A Systematic Review of Human-AI Co-Creativity
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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.
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
17 of 132 references.
References · 17
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
- 2023A Comprehensive Overview of Large Language Modelscited by 648
- 2022Designing Creative AI Partners with COFI: A Framework for Modeling Interaction in Human-AI Co-Creative Systemscited by 59
- 2022Beyond Text Generation: Supporting Writers with Continuous Automatic Text Summariescited by 47
- 2023Choice Over Control: How Users Write with Large Language Models using Diegetic and Non-Diegetic Promptingcited by 36
- 2023Spellburst: A Node-based Interface for Exploratory Creative Coding with Natural Language Promptscited by 30
- 2020Collaborative Storytelling with Large-scale Neural Language Modelscited by 21
- 2018Co-Creative Level Design via Machine Learningcited by 18
- 2023Tweetorial Hooks: Generative AI Tools to Motivate Science on Social Mediacited by 11
- 2022A Collaborative, Interactive and Context-Aware Drawing Agent for Co-Creative Designcited by 10
- 2022Creative Wand: A System to Study Effects of Communications in Co-Creative Settingscited by 9
- 2023Lode Encoder: AI-constrained co-creativitycited by 6
5 more
- 2019Deep Learning in a Computational Model for Conceptual Shifts in a Co-Creative Design Systemcited by 5
- 2020Deep Learning of Individual Aestheticscited by 3
- 2019Software architecture for YOLO, a creativity-stimulating robotcited by 2
- 2021Empirically Evaluating Creative Arc Negotiation for Improvisational Decision-makingcited by 1
- 2023Human-Robot Co-Creativity: A Scoping Review -- Informing a Research Agenda for Human-Robot Co-Creativity with Older Adultscited by 1
Built on by
Citing papers per year: 2025: 2; 2026: 2.
4 papers.
- 2026Towards Agentic Intelligence for Materials Sciencecited by 3
- 2025Accepted with Minor Revisions: Value of AI-Assisted Scientific Writingcited by 1
- 2025Closing the Expression Gap in LLM Instructions via Socratic Questioningcited by 0
- 2026No Pixel Left Behind: Filling Gaps in Anime Colorizationcited by 0
- 2026No Pixel Left Behind: Filling Gaps in Anime Colorization1 Sep 2026
- 2026Towards Agentic Intelligence for Materials Science29 Jan 2026
- 2025Accepted with Minor Revisions: Value of AI-Assisted Scientific Writing16 Nov 2025
- 2025Closing the Expression Gap in LLM Instructions via Socratic Questioning31 Oct 2025