The publications below trace the development of co-creative AI, Creative Sense-Making, enactive AI, participatory interaction, quantified collaboration, and human-AI co-creation across more than a decade of interdisciplinary research conducted by Nicholas Davis and collaborators spanning computational creativity, cognitive science, human-computer interaction, and human-centered AI.
These works document the emergence of an interaction-centered paradigm for artificial intelligence — one in which creativity and intelligence arise through dynamic participation between humans and AI systems rather than isolated generation alone. Developed through collaborations with researchers across computational creativity, design, cognitive science, and AI, this body of work helped establish many of the foundational concepts now associated with co-creative AI and enactive approaches to human-AI interaction.
Many of these works were developed in collaboration with researchers at the Georgia Institute of Technology, the University of North Caroloina at Charlotte, and broader interdisciplinary communities within computational creativity, cognitive science, and human-centered AI.
This early period focused on distributed cognition, creativity support systems, perceptual interaction, and collaborative creativity. The work explored how creativity emerges through interaction between people, environments, tools, and evolving constraints rather than solely inside isolated individuals.
Awarded Best Student Paper at ACM Creativity & Cognition 2011, this work explored how cognition and creativity distribute across people, media systems, workflows, and collaborative environments during filmmaking. The paper helped establish an interaction-centered perspective on creativity.
Introduced early ideas surrounding perceptual logic and collaborative computational art systems, laying conceptual foundations for later work on perceptual attunement and interaction-centered creativity.
Explored how computational systems can scaffold creativity and lower barriers to creative participation for novice creators. This work emphasized creativity support over autonomous generation.
Proposed that creativity support systems should be grounded in cognitive theories of interaction and exploration rather than static productivity models alone. This work helped bridge creativity research and human-computer interaction.
One of the earliest formal articulations of co-creative AI. The paper proposed that computational systems could function as creative collaborators rather than merely tools or autonomous generators. This work helped establish the conceptual foundations of human-AI co-creation.
This era introduced enactive cognition into computational creativity research and proposed that AI systems should participate dynamically in creative interaction rather than generate outputs in isolation.
Presented at ICCC 2014, this paper introduced the enactive model of creativity and proposed the concept of “artistic computer colleagues.” The framework synthesized enactive cognition, ecological psychology, improvisation, and participatory interaction into a new paradigm for co-creative AI.
Extended the enactive framework formally into co-creative AI theory. This work proposed that creativity emerges through interaction between dynamically coupled participants rather than isolated symbolic computation alone. (Nick M. Davis)
One of the earliest true co-creative AI systems. The Drawing Apprentice collaborated with users in real time on a shared drawing canvas through reciprocal improvisational interaction. The system became both a co-creative drawing partner and a research platform for studying human-AI collaboration. (ACM Digital Library)
Applied enactive cognitive theory to pretend play and imaginative interaction, helping extend interaction-centered theories of cognition and creativity into collaborative play environments.
This period focused on how meaning emerges dynamically during human-AI collaboration through interaction, coordination, turn-taking, and improvisation.
One of the earliest empirical investigations of participatory sense-making in a human-AI creative system. The paper demonstrated that collaborative meaning-making can emerge dynamically through interaction between humans and AI systems. This work became foundational to interaction-centered co-creative AI research.
Extended the Drawing Apprentice with deep learning-based object recognition to support richer improvisational collaboration. The work explored dialogical interaction between human sketch input and AI interpretation in real time.
Investigated machine learning approaches for modeling embodied motion trajectories and dynamic interaction patterns, contributing to broader research on interaction-centered AI systems.
This phase introduced frameworks for quantifying co-creative interaction itself. Rather than evaluating only final artifacts, the research focused on activity traces, interaction dynamics, creative trajectories, and the emergence of meaning through collaboration.
Presented at ACM Creativity & Cognition 2017, this paper introduced Creative Sense-Making (CSM), a cognitive framework for modeling and quantifying co-creative interaction dynamics through time. Major contributions included:
activity traces,
creative trajectories,
and:
which modeled collaboration continuously during interaction itself.
The doctoral dissertation expanded the Creative Sense-Making framework into a comprehensive theory of quantified co-creation grounded in enactive cognition, participatory sense-making, and interaction dynamics.
Extended the Creative Sense-Making framework into practical quantified collaboration systems capable of modeling co-creative interaction dynamically through time.
Explored machine learning architectures capable of simultaneously classifying and generating collaborative creative responses within co-creative systems.
Introduced computational methods for generating conceptual shifts during co-creative drawing interaction using deep learning. The work explored how AI systems can intentionally provoke creative divergence and novelty. (arXiv)
Presented a foundational framework for evaluating co-creative systems by focusing on interaction dynamics, user experience, timing of evaluation, and collaborative emergence rather than isolated output quality alone. (arXiv)
Extended conceptual shift modeling into design creativity systems, exploring how AI systems can dynamically provoke more creative outcomes through adaptive collaborative interaction. (arXiv)
Recent work increasingly generalized these ideas into broader theories of interaction-centered AI, enactive AI, participatory intelligence, and human-AI co-creation.
Published in the Handbook of Human-Centered Artificial Intelligence, this chapter positioned human-AI co-creation as a fundamentally new interaction paradigm centered on participation, adaptation, collaboration, and shared meaning construction. The work synthesized more than a decade of research into interaction-centered AI systems.
Winner of the ICCC 2024 Best Paper Award. This work formalized enactive AI through five pillars:
autonomy,
sense-making,
embodiment,
emergence,
and experience.
The paper established enaction as a foundational framework for understanding co-creative AI systems and interaction-centered intelligence.
Extended the Drawing Apprentice lineage into a modern quantified co-creative AI research platform capable of modeling interaction dynamics, visualizing creative trajectories, and analyzing collaborative interaction using the Creative Sense-Making framework. (arXiv)
Published in Artificial Media: Emerging Trends in Narratives, Education and Creative Practice (Springer), this chapter extends earlier work on Creative Sense-Making, quantified co-creation, and interaction-centered AI into the era of generative artificial intelligence.
The work argues that modern generative AI systems often function as “black boxes” in which the contributions of both human creators and AI systems become obscured within the final artifact. Building upon earlier frameworks involving:
activity traces,
creative trajectories,
participatory interaction,
and quantified collaboration,
the chapter proposes explainable co-creative AI systems capable of revealing and modeling the evolving interaction dynamics underlying human-AI collaboration.
The publication represents an important continuation of earlier co-creative AI research into contemporary concerns involving:
explainability,
authorship,
transparency,
hybrid intelligence,
and human-centered generative AI systems.
Rather than treating AI creativity as isolated autonomous generation, the framework emphasizes:
creativity as an emergent process arising through interaction between humans and AI systems.
Across this body of work, several recurring themes emerge:
Creativity as interaction rather than isolated generation
Intelligence as participatory and relational
Co-creative AI as a collaborative paradigm
Enaction as a foundation for interaction-centered AI
Quantified co-creation through activity traces and creative trajectories
Participatory sense-making between humans and AI systems
Human-AI co-creation as a new interaction paradigm
Together, these works helped establish many of the conceptual foundations now shaping:
co-creative AI,
enactive AI,
human-centered AI,
hybrid intelligence,
participatory AI,
and interaction-centered models of artificial intelligence.