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Machine learning has been applied to a number of creative, design-orientedtasks. However, it remains unclear how to best empower human users with thesemachine learning approaches, particularly those users without technicalexpertise. In this paper we propose a general framework for turn-basedinteraction between human users and AI agents designed to support humancreativity, called {co-creative systems}. The framework can be used to betterunderstand the space of possible designs of co-creative systems and revealfuture research directions. We demonstrate how to apply this framework inconjunction with a pair of recent human subject studies, comparing between thefour human-AI systems employed in these studies and generating hypothesestowards future studies.

An Interaction Framework for Studying Co-Creative AI