A Systematic Review of Human-AI Co-Creativity
This review synthesizes insights for designing co-creative AI systems to support human creativity in domains like visual arts and writing, but it is incremental as it builds on prior work without introducing new methods.
The paper conducted a systematic review of 62 papers on human-AI co-creative systems, identifying key design dimensions and 24 considerations, with findings showing that high user control leads to greater satisfaction and trust, and proactive systems enhance collaboration when adaptive.
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.