# A Systematic Review of Human-AI Co-Creativity

arXiv: https://arxiv.org/abs/2506.21333 · HTML: https://mlsanity.com/paper/2506.21333/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 132 references that are in the map.
- First posted on arXiv 26 Jun 2025.

## Builds on (most cited first, 17 of 17)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [A Comprehensive Overview of Large Language Models](https://mlsanity.com/paper/2307.06435/) (2023)
- [Designing Creative AI Partners with COFI: A Framework for Modeling Interaction in Human-AI Co-Creative Systems](https://mlsanity.com/paper/2204.07666/) (2022)
- [Beyond Text Generation: Supporting Writers with Continuous Automatic Text Summaries](https://mlsanity.com/paper/2208.09323/) (2022)
- [Choice Over Control: How Users Write with Large Language Models using Diegetic and Non-Diegetic Prompting](https://mlsanity.com/paper/2303.03199/) (2023)
- [Spellburst: A Node-based Interface for Exploratory Creative Coding with Natural Language Prompts](https://mlsanity.com/paper/2308.03921/) (2023)
- [Collaborative Storytelling with Large-scale Neural Language Models](https://mlsanity.com/paper/2011.10208/) (2020)
- [Co-Creative Level Design via Machine Learning](https://mlsanity.com/paper/1809.09420/) (2018)
- [Tweetorial Hooks: Generative AI Tools to Motivate Science on Social Media](https://mlsanity.com/paper/2305.12265/) (2023)
- [A Collaborative, Interactive and Context-Aware Drawing Agent for Co-Creative Design](https://mlsanity.com/paper/2209.12588/) (2022)
- [Creative Wand: A System to Study Effects of Communications in Co-Creative Settings](https://mlsanity.com/paper/2208.02886/) (2022)
- [Lode Encoder: AI-constrained co-creativity](https://mlsanity.com/paper/2308.01312/) (2023)
- [Deep Learning in a Computational Model for Conceptual Shifts in a Co-Creative Design System](https://mlsanity.com/paper/1906.10188/) (2019)
- [Deep Learning of Individual Aesthetics](https://mlsanity.com/paper/2009.12216/) (2020)
- [Software architecture for YOLO, a creativity-stimulating robot](https://mlsanity.com/paper/1909.10823/) (2019)
- [Empirically Evaluating Creative Arc Negotiation for Improvisational Decision-making](https://mlsanity.com/paper/2106.02921/) (2021)
- [Human-Robot Co-Creativity: A Scoping Review -- Informing a Research Agenda for Human-Robot Co-Creativity with Older Adults](https://mlsanity.com/paper/2309.07033/) (2023)

## Built on by (most cited first, 4 of 4)

- [Towards Agentic Intelligence for Materials Science](https://mlsanity.com/paper/2602.00169/) (2026)
- [Accepted with Minor Revisions: Value of AI-Assisted Scientific Writing](https://mlsanity.com/paper/2511.12529/) (2025)
- [Closing the Expression Gap in LLM Instructions via Socratic Questioning](https://mlsanity.com/paper/2510.27410/) (2025)
- [No Pixel Left Behind: Filling Gaps in Anime Colorization](https://mlsanity.com/paper/2609.00800/) (2026)

## Abstract (arXiv)

The co creativity community is making significant progress in developing more sophisticated and tailored systems to support and enhance human creativity. Design considerations from prior work can serve as a valuable and efficient foundation for future systems. To support this effort, we conducted a systematic literature review of 62 papers on co-creative systems. These papers cover a diverse range of applications, including visual arts, design, and writing, where the AI acts not just as a tool but as an active collaborator in the creative process. From this review, we identified several key dimensions relevant to system design: phase of the creative process, creative task, proactive behavior of the system, user control, system embodiment, and AI model type. Our findings suggest that systems offering high user control lead to greater satisfaction, trust, and a stronger sense of ownership over creative outcomes. Furthermore, proactive systems, when adaptive and context sensitive, can enhance collaboration. We also extracted 24 design considerations, highlighting the value of encouraging users to externalize their thoughts and of increasing the system's social presence and transparency to foster trust. Despite recent advancements, important gaps remain, such as limited support for early creative phases like problem clarification, and challenges related to user adaptation to AI systems.