Generative and Malleable User Interfaces with Generative and Evolving Task-Driven Data Model

要旨

Unlike static and rigid user interfaces, generative and malleable user interfaces offer the potential to respond to diverse users’ goals and tasks. However, current approaches primarily rely on generating code, making it difficult for end-users to iteratively tailor the generated interface to their evolving needs. We propose employing task-driven data models—representing the essential information entities, relationships, and data within information tasks—as the foundation for UI generation. We leverage AI to interpret users’ prompts and generate the data models that describe users’ intended tasks, and by mapping the data models with UI specifications, we can create generative user interfaces. End-users can easily modify and extend the interfaces via natural language and direct manipulation, with these interactions translated into changes in the underlying model. The technical evaluation of our approach and user evaluation of the developed system demonstrate the feasibility and effectiveness of generative and malleable user interfaces.

著者
Yining Cao
University of California, San Diego, San Diego, California, United States
Peiling Jiang
University of California San Diego, San Diego, California, United States
Haijun Xia
University of California, San Diego, San Diego, California, United States
DOI

10.1145/3706598.3713285

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713285

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: Malleable and Adaptive Interface

G401
7 件の発表
2025-04-28 20:10:00
2025-04-28 21:40:00
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