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.

Co-creation in embodied contexts is central to the human experience but is often lacking in our interactions with computers. We seek to develop a better understanding of embodied human co-creativity to inform the human-centered design of machines that can co-create with us. In this paper, we ask: What characterizes dancers’ experiences of embodied dyadic interaction in movement improvisation? To answer this, we ran focus groups with 24 university dance students and conducted a thematic analysis of their responses. We synthesize our findings in an Interconnected Model of Improvisational Dance Inputs, where movement choices are shaped by the interplay between in-the-moment influences between the self, partner, and the environment, a set of generative strategies, and heuristics for a successful collaboration. We present a set of design recommendations for LuminAI, a co-creative AI dance partner. Our contributions can inform the design of AI in embodied co-creative domains.

This paper introduces a new method for quantifying open-ended collaborative embodied improvisation: Observable Creative Sense-Making (OCSM). This technique builds on previous work on Creative Sense-Making (CSM), examines its shortcomings, and addresses it by reformalizing and grounding CSM in current literature from embodied social cognition and an intersubjective perspective of creativity. We apply this method to empirical studies of human collaboration in dance improvisation with 16 advanced college dancers and establish the method’s validity. The OCSM method described in this paper includes a qualitative coding technique, a web-based tool for coding the interaction, and the cognitive theory behind its application.