# What is in a Text-to-Image Prompt: The Potential of Stable Diffusion in Visual Arts Education

arXiv: https://arxiv.org/abs/2301.01902 · HTML: https://mlsanity.com/paper/2301.01902/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jan 2023.

## Built on by (most cited first, 14 of 14)

- [In-Context Impersonation Reveals Large Language Models' Strengths and Biases](https://mlsanity.com/paper/2305.14930/) (2023)
- [Students' Voices on Generative AI: Perceptions, Benefits, and Challenges in Higher Education](https://mlsanity.com/paper/2305.00290/) (2023)
- [A Survey on Quality Metrics for Text-to-Image Generation](https://mlsanity.com/paper/2403.11821/) (2024)
- [Training-free Composite Scene Generation for Layout-to-Image Synthesis](https://mlsanity.com/paper/2407.13609/) (2024)
- [RAPO++: Cross-Stage Prompt Optimization for Text-to-Video Generation via Data Alignment and Test-Time Scaling](https://mlsanity.com/paper/2510.20206/) (2025)
- [Charting the Future of AI in Project-Based Learning: A Co-Design Exploration with Students](https://mlsanity.com/paper/2401.14915/) (2024)
- [PromptMap: An Alternative Interaction Style for AI-Based Image Generation](https://mlsanity.com/paper/2503.09436/) (2025)
- [Detail++: Training-Free Detail Enhancer for T2I Diffusion Models](https://mlsanity.com/paper/2507.17853/) (2025)
- [Picturing Ambiguity: A Visual Twist on the Winograd Schema Challenge](https://mlsanity.com/paper/2405.16277/) (2024)
- [PromptEvolver: Prompt Inversion through Evolutionary Optimization in Natural-Language Space](https://mlsanity.com/paper/2604.06061/) (2026)
- [Melon Fruit Detection and Quality Assessment Using Generative AI-Based Image Data Augmentation](https://mlsanity.com/paper/2407.10413/) (2024)
- [Speaking images. A novel framework for the automated self-description of artworks](https://mlsanity.com/paper/2506.05368/) (2025)
- [Thematic and Task-Based Categorization of K-12 GenAI Usages with Hierarchical Topic Modeling](https://mlsanity.com/paper/2508.09997/) (2025)
- [Evolutionary Token-Level Prompt Optimization for Diffusion Models](https://mlsanity.com/paper/2604.09861/) (2026)

## Abstract (arXiv)

Text-to-Image artificial intelligence (AI) recently saw a major breakthrough with the release of Dall-E and its open-source counterpart, Stable Diffusion. These programs allow anyone to create original visual art pieces by simply providing descriptions in natural language (prompts). Using a sample of 72,980 Stable Diffusion prompts, we propose a formalization of this new medium of art creation and assess its potential for teaching the history of art, aesthetics, and technique. Our findings indicate that text-to-Image AI has the potential to revolutionize the way art is taught, offering new, cost-effective possibilities for experimentation and expression. However, it also raises important questions about the ownership of artistic works. As more and more art is created using these programs, it will be crucial to establish new legal and economic models to protect the rights of artists.