# Artificial muses: Generative Artificial Intelligence Chatbots Have Risen to Human-Level Creativity

arXiv: https://arxiv.org/abs/2303.12003 · HTML: https://mlsanity.com/paper/2303.12003/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 67 references that are in the map.
- First posted on arXiv 21 Mar 2023.

## Builds on (most cited first, 2 of 2)

- [ChatGPT: Jack of all trades, master of none](https://mlsanity.com/paper/2302.10724/) (2023)
- [A very preliminary analysis of DALL-E 2](https://mlsanity.com/paper/2204.13807/) (2022)

## Built on by (most cited first, 24 of 24)

- [Generative AI](https://mlsanity.com/paper/2309.07930/) (2023)
- [Knowledge Mechanisms in Large Language Models: A Survey and Perspective](https://mlsanity.com/paper/2407.15017/) (2024)
- [Probing the Creativity of Large Language Models: Can models produce divergent semantic association?](https://mlsanity.com/paper/2310.11158/) (2023)
- [CoGenesis: A Framework Collaborating Large and Small Language Models for Secure Context-Aware Instruction Following](https://mlsanity.com/paper/2403.03129/) (2024)
- [Divergent Creativity in Humans and Large Language Models](https://mlsanity.com/paper/2405.13012/) (2024)
- [Large Language Models show both individual and collective creativity comparable to humans](https://mlsanity.com/paper/2412.03151/) (2024)
- [Beyond Static Responses: Multi-Agent LLM Systems as a New Paradigm for Social Science Research](https://mlsanity.com/paper/2506.01839/) (2025)
- [Has the Creativity of Large-Language Models peaked? An analysis of inter- and intra-LLM variability](https://mlsanity.com/paper/2504.12320/) (2025)
- [Generative AI and Creativity: A Systematic Literature Review and Meta-Analysis](https://mlsanity.com/paper/2505.17241/) (2025)
- [Domain-specific ChatBots for Science using Embeddings](https://mlsanity.com/paper/2306.10067/) (2023)
- [Deconstructing the Veneer of Simplicity: Co-Designing Introductory Generative AI Workshops with Local Entrepreneurs](https://mlsanity.com/paper/2402.17082/) (2024)
- [Speaker effects in language comprehension: An integrative model of language and speaker processing](https://mlsanity.com/paper/2412.07238/) (2024)
- [Expertise elevates AI usage: experimental evidence comparing laypeople and professional artists](https://mlsanity.com/paper/2501.12374/) (2025)
- [Towards a Science Exocortex](https://mlsanity.com/paper/2406.17809/) (2024)
- [Beyond Automation: Socratic AI, Epistemic Agency, and the Implications of the Emergence of Orchestrated Multi-Agent Learning Architectures](https://mlsanity.com/paper/2508.05116/) (2025)
- [Are Semantic Networks Associated with Idea Originality in Artificial Creativity? A Comparison with Human Agents](https://mlsanity.com/paper/2602.02048/) (2026)
- [The Effect of Idea Elaboration on the Automatic Assessment of Idea Originality](https://mlsanity.com/paper/2604.20569/) (2026)
- [It's a Feature, Not a Bug: Measuring Creative Fluidity in Image Generators](https://mlsanity.com/paper/2406.18570/) (2024)
- [Is GPT-4 conscious?](https://mlsanity.com/paper/2407.09517/) (2024)
- [Stable diffusion models reveal a persisting human and AI gap in visual creativity](https://mlsanity.com/paper/2511.16814/) (2025)
- [Creativity in AI as Emergence from Domain-Limited Generative Models](https://mlsanity.com/paper/2601.08388/) (2026)
- [Creativity Reconsidered: Generative AI and the Problem of Intentional Agency](https://mlsanity.com/paper/2601.15797/) (2026)
- [Artificial Intelligence and Innovation Ecosystem: Evolutionary Developments, Challenges, and Future Directions](https://mlsanity.com/paper/2607.24589/) (2026)
- [Recipes for Creativity: Iterative Generation and Evaluation in Large Language Models](https://mlsanity.com/paper/2608.07243/) (2026)

## Abstract (arXiv)

A widespread view is that Artificial Intelligence cannot be creative. We tested this assumption by comparing human-generated ideas with those generated by six Generative Artificial Intelligence (GAI) chatbots: alpa.!ai, Copy.!ai, ChatGPT (versions 3 and 4), Studio.!ai, and YouChat. Humans and a specifically trained AI independently assessed the quality and quantity of ideas. We found no qualitative difference between AI and human-generated creativity, although there are differences in how ideas are generated. Interestingly, 9.4 percent of humans were more creative than the most creative GAI, GPT-4. Our findings suggest that GAIs are valuable assistants in the creative process. Continued research and development of GAI in creative tasks is crucial to fully understand this technology's potential benefits and drawbacks in shaping the future of creativity. Finally, we discuss the question of whether GAIs are capable of being truly creative.