Nicholas Davis, PhD

Co-Creative AI, Participatory Cognition, Human-Centered AI,  Creative Technologies

Human–AI interaction and co-creative systems specialist grounded in enactive cognition and adaptive regulation.

Nicholas Davis, PhD (ndavis35@gatech.edu) is a researcher, theorist, and designer working at the intersection of artificial intelligence, cognitive science, computational creativity, human-computer interaction, and enactive cognition. His work focuses on how intelligence, creativity, and meaning emerge dynamically through interaction between humans and AI systems. 

Over more than a decade of interdisciplinary research, Davis helped pioneer many of the foundational concepts underlying modern co-creative AI and interaction-centered approaches to artificial intelligence. His research introduced frameworks and systems involving artistic computer colleagues, participatory sense-making, quantified co-creation, creative trajectories, sense-making curves, enactive AI, and human-AI co-creation as an interaction paradigm.

Much of this work explored a central idea that continues shaping the field today: intelligence may not reside solely inside isolated humans or machines, but may instead emerge dynamically through interaction between coupled participants engaged in shared processes of sense-making.

Research Focus

Nicholas Davis’s research spans several interconnected areas within artificial intelligence, cognitive science, and computational creativity, including: Co-Creative AI, Human-AI Co-Creation, Enactive AI, Creative Sense-Making, Participatory Sense-Making, Computational Creativity, Human-Computer Interaction, Adaptive Systems, Hybrid Intelligence, Interaction-Centered AI, Quantified Co-Creation, Explainable Creative Systems, Creativity Support Tools, and Interactive Machine Learning.

His work combines theoretical research with the development of interactive AI systems designed to collaborate dynamically with humans in creative and cognitive tasks.

Rather than treating AI as merely a tool or autonomous generator, Davis’s research explores AI systems as adaptive participants capable of engaging in improvisational interaction, shared meaning construction, and collaborative creativity.

Research Timeline and Historical Development

Nicholas Davis began publishing research on human-computer co-creativity during the early development of computational creativity and interactive AI systems. Early work focused on: human-computer co-creativity, improvisational creative systems, pretend play, collaborative drawing, and interaction dynamics in creative cognition. This research gradually evolved into broader frameworks involving: enactive cognition, participatory interaction, Creative Sense-Making, quantified co-creation, and interaction-centered models of intelligence.

Key milestones in this trajectory include:

2013 — Human-Computer Co-Creativity

Early work exploring how humans and computational systems collaborate creatively through interaction rather than isolated generation.

2014 — Enactive AI and Artistic Computer Colleagues

Introduction of the enactive model of creativity and the concept of artistic computer colleagues through research presented at the International Conference on Computational Creativity.

2015–2017 — The Drawing Apprentice and Participatory Sense-Making

Development of The Drawing Apprentice, one of the earliest co-creative AI drawing systems designed for improvisational collaboration with human users in real time. This work led to empirical studies on: participatory sense-making, quantified co-creation, creative trajectories, and interaction dynamics during collaboration.

2017 — Creative Sense-Making and Quantified Co-Creation

Development of frameworks for quantifying interaction dynamics in co-creation through activity traces, sense-making curves, and creative trajectories.

2020s — Human-AI Co-Creation and Interaction-Centered AI

Expansion of earlier co-creative AI frameworks into broader theories involving: hybrid intelligence, explainable co-creative systems, generative AI collaboration, interaction-centered intelligence, and human-AI co-creation as a new paradigm for AI interaction.

Georgia Tech and Academic Background

Nicholas Davis earned a PhD in Human-Centered Computing from the Georgia Institute of Technology, where his research focused on co-creative AI, computational creativity, enactive cognition, adaptive systems, and human-AI interaction. His work emerged from an interdisciplinary synthesis of: cognitive science, artificial intelligence, human-computer interaction, creativity research, ecological psychology, enactive cognition, and computational creativity. During this period, Davis developed many of the early theoretical foundations and co-creative systems that later contributed to modern interaction-centered approaches to AI. His research has been published in venues spanning: computational creativity, human-computer interaction, human-centered AI, artificial intelligence, cognitive science, and creativity research.

Major Contributions and Frameworks

Co-Creative AI

Nicholas Davis’s work helped establish some of the earliest interaction-centered frameworks for co-creative artificial intelligence, a field focused on designing AI systems that collaborate with humans during creative processes rather than operating as isolated autonomous generators. This research reframed AI systems from passive tools or independent creators into active collaborative participants capable of improvisation, adaptation, feedback exchange, and participatory interaction with users. The work contributed to the broader emergence of co-creative AI as a distinct research area within computational creativity, human-computer interaction, and human-centered AI.

