Collaboration Data

Human-Human Creative Collaboration

Collaborative groups often exhibit alternating periods of:

• discussion

• idea generation

• negotiation

• agreement

• implementation

These transitions create interaction trajectories that reveal how shared understanding develops through time.

Human-AI Co-Creation

Co-Creative Systems

Human-AI systems such as Drawing Apprentice and AI Drawing Partner provide unique opportunities for studying interaction dynamics.

In these environments:

• humans contribute ideas

• AI systems respond

• participants adapt

• trajectories emerge

The resulting curves reveal how novelty and meaning develop through reciprocal interaction.

Comparing Contexts

Although the domains differ, common patterns often emerge:

• oscillation between exploration and coordination

• transitions between interaction modes

• emergence of shared meaning

• stabilization of creative outcomes

These recurring patterns suggest that interaction dynamics may provide a general framework for understanding creativity across diverse contexts.

Toward Quantified Co-Creation

The examples presented here represent early steps toward a broader science of interaction.

By visualizing interaction dynamics, Sense-Making Curves make it possible to compare creative processes, identify recurring structures, and investigate how creativity emerges through participation.