# Pencils to Pixels: A Systematic Study of Creative Drawings across Children, Adults and AI

arXiv: https://arxiv.org/abs/2502.05999 · HTML: https://mlsanity.com/paper/2502.05999/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 45 references that are in the map.
- First posted on arXiv 9 Feb 2025.

## Builds on (most cited first, 10 of 10)

- [Optimizing Prompts for Text-to-Image Generation](https://mlsanity.com/paper/2212.09611/) (2022)
- [Rich Human Feedback for Text-to-Image Generation](https://mlsanity.com/paper/2312.10240/) (2023)
- [Separating Style and Content for Generalized Style Transfer](https://mlsanity.com/paper/1711.06454/) (2017)
- [Prompting AI Art: An Investigation into the Creative Skill of Prompt Engineering](https://mlsanity.com/paper/2303.13534/) (2023)
- [SketchAgent: Language-Driven Sequential Sketch Generation](https://mlsanity.com/paper/2411.17673/) (2024)
- [AI-AI Bias: large language models favor communications generated by large language models](https://mlsanity.com/paper/2407.12856/) (2024)
- [Evaluating Creative Short Story Generation in Humans and Large Language Models](https://mlsanity.com/paper/2411.02316/) (2024)
- [Small Language Models can Outperform Humans in Short Creative Writing: A Study Comparing SLMs with Humans and LLMs](https://mlsanity.com/paper/2409.11547/) (2024)
- [Visual Data-Type Understanding does not emerge from Scaling Vision-Language Models](https://mlsanity.com/paper/2310.08577/) (2023)
- [Substance or Style: What Does Your Image Embedding Know?](https://mlsanity.com/paper/2307.05610/) (2023)

## Built on by (most cited first, 6 of 6)

- [Beyond Divergent Creativity: A Human-Based Evaluation of Creativity in Large Language Models](https://mlsanity.com/paper/2601.20546/) (2026)
- [Do Large Language Models Always Tell The Same Stories?](https://mlsanity.com/paper/2606.17350/) (2026)
- [Simple Lines, Big Ideas: Towards Interpretable Assessment of Human Creativity from Drawings](https://mlsanity.com/paper/2511.12880/) (2025)
- [AGC-Bench: Measuring Artificial General Creativity](https://mlsanity.com/paper/2607.01152/) (2026)
- [Unified Agentic Video Editing Across Levels of Complexity and Creativity](https://mlsanity.com/paper/2609.12769/) (2026)
- [PainterBench: A Figural Divergent-Thinking Benchmark for Tool-Using Language Models](https://mlsanity.com/paper/2609.34195/) (2026)

## Abstract (arXiv)

Can we derive computational metrics to quantify visual creativity in drawings across intelligent agents, while accounting for inherent differences in technical skill and style? To answer this, we curate a novel dataset consisting of 1338 drawings by children, adults and AI on a creative drawing task. We characterize two aspects of the drawings -- (1) style and (2) content. For style, we define measures of ink density, ink distribution and number of elements. For content, we use expert-annotated categories to study conceptual diversity, and image and text embeddings to compute distance measures. We compare the style, content and creativity of children, adults and AI drawings and build simple models to predict expert and automated creativity scores. We find significant differences in style and content in the groups -- children's drawings had more components, AI drawings had greater ink density, and adult drawings revealed maximum conceptual diversity. Notably, we highlight a misalignment between creativity judgments obtained through expert and automated ratings and discuss its implications. Through these efforts, our work provides, to the best of our knowledge, the first framework for studying human and artificial creativity beyond the textual modality, and attempts to arrive at the domain-agnostic principles underlying creativity. Our data and scripts are available on GitHub.