Artistic Computer Colleagues

One of the earliest conceptualizations of AI systems as improvisational creative collaborators. Developed through research on enactive cognition and computational creativity, the concept of “artistic computer colleagues” proposed that AI systems could participate dynamically within creative interaction rather than simply generate isolated outputs. These systems were envisioned as collaborative partners capable of reciprocal engagement, turn-taking, adaptation, and shared creative exploration with human users. This framework helped shift computational creativity research toward interaction-centered models of collaboration and co-creation.

Enactive AI

An interaction-centered framework grounded in enactive cognitive science in which intelligence emerges dynamically through participation between agents and environments. Rather than treating cognition as isolated symbolic computation, Enactive AI emphasizes: perception-action coupling, adaptive interaction, participatory sense-making, embodiment, emergence, and relational intelligence.

This work helped extend enactive cognition into co-creative AI and broader theories of interaction-centered intelligence, arguing that intelligence unfolds through ongoing interaction and adaptive regulation rather than static internal representation alone. The framework was further formalized in The Five Pillars of Enaction as a Theoretical Framework for Co-Creative Artificial Intelligence, which received the ICCC 2024 Best Paper Award.

Creative Sense-Making

Creative Sense-Making (CSM) is a cognitive framework developed to model and quantify how creativity and meaning emerge dynamically through interaction during co-creation. Introduced at ACM Creativity & Cognition 2017 and later expanded into a doctoral dissertation at Georgia Institute of Technology, the framework proposed that creativity should be studied not only through final artifacts, but through evolving interaction dynamics unfolding through time. Major concepts introduced through the framework include:

• activity traces,

• creative trajectories,

• interaction trends,

• and sense-making curves.

The framework became foundational for later work involving quantified co-creation, explainable co-creative AI, and interaction-centered intelligence. 

Quantified Co-Creation

Quantified Co-Creation extended Creative Sense-Making into practical computational methods for modeling collaborative dynamics in human-AI interaction. Rather than evaluating creativity only through completed outputs, this work focused on capturing and analyzing interaction histories, activity traces, turn-taking behavior, collaboration rhythms, conceptual shifts, and evolving creative trajectories. The framework introduced new approaches for visualizing and quantifying co-creative interaction dynamics during collaboration itself, helping establish interaction-centered evaluation methods for co-creative AI systems. 

Participatory Sense-Making in AI Systems

Nicholas Davis’s research helped introduce participatory sense-making from enactive cognitive science into co-creative AI research. This work explored how humans and AI systems dynamically construct meaning together through interaction, coordination, improvisation, and collaborative engagement. One of the earliest empirical investigations of participatory sense-making in human-AI collaboration was conducted through the Drawing Apprentice system, demonstrating that meaning and creativity can emerge relationally during interaction itself rather than existing solely inside isolated participants. This work helped establish interaction dynamics as a central phenomenon within co-creative AI research.

The Drawing Apprentice

The Drawing Apprentice was one of the earliest co-creative AI systems designed for real-time improvisational drawing collaboration between humans and AI agents. Developed at Georgia Institute of Technology, the system collaborated with users on a shared drawing canvas by analyzing human sketches and generating adaptive visual responses in real time. Unlike traditional generative systems that operate independently, the Drawing Apprentice emphasized reciprocal interaction, turn-taking, collaborative improvisation, and creative dialogue between human and AI participants. The system became both a co-creative drawing partner and an experimental research platform for studying interaction dynamics, participatory sense-making, quantified co-creation, and human-AI collaboration.

Human-AI Co-Creation as an Interaction Paradigm

Recent work increasingly generalized co-creative AI into broader frameworks involving hybrid intelligence, explainable co-creative systems, interaction-centered AI, and participatory intelligence. Published work in The Handbook of Human-Centered Artificial Intelligence positioned human-AI co-creation as a fundamentally new interaction paradigm centered on participation, adaptation, collaboration, and shared meaning construction. More recent frameworks such as the Co-Creative Design Framework (CCDF) further formalized dimensions of:

• agency,

• interaction dynamics,

• communication,

• and collaborative adaptation

within hybrid intelligence systems. This work increasingly argues that intelligence, creativity, and meaning emerge through interaction itself, positioning co-creative systems as important research platforms for studying interaction-centered intelligence more broadly. 

Industry and Research Experience

Nicholas Davis’s work spans both academic research and applied AI system design.

His research has involved: co-creative AI systems, adaptive interaction frameworks, explainable AI, quantified creativity systems, generative collaboration, and human-centered artificial intelligence.

He has developed interactive AI systems capable of: improvisational collaboration, adaptive participation, creative interaction, interaction trace analysis, and dynamic co-creative engagement.

More recently, his work has expanded into broader investigations involving: hybrid intelligence, interaction-centered AI, creative cognition, adaptive systems, and emergent models of human-AI collaboration.

Publications

The publications and theoretical foundations collected throughout this site document more than a decade of interdisciplinary research spanning co-creative AI, enactive cognition, computational creativity, participatory sense-making, quantified co-creation, and human-AI interaction.

Together, these works trace the development of an interaction-centered paradigm of artificial intelligence in which intelligence, creativity, and meaning emerge dynamically through participation between humans and AI systems.

Publications

Research Timeline

Contact

For research inquiries, collaborations, speaking engagements, interviews, or academic discussion related to: co-creative AI, enactive AI, Creative Sense-Making, human-AI co-creation, quantified co-creation, or interaction-centered AI,

Contact Nicholas Davis, PhD: ndavis35@gatech.edu

Website: https://www.co-creativeai.com

Professional Profile: https://www.nickmdavis.com

Publication Timeline

Book Chapters

Davis, N., Clemens, M., Rezwana, J., & Browne, E. (2025). Human-AI Co-Creation: A New Interaction Paradigm for Human-AI Interaction. In Handbook of Human-Centered Artificial Intelligence (pp. 1-57). Singapore: Springer Nature Singapore.

Davis, N., Clemens, M., Browne, E., & Rezwana, J. (2025). Unlocking the Black Box of artificial media with quantified and explainable co-creative AI systems. In Artificial Media: Emerging Trends in Narratives, Education and Creative Practice (pp. 21-48). Cham: Springer Nature Switzerland.

Davis, N. 2024. Creative Sense-Making: A Cognitive Framework for Modeling Interaction Dynamics in Co-Creative AI. In AI, Co-Creativity, and Creativity. Routledge. 

Davis, N., Hsiao, C.-P., Popova, Y., & Magerko, B. 2015. An Enactive Model of Creativity for Computational Collaboration and Co-creation. In Creativity in the Digital Age (pp. 109–133). Springer.

Journal Articles

Nitsche, M., Riedl, M., Davis, N. Creativity, Cognition, and Machinima. Animation Journal, 27. 50-66.

Davis, N. and Do, E.Y.-L. Understanding Artistic Creativity with Perceptive Sketching Tools. In Journal of Communication and Society, Special Issue on Creative Technologies.

Davis, N., Hsiao, C.-P., Singh, K.Y, Lin, B., Magerko, B. Quantifying Collaboration with a Co-Creative Drawing Agent. ACM TiiS Special Issue on Highlights from IUI ’16.

Conference Presentations with Proceedings (refereed and archived)

2011

Davis, N., Li, B., O’Neill, B., Riedl, M., Nitsche, M. Distributed Cognition in Digital Filmmaking. In Proceedings of the 8th ACM Conference on Creativity and Cognition, Atlanta, GA, USA, November 3-6, 2011, pp. 207-216. [*Won Best Student Paper Award*]

Davis, N, Gupta, P., Gupta, S. and Do, E. Y.-L. Computing Harmony With PerLogicArt: Perceptual Logic Inspired Collaborative Art. In Proceedings of the 8th ACM Conference on Creativity and Cognition, Atlanta, GA, USA, November 3-6, 2011, pp. 185-194.

2012

Boyang, L., Zook, A., Davis, N., Riedl, M. Goal-driven Conceptual Blending: A Computational Approach for Creativity. Proceedings of International Conference on Computational Creativity 2012, Dublin, Ireland, 2012.

Hsiao, C.-P, Davis, N., Do, E. Y.-L. Dancing on the Desktop – Gesture Modeling System to Augment Design Cognition. To appear in Proceedings for ACADIA, Synthetic Digital Ecologies, San Francisco, CA, USA, October 18-21.

2013

Davis, N, Zook, A., Riedl, M., Kirschner, F., & Nitsche, M. Evaluating Novice-Oriented Creativity Support Tools. In Proceedings for CHI 2013 Workshop on Evaluating Creativity Support Environments.

Hsiao, C.-P., Davis, N., Do, E. Y.-L. Sketch Master – A Sketch Game for Collecting Exploratory Data. In proceedings of ACM Creativity & Cognition 2013.

Davis, N., Zook, A., O’Neill, B., Headrick, B., Riedl, M., Grosz, A., & Nitsche, M. 2013. Creativity support for novice digital filmmaking. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 651–660).

Davis, N., Winnemöller, H., Dontcheva, M., & Do, E. Y.-L. 2013. Toward a cognitive theory of creativity support. In Proceedings of the 9th ACM Conference on Creativity & Cognition (pp. 13–22).

Sysoev, I., Chitloor, R. D., Rajaram, A., Summerlin, R. S., Davis, N., & Walker, B. N. 2013. Middie mercury: an ambient music generator for relaxation. In Proceedings of the 8th Audio Mostly Conference (p. 20).

Davis, N. 2013. Human-Computer Co-Creativity: Blending Human and Computational Creativity. In Ninth Artificial Intelligence and Interactive Digital Entertainment Conference AIIDE ‘13.

2014

Davis, N., Popova, Y., Sysoev, I., Hsiao, C.-P., Zhang, D., & Magerko, B. 2014. Building Artistic Computer Colleagues with an Enactive Model of Creativity. In International Conference on Computational Creativity. Ljubljana, Slovenia: AAAI.

2015

Swarts, M., Davis, N., Hsiao, C.-P., & Hallam, J. 2015. Sharing the lights: exploration on teaching electronics for sensory augmentation development. In Proceedings of the 6th Augmented Human International Conference (pp. 203–204).

Davis, N., Comerford, M., Jacob, M., Hsiao, C.-P., & Magerko, B. 2015. An Enactive Characterization of Pretend Play. In Proceedings of the 2015 ACM SIGCHI Conference on Creativity and Cognition (pp. 275–284).

Davis, N., Hsiao, C.-P., Singh, K. Y., Li, L., Moningi, S., & Magerko, B. 2015. Drawing Apprentice: An Enactive Co-Creative Agent for Artistic Collaboration. In Proceedings of the 2015 ACM Creativity & Cognition Conference (pp. 185–186).

2016

Davis, N., Hsiao, C.-Pi., Yashraj Singh, K., Li, L., & Magerko, B. 2016. Empirically Studying Participatory Sense-Making in Abstract Drawing with a Co-Creative Cognitive Agent. In Proceedings of the 21st International Conference on Intelligent User Interfaces (pp. 196–207).

Li, R., Wang, Y., Hsiao, C.-P., Davis, N., Hallam, J., & Do, E. 2016. Tactile Teacher: Enhancing Traditional Piano Lessons with Tactile Instructions. In Proceedings of the 19th ACM Conference on Computer Supported Cooperative Work and Social Computing Companion (pp. 329–332).

Davis, N., Hsiao, C.-P., Singh, K.Y., Magerko, B. 2016. Co-Creative Drawing Agent with Object Recognition. In Proceedings of the 12th Conference on Artificial Intelligence and Interactive Digital Entertainment.

Singh, K.Y., Davis, N., Hsiao, C.-P., Jacob, M., Patel, K., Magerko, B. 2016. Recognizing Actions in Motion Trajectories using Deep Neural Networks. In Proceedings of the 12th Conference on Artificial Intelligence and Interactive Digital Entertainment.

2017

Davis, N., Singh, K.Y., Hsiao, C.-P., Lin, B. Magerko, B. Quantifying Interaction Dynamics in Co-Creation. Accepted to ACM Creativity & Cognition ’17.

Long, D., Jacob, M., Davis, N., Magerko, B. Designing for Socially Interactive Systems. Accepted to ACM Creativity & Cognition ’17.

Singh, K.Y., Davis, N., Hsiao, C.-P., Macias,R. Lin, B., Magerko, B. 2017. Unified Classification and Generation Networks for Co-Creative Systems. Submitted to International Conference of Computational Creativity (ICCC ‘17).

2018

Karimi, P., Davis, N., Grace, K., & Maher, M. L. (2018). Deep learning for identifying potential conceptual shifts for co-creative drawing. arXiv preprint arXiv:1801.00723.

Karimi, P., Grace, K., Davis, N., & Maher, M. L. (2018, July). Creative