# Human-AI Schema Discovery and Application for Creative Problem Solving

arXiv: https://arxiv.org/abs/2508.05045 · HTML: https://mlsanity.com/paper/2508.05045/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 10 references that are in the map.
- First posted on arXiv 7 Aug 2025.

## Builds on (most cited first, 3 of 3)

- [PopBlends: Strategies for Conceptual Blending with Large Language Models](https://mlsanity.com/paper/2111.04920/) (2021)
- [ReelFramer: Human-AI Co-Creation for News-to-Video Translation](https://mlsanity.com/paper/2304.09653/) (2023)
- [PodReels: Human-AI Co-Creation of Video Podcast Teasers](https://mlsanity.com/paper/2311.05867/) (2023)

## Abstract (arXiv)

Humans often rely on underlying structural patterns-schemas-to create, whether by writing stories, designing software, or composing music. Schemas help organize ideas and guide exploration, but they are often difficult to discover and apply, especially in complex or unfamiliar domains. My Ph.D. research develops a framework for human-AI schema discovery and application to support creative problem solving. I design systems that support users in sensemaking over examples to abstract schemas, and in operationalizing schemas into human-AI co-creative workflows for application. This research offers insights into how schema-guided interaction can make implicit knowledge more accessible and actionable, advancing more transparent and collaborative human-AI systems.