

2026-03
2026-02

2026-02
2026-02
2025-04
2025-04

Our paper has been accepted to DIS 2026!
Our position paper got accepted to Herding CATs: Making Sense of Creative Activity Traces workshop!
Attending CHI 2026 as a Student Volunteer!
Our paper has been accepted to TOCHI 2026!
Our paper received an Honorable Mention Award at CHI 2025!
Our paper has been accepted to CHI 2025!
TechnologyRedux: Revisiting Past, Reflecting Present, Provoking Future
Jiyeon Amy Seo, Soobin Park, EunJeong Cheon, Youn-kyung Lim, Hyungjun ChoACM Conference on Designing Interactive Systems 2026 (DIS 2026)
2026
Human-AI Interaction Traces as Blackout Poetry: Reframing AI-Supported Writing as Found-Text Creativity
Syemin Park, Soobin Park, Youn-kyung LimACM CHI WORKSHOP 2026
2026
Constella: Supporting Storywriters’ Interconnected Character Creation through LLM-based Multi-Agents
Syemin Park, Soobin Park, Youn-kyung LimACM Transactions on Computer-Human Interaction (TOCHI 2026)
2026

Reimagining Personal Data: Unlocking the Potential of AI-Generated Images in Personal Data Meaning-Making (🏅 Honorable Mention)
Soobin Park, Hankyung Kim, Youn-kyung Lim
ACM Conference on Human Factors in Computing Systems 2025 (CHI 2025) 2025

Investigating the Potential of Group Recommendation Systems As a Medium of Social Interactions: A Case of Experiences Between Two Users
 Daehyun Kwak, Soobin Park, Inha Cha, Hankyung Kim, Youn-kyung LimACM Conference on Human Factors in Computing Systems 2024 (CHI 2024)2024


All Publications
WIP...Daejeon Artience 2025- (Exhibition in November, 2026)

(will be updated...)
Human-AI Co-Interpretation Interaction Design
2026-

Generative AI systems are increasingly embedded in our daily lives. However, current human-AI interaction is designed primarily around optimizing for productivity and efficiency, leaving users as passive consumers of AI-produced content. This dynamic has been shown to erode critical thinking, reduce creative diversity, and foster over-reliance on AI-generated outputs. In this project, we propose Human-AI Co-Interpretation Interaction as a new interaction model that repositions AI as a resource for expanding human thought. Rather than designing AI systems that strive to deliver definitive answers, we center on the human reinterpretation of AI outputs in the user’s own way, foregrounding the user’s own meaning-making as the core value of the interaction.

To ground this model, we draw on generative AI’s inherent variability, its probabilistic nature produces unexpected, open-ended outputs that defamiliarize the familiar, invite multiple interpretive angles, and leave room for users to reason and imagine beyond what the system explicitly provides. We explore Human-AI Co-Interpretation Interaction across three application domains: personal informatics, creativity support tools, and everyday smart objects. Through this project, we aim to develop a conceptual framework and design guidelines for AI systems based on co-interpretation, ultimately building toward interactions that enhance rather than diminish users’ critical, reflective and creative capacities.
AI as Reflective Resources
2024-2025

With the increasing availability of personal data in everyday life, supporting people’s meaning-making from personal data has become an important topic in HCI research. Prior work has explored alternative ways of representing personal data, such as data visualization or data physicalization, to facilitate more meaningful and reflective engagement with personal data. Recently, generative AI has introduced new opportunities to transform personal data into alternative visual forms. In particular, image-generative AI can represent personal data in more subjective, abstract, and imaginative visual forms, which may encourage users to reflect on and interpret their data through imagination and speculation. Based on this background, we aim to explore new possibilities of how image-generative AI can be leveraged to design for personal data meaning-making.


Publication: CHI 2025 Paper
Image Collage Generator
Designed and developed an application generating new forms of images by transforming image pixels.
2022

Coral Generator

Designed and developed an application generating coral shapes using L-system algorithms.2022


Emoji Finder

Designed and developed an application recommending emojis based on face detection.

VIDEO2022


Face Hawaiian Ukulele

Designed and developed an application generating Hawaiian ukulele sound with the sound of wave, wind and bird based on face detection.

VIDEO2022


Wrist Checker

Created a device designed to help maintain proper wrist posture while using a computer. The device visualizes the user's current posture as a character on the screen and provides corrective feedback if the wrist position deviates. Additionally, it presents a final report based on the user's adherence to correct wrist positioning.

VIDEO2022


I’m not a flower, I’m a flame

Participated in a workshop to visualize the sentences of contemporary Korean feminists. Designed a graphic symbol with Jin-Ah Kim, a Korean copywriter’s sentence, “I’m not a flower, I’m a flame.”

Collaboration: Subin Choi, Haneul Park
Workshop: FDSC
Photography: Kyeonghee Kang (Filed)

2019

Drawing Machine

Created a drawing machine that generates unique results each time by drawing with lines that are spontaneously formed through movement.

Collaboration: Chanwoo Lee

2018