Co-Creative AI

Toward an Interaction-Centered Paradigm of Human-AI Co-Creation

Co-Creative AI is an interaction-centered paradigm of artificial intelligence focused on how humans and AI systems collaborate in shared processes of creativity, meaning construction, adaptive interaction, and participatory sense-making. This site documents more than a decade of interdisciplinary research by Nicholas Davis, PhD and collaborators spanning: co-creative AI, Creative Sense-Making, enactive AI, participatory sense-making, quantified co-creation, hybrid intelligence, and human-AI interaction. 

Beginning with early work in computational creativity and human-computer co-creativity, this research evolved into one of the earliest sustained research programs investigating AI systems as collaborative participants rather than isolated generators or passive tools. Early publications such as:

helped establish foundational concepts for modern co-creative AI systems. This work introduced frameworks and concepts including: artistic computer colleagues, participatory interaction, creative trajectories, quantified co-creation, sense-making curves, conceptual shifts, and interaction-centered models of intelligence.

The research collected here helped establish many of the foundational concepts underlying modern co-creative AI systems, including: artistic computer colleagues, creative trajectories, sense-making curves, participatory interaction, and interaction-centered models of intelligence.

Rather than treating AI as merely a tool or autonomous generator, this research explores AI systems as dynamically coupled participants engaged in shared creative and cognitive processes with humans. Within this perspective, intelligence is not viewed as isolated computation occurring solely within humans or machines. Instead, intelligence emerges dynamically through interaction between coupled participants engaged in shared processes of sense-making.

Toward Interaction-Centered AI

Traditional artificial intelligence has largely focused on: prediction, optimization, classification, automation, and autonomous generation. Co-Creative AI proposes a fundamentally different paradigm. Rather than treating cognition as isolated information processing, co-creative systems investigate how creativity, meaning, and adaptive intelligence emerge through interaction itself. This interaction-centered perspective draws from: enactive cognition, participatory sense-making, human-computer interaction, computational creativity, ecological psychology, adaptive systems research, and hybrid intelligence frameworks.

Over time, these ideas evolved into broader theoretical frameworks including: Creative Sense-Making, Enactive AI, quantified co-creation, and human-AI co-creation as a new interaction paradigm.

This research includes the development of some of the earliest co-creative AI systems designed to collaborate with humans in real time, including: The Drawing Apprentice and The AI Drawing Partner.  These systems were not designed simply to generate outputs autonomously. Instead, they were developed as: interactive collaborators, improvisational partners, and quantified co-creative systems capable of: modeling interaction dynamics, logging activity traces, visualizing collaboration patterns, generating conceptual shifts, and quantifying the co-creative process itself. 

A major contribution of this research has been the development of frameworks for quantifying co-creation in situ through:

These approaches helped establish a new subfield of co-creative AI research focused on understanding how meaning and creativity emerge dynamically during interaction between humans and AI systems. More recent work extends these frameworks into the era of generative AI and hybrid intelligence through:

Together, the theories, systems, and publications collected here document the emergence of an interaction-centered paradigm of artificial intelligence — one in which creativity and intelligence arise not from humans or machines alone, but through dynamic participation between them.

Why This Matters Now

The rise of generative AI has brought human-AI collaboration into mainstream creative practice. Yet many of the core challenges surrounding: interaction, explainability, authorship, participation, hybrid intelligence, and adaptive collaboration, were explored much earlier within co-creative AI research.

As AI systems become increasingly integrated into creative, scientific, and cognitive workflows, interaction-centered frameworks such as Creative Sense-Making, participatory sense-making, and Enactive AI may become increasingly important for understanding how humans and AI systems collaborate meaningfully together.