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

arXiv: https://arxiv.org/abs/2502.15105 · HTML: https://mlsanity.com/paper/2502.15105/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2025.

## Built on by (most cited first, 2 of 2)

- [Understanding, Protecting, and Augmenting Human Cognition with Generative AI: A Synthesis of the CHI 2025 Tools for Thought Workshop](https://mlsanity.com/paper/2508.21036/) (2025)
- [Generative Ontology Induction: Domain-Agnostic Schema Discovery from Document Corpora Using Large Language Models](https://mlsanity.com/paper/2607.16201/) (2026)

## Abstract (arXiv)

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.