# The Ink Splotch Effect: A Case Study on ChatGPT as a Co-Creative Game Designer

arXiv: https://arxiv.org/abs/2403.02454 · HTML: https://mlsanity.com/paper/2403.02454/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 24 references that are in the map.
- First posted on arXiv 4 Mar 2024.

## Builds on (most cited first, 9 of 9)

- [Level Generation Through Large Language Models](https://mlsanity.com/paper/2302.05817/) (2023)
- [Friend, Collaborator, Student, Manager: How Design of an AI-Driven Game Level Editor Affects Creators](https://mlsanity.com/paper/1901.06417/) (2019)
- [Language as Reality: A Co-Creative Storytelling Game Experience in 1001 Nights using Generative AI](https://mlsanity.com/paper/2308.12915/) (2023)
- [The Five-Dollar Model: Generating Game Maps and Sprites from Sentence Embeddings](https://mlsanity.com/paper/2308.04052/) (2023)
- ["An Adapt-or-Die Type of Situation": Perception, Adoption, and Use of Text-To-Image-Generation AI by Game Industry Professionals](https://mlsanity.com/paper/2302.12601/) (2023)
- [Lode Encoder: AI-constrained co-creativity](https://mlsanity.com/paper/2308.01312/) (2023)
- [A Preliminary Study on a Conceptual Game Feature Generation and Recommendation System](https://mlsanity.com/paper/2308.13538/) (2023)
- [Design-Driven Requirements for Computationally Co-Creative Game AI Design Tools](https://mlsanity.com/paper/2107.13738/) (2021)
- [Designing Game Feel. A Survey](https://mlsanity.com/paper/2011.09201/) (2020)

## Built on by (most cited first, 12 of 12)

- [GAVEL: Generating Games Via Evolution and Language Models](https://mlsanity.com/paper/2407.09388/) (2024)
- [Word2World: Generating Stories and Worlds through Large Language Models](https://mlsanity.com/paper/2405.06686/) (2024)
- [MultiGen: Level-Design for Editable Multiplayer Worlds in Diffusion Game Engines](https://mlsanity.com/paper/2603.06679/) (2026)
- [Unbounded: A Generative Infinite Game of Character Life Simulation](https://mlsanity.com/paper/2410.18975/) (2024)
- [FactorSim: Generative Simulation via Factorized Representation](https://mlsanity.com/paper/2409.17652/) (2024)
- [GPT for Games: An Updated Scoping Review (2020-2024)](https://mlsanity.com/paper/2411.00308/) (2024)
- [Grammar-based Game Description Generation using Large Language Models](https://mlsanity.com/paper/2407.17404/) (2024)
- [AnimeGamer: Infinite Anime Life Simulation with Next Game State Prediction](https://mlsanity.com/paper/2504.01014/) (2025)
- [From Code to Play: Benchmarking Program Search for Games Using Large Language Models](https://mlsanity.com/paper/2412.04057/) (2024)
- [Can Large Language Models Create New Knowledge for Spatial Reasoning Tasks?](https://mlsanity.com/paper/2405.14379/) (2024)
- [Fly, Fail, Fix: Iterative Game Repair with Reinforcement Learning and Large Multimodal Models](https://mlsanity.com/paper/2507.12666/) (2025)
- [Multi-Agent Game Generation and Evaluation via Audio-Visual Recordings](https://mlsanity.com/paper/2508.00632/) (2025)

## Abstract (arXiv)

This paper studies how large language models (LLMs) can act as effective, high-level creative collaborators and ``muses'' for game design. We model the design of this study after the exercises artists use by looking at amorphous ink splotches for creative inspiration. Our goal is to determine whether AI-assistance can improve, hinder, or provide an alternative quality to games when compared to the creative intents implemented by human designers. The capabilities of LLMs as game designers are stress tested by placing it at the forefront of the decision making process. Three prototype games are designed across 3 different genres: (1) a minimalist base game, (2) a game with features and game feel elements added by a human game designer, and (3) a game with features and feel elements directly implemented from prompted outputs of the LLM, ChatGPT. A user study was conducted and participants were asked to blindly evaluate the quality and their preference of these games. We discuss both the development process of communicating creative intent to an AI chatbot and the synthesized open feedback of the participants. We use this data to determine both the benefits and shortcomings of AI in a more design-centric role.