Schemex: Discovering Design Patterns from Examples through Iterative Abstraction and Refinement

TL;DR

Schemex is an AI-assisted workflow that improves human schema induction by combining clustering, abstraction, and refinement, demonstrated through real-world case studies showing high accuracy and usefulness.

Contribution

The paper introduces Schemex, a novel AI-powered approach that enhances schema induction through iterative abstraction and refinement stages, supporting human-AI collaboration.

Findings

High accuracy in schema generation demonstrated in case studies

Schemex effectively supports human high-level thinking tasks

Qualitative analysis confirms usefulness of generated schemas

Abstract

Expertise is often built by learning from examples. This process, known as schema induction, helps us identify patterns from examples. Despite its importance, schema induction remains a challenging cognitive task. Recent advances in generative AI reasoning capabilities offer new opportunities to support schema induction through human-AI collaboration. We present Schemex, an AI-powered workflow that enhances human schema induction through three stages: clustering, abstraction, and refinement via contrasting examples. We conducted an initial evaluation of Schemex through two real-world case studies: writing abstracts for HCI papers and creating news TikToks. Qualitative analysis demonstrates the high accuracy and usefulness of the generated schemas. We also discuss future work on developing more flexible methods for workflow construction to help humans focus on high-level thinking.