33 citations · 62 across the 14 of their papers we have counts for
16 papers
Elicitive User Interfaces: Designing How Users Shape Generative Interfaces
Eunhye Kim, Bryan Min, Haijun Xia +1
Generative user interfaces (GenUI) promise personalized interfaces to a user's tasks and needs. However, user needs are often implicit---difficult for systems to infer and users to…
Maru: Information Architecture as a Shared Language for Generating Aligned and Persistent User Interfaces
Eunhye Kim, DaEun Choi, Bryan Min +3
Generative user interfaces (GenUIs) promise on-demand components tailored to users' needs. As users iterate on information tasks, they construct personal structures over informatio…
"When to Hand Off, When to Work Together": Understanding Concurrent Human-Agent Interaction in Shared Co-Creative Workspaces
Kihoon Son, Hyewon Lee, DaEun Choi +6
Shared co-creative workspaces allow users to contribute while agents execute. Yet how users adjust their participation as their work and the agent's execution shape one another rem…
Contexty: Capturing and Organizing In-situ Thoughts for Context-Aware AI Support
Yoonsu Kim, Chanbin Park, Kihoon Son +2
During complex knowledge work, people engage in iterative sensemaking: interpreting information, connecting ideas, and refining their understanding. Yet in current human-AI collabo…
ClearFairy: Capturing Creative Workflows through Decision Structuring, In-Situ Questioning, and Rationale Inference
Kihoon Son, DaEun Choi, Tae Soo Kim +3
Capturing professionals' decision-making in creative workflows (e.g., UI/UX) is essential for reflection, collaboration, and knowledge sharing, yet existing methods often leave rat…
IdeaBlocks: Expressing and Reusing Divergent Intents for Graphic Design Exploration using Generative AI
DaEun Choi, Kihoon Son, Jaesang Yu +2
While designers increasingly leverage Generative AI for divergent exploration, current interaction is optimized for convergent refinement, forcing users to specify fixed targets ra…
IntentFlow: Investigating Fluid Dynamics of Intent Communication in Generative AI
Yoonsu Kim, Kihoon Son, Seoyoung Kim +2
Generative AI shifts interaction toward intent-based outcome specification, despite user intents being inherently vague, fluid, and evolving. While a growing body of HCI research h…
Applying the Gricean Maxims to a Human-LLM Interaction Cycle: Design Insights from a Participatory Approach
Yoonsu Kim, Brandon Chin, Kihoon Son +2
While large language models (LLMs) are increasingly used to assist users in various tasks through natural language interactions, these interactions often fall short due to LLMs' li…
Expandora: Broadening Design Exploration with Text-to-Image Model
DaEun Choi, Kihoon Son, Hyunjoon Jung +1
Broad exploration of references is critical in the visual design process. While text-to-image (T2I) models offer efficiency and customization of exploration, they often limit suppo…
Beyond Prompts: Learning from Human Communication for Enhanced AI Intent Alignment
Yoonsu Kim, Kihoon Son, Seoyoung Kim +1
AI intent alignment, ensuring that AI produces outcomes as intended by users, is a critical challenge in human-AI interaction. The emergence of generative AI, including LLMs, has i…
One vs. Many: Comprehending Accurate Information from Multiple Erroneous and Inconsistent AI Generations
Yoonjoo Lee, Kihoon Son, Tae Soo Kim +4
As Large Language Models (LLMs) are nondeterministic, the same input can generate different outputs, some of which may be incorrect or hallucinated. If run again, the LLM may corre…
Unveiling Disparities in Web Task Handling Between Human and Web Agent
Kihoon Son, Jinhyeon Kwon, DaEun Choi +4
With the advancement of Large-Language Models (LLMs) and Large Vision-Language Models (LVMs), agents have shown significant capabilities in various tasks, such as data analysis, ga…
Demystifying Tacit Knowledge in Graphic Design: Characteristics, Instances, Approaches, and Guidelines
Kihoon Son, DaEun Choi, Tae Soo Kim +1
Despite the growing demand for professional graphic design knowledge, the tacit nature of design inhibits knowledge sharing. However, there is a limited understanding on the charac…
CreativeConnect: Supporting Reference Recombination for Graphic Design Ideation with Generative AI
DaEun Choi, Sumin Hong, Jeongeon Park +2
Graphic designers often get inspiration through the recombination of references. Our formative study (N=6) reveals that graphic designers focus on conceptual keywords during this p…
ChoiceMates: Supporting Unfamiliar Online Decision-Making with Multi-Agent Conversational Interactions
Jeongeon Park, Bryan Min, Kihoon Son +3
From deciding on a PhD program to buying a new camera, unfamiliar decisions--decisions without domain knowledge--are frequent and significant. The complexity and uncertainty of suc…
GenQuery: Supporting Expressive Visual Search with Generative Models
Kihoon Son, DaEun Choi, Tae Soo Kim +2
Designers rely on visual search to explore and develop ideas in early design stages. However, designers can struggle to identify suitable text queries to initiate a search or to di…