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An Interaction Framework for Studying Co-Creative AI
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An Interaction Framework for Studying Co-Creative AI
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Machine learning has been applied to a number of creative, design-oriented tasks. However, it remains unclear how to best empower human users with these machine learning approaches, particularly those users without technical expertise. In this paper we propose a general framework for turn-based interaction between human users and AI agents designed to support human creativity, called {co-creative systems}. The framework can be used to better understand the space of possible designs of co-creative systems and reveal future research directions. We demonstrate how to apply this framework in conjunction with a pair of recent human subject studies, comparing between the four human-AI systems employed in these studies and generating hypotheses towards future studies.
Forward citations
Cited by 3 Pith papers
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Creo: From One-Shot Image Generation to Progressive, Co-Creative Ideation
Creo scaffolds text-to-image generation through progressive stages with editable abstractions and decision locking to improve controllability, agency, and output diversity.
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Interaction-Centered Intelligence: Toward an Interaction-Based Theory of Human-AI Co-Creation
Proposes Interaction-Centered Intelligence as a framework where intelligence emerges from interaction dynamics rather than internal agent computation.
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Cognitive Trajectory Modeling: Quantifying Human-AI Co-Creation through Cognitively Grounded Interaction Trajectories
Cognitive Trajectory Modeling offers a new conceptual framework for representing co-creative interaction dynamics as temporally organized trajectories in attractor landscapes, generalizing concepts from the Enactive M...
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