  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.
