A Systematic Review of Human-AI Co-Creativity

2025-06-26

AI Summary

Despite growing interest in human-AI co-creativity, foundational design principles for AI as an active collaborator—particularly regarding user trust, satisfaction, and creative ownership—remain poorly understood, with critical gaps in early-stage support and user adaptation. Method: We systematically reviewed 62 empirical studies on human-AI co-creation, applying multi-dimensional coding and thematic analysis to derive actionable insights. Contribution/Results: We propose the first six-dimensional design framework—spanning creative phase, task type, proactivity level, user control, embodiment, and model type—and distill 24 reusable design guidelines. Results show that high user control combined with context-sensitive, adaptive proactivity significantly enhances trust, satisfaction, and perceived creative ownership. Key gaps identified include insufficient AI support during ideation-adjacent phases (e.g., problem framing) and persistent challenges in user adaptation to AI collaboration. This work provides empirically grounded, human-centered design guidance for building trustworthy next-generation co-creative AI systems.

Technology Category

• Humans and AI: User Experience and Usability
• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• Reviewing human-AI co-creativity systems for diverse applications
• Identifying key design dimensions for effective creative collaboration
• Addressing gaps in early creative phase support and user adaptation

Innovation

• Systematic review of 62 co-creative systems papers
• Identified key design dimensions for AI collaboration
• Highlighted 24 design considerations for user trust