Creative Sense-Making:
Quantifying Interaction Dynamics in Co-Creation (Davis et al.)
How can we measure what happens during open-ended creative collaboration?
Creative Sense-Making: Quantifying Interaction Dynamics in Co-Creation introduces a framework for studying creativity not only through final artifacts, but through the interaction dynamics that unfold while people — and co-creative systems — collaborate.
Published at ACM Creativity & Cognition 2017, the paper presents Creative Sense-Making as a cognitive framework and coding method for quantifying interaction dynamics during open-ended co-creation, including collaborative drawing and pretend play.
Overview
Creative collaboration is dynamic, improvisational, and difficult to measure.
Participants shift between acting, pausing, communicating, exploring, responding, and making sense of one another’s contributions. In open-ended co-creation, the most important creative dynamics often occur not only in the finished artifact, but in the evolving rhythm of interaction itself.
This paper addresses that challenge by proposing a method for quantifying how co-creative interaction unfolds through time.
Rather than asking only:
What was created?
Creative Sense-Making asks:
How did the creative interaction develop?
The framework was applied across two domains: human collaboration in pretend play and AI-supported collaborative drawing. This cross-domain application was used to establish validity and inter-rater reliability within each domain.
The Creative Sense-Making Framework
Creative Sense-Making synthesizes cognitive science theories and empirical work on open-ended improvisation into a practical method for analyzing co-creative interaction.
The framework includes:
a cognitive theory of co-creative sense-making,
a qualitative interaction coding technique,
interaction coding software,
and a method for visualizing interaction dynamics through time.
Its central purpose is to make the hidden dynamics of co-creation more visible, analyzable, and comparable across different creative contexts.
Clamped and Unclamped Cognition
A core idea in the framework is that creative participants move between different cognitive and interactional states.
When participants are fluidly engaged in action, they may be in a more clamped state: acting with confidence, continuity, and task-directed flow.
When the situation becomes uncertain, surprising, ambiguous, or in need of reinterpretation, cognition may unclamp: the participant shifts into reflection, communication, exploration, or reorientation.
This movement between clamped and unclamped cognition provides a way to describe how collaborators regulate creative activity over time.
Quantifying Interaction Dynamics
The paper proposes that co-creative interaction can be coded continuously through time using observable behavioral markers.
These coded interaction states can then be visualized as a sense-making curve, allowing researchers to examine:
interaction rhythm,
turn-taking patterns,
periods of action and reflection,
communication dynamics,
disengagement or waiting,
and shifts in collaborative strategy.
This creates a bridge between qualitative observation and quantitative analysis.
Instead of treating co-creation as too open-ended to measure, Creative Sense-Making provides a method for capturing the temporal structure of collaboration.
Domains of Application
The framework was tested in two different forms of co-creation:
Pretend Play
The human collaboration study examined open-ended pretend play as an improvisational domain where participants continuously negotiate shared meaning, roles, and imagined situations.
This domain demonstrated how Creative Sense-Making can be used to study human collaborative creativity.
Collaborative Drawing
The AI-based study applied the framework to collaborative drawing, connecting Creative Sense-Making to earlier work on co-creative drawing agents and human–AI collaboration.
This domain demonstrated how the framework can be used to evaluate interaction dynamics in co-creative AI systems.
Why This Paper Matters
This paper is important because it helped shift evaluation in co-creative systems away from only measuring final products or user impressions.
It offered a way to study:
how collaboration unfolds,
how interaction shapes creativity,
how participants regulate uncertainty,
and how creative meaning emerges dynamically over time.
The work provides an early foundation for later research on:
Co-Creative Sense-Making,
quantified co-creative AI systems,
AI Drawing Partner,
participatory coherence,
interactional drift,
and enactive co-creative AI.
Relationship to Later Research
Creative Sense-Making became a foundational step toward later frameworks that model and quantify human–AI co-creation more directly.
The later AI Drawing Partner and quantified co-creative AI systems extend this trajectory by automatically modeling co-creative interaction, visualizing collaboration dynamics, and using interaction data to explain how artificial media is produced.
Observable Creative Sense-Making also builds on this earlier work by extending Creative Sense-Making into embodied improvisational co-creative interaction.
Research Significance
Creative Sense-Making establishes a key methodological foundation for studying creativity as an interactional process.
Its central contribution is the idea that creative collaboration can be understood, coded, visualized, and analyzed as a dynamic process of sense-making through time.
This makes the paper a core foundation for the broader Co-Creative AI research program, especially the movement toward interaction-centered, quantified, explainable, and enactive approaches to human–AI co-creation.