An Interaction Framework for Studying Co-Creative AI
This work addresses the challenge of designing effective AI systems for human creativity, particularly for users without technical expertise, but it is incremental as it builds on existing studies and frameworks.
The paper tackles the problem of empowering non-technical users with machine learning in creative tasks by proposing a general framework for turn-based interaction in co-creative systems, and demonstrates its application through comparisons of existing human-AI systems to generate future research hypotheses.
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.