FrameKit: A Tool for Authoring Adaptive UIs Using Keyframes
Authors
Adaptive user interfaces (AUIs) can improve user experience by adapting how information and functionality are presented in a user interface. However, the dynamic nature and potentially numerous variations of AUIs make them challenging to author. In this paper, we present a generalized framework for defining adaptation as interpolations between UIs and introduce a computational approach for intelligently generating new variations of a UI from a small set of designs. Based on this approach, we built FrameKit, an authoring tool with a programming-by-example interface that retains flexibility and control afforded by manual authoring while reducing effort through automatic generation. We demonstrate that FrameKit can support adaptations that typically require domain-specific toolkits, such as those found in context-aware applications, responsive UIs, and ability-based adaptation. We evaluated FrameKit with ten front-end developers, who successfully authored AUIs after a short tutorial session and suggested that FrameKit provides an effective mental model for AUI authoring.
Research Questions / Practical Problems
Question signals indexed for this paper.
- 80%
EvalLM: Interactive Evaluation of Large Language Model Prompts on User-Defined Criteria
CHI '24· Human-LLM Collaboration +1
- 80%
RAG Without the Lag: Enabling "What-If" Analysis for Retrieval-Augmented Generation Pipelines
CHI '26· Human-LLM Collaboration +1
- 67%
GenieWizard: Multimodal App Feature Discovery with Large Language Models
CHI '25· Full-Body Interaction & Embodied Input +2
- 67%
Prototyping with Prompts: Emerging Approaches and Challenges in Generative AI Design for Collaborative Software Teams
CHI '25· Generative AI (Text, Image, Music, Video) +2
- 67%
Assistance or Disruption? Exploring and Evaluating the Design and Trade-offs of Proactive AI Programming Support
CHI '25· Human-LLM Collaboration +2
- 67%
PointAloud: An Interaction Suite for AI-Supported Pointer-Centric Think-Aloud Computing
CHI '26· Human-LLM Collaboration +2
- 67%
Computer Science Achievement and Writing Skills Predict Vibe Coding Proficiency
CHI '26· Human-LLM Collaboration +2
- 67%
From Throw-Away to Takeaway: How GenAI and Vibe Coding Accelerate Prototyping Across Technical Skill Levels
CHI '26· Generative AI (Text, Image, Music, Video) +2
- 67%
The Way We Notice, That’s What Really Matters: Instantiating UI Components with Distinguishing Variations
CHI '26· Human-LLM Collaboration +2
- 67%
Creating Design Resources to Scaffold the Ideation of AI Concepts
DIS '23· Generative AI (Text, Image, Music, Video) +2
Based on Jaccard similarity of research subtopics & professions (≥60%)