Perceptions of Interaction Dynamics in Co-Creative AI: A Comparative Study of Interaction Modalities in Drawcto
Manoj Deshpande, Jisu Park, Supratim Pait, Brian Magerko
Abstract
This paper explores how different interaction modalities with AI agents affect human perception of the co-creative process. We utilize the Wizard of Oz methodology within Drawcto, a co-creative drawing system, to examine co-creativity across three scenarios: human-human, human-robot, and human-software interactions. Using a mixed-methods approach, we combined insights using the Observable Creative Sensemaking (OCSM) method with data from structured interviews. The study involved 20 participants engaging in a collaborative drawing task under each interaction scenario. Key findings reveal an average OCSM curve indicative of typical human-human interactions, varied themes in human-AI collaboration, and a notable influence of AI embodiment on participant perceptions, with the robot interface resembling human-human collaboration more closely than the software interface. Overall, this research offers valuable insights into how different interaction modalities influence the perceived role of AI in the co-creative process and provides design considerations for enhancing human-AI co-creative interactions.