# Deep Learning in a Computational Model for Conceptual Shifts in a Co-Creative Design System

arXiv: https://arxiv.org/abs/1906.10188 · HTML: https://mlsanity.com/paper/1906.10188/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2019.

## Built on by (most cited first, 5 of 5)

- [A Human-Computer Collaborative Tool for Training a Single Large Language Model Agent into a Network through Few Examples](https://mlsanity.com/paper/2404.15974/) (2024)
- [A Systematic Review of Human-AI Co-Creativity](https://mlsanity.com/paper/2506.21333/) (2025)
- [A Novel Mathematical Framework for Objective Characterization of Ideas](https://mlsanity.com/paper/2409.07578/) (2024)
- [Interaction-Centered Intelligence: Toward an Interaction-Based Theory of Human-AI Co-Creation](https://mlsanity.com/paper/2606.00807/) (2026)
- [Creativity in the Age of AI: Evaluating the Impact of Generative AI on Design Outputs and Designers' Creative Thinking](https://mlsanity.com/paper/2411.00168/) (2024)

## Abstract (arXiv)

This paper presents a computational model for conceptual shifts, based on a novelty metric applied to a vector representation generated through deep learning. This model is integrated into a co-creative design system, which enables a partnership between an AI agent and a human designer interacting through a sketching canvas. The AI agent responds to the human designer's sketch with a new sketch that is a conceptual shift: intentionally varying the visual and conceptual similarity with increasingly more novelty. The paper presents the results of a user study showing that increasing novelty in the AI contribution is associated with higher creative outcomes, whereas low novelty leads to less creative outcomes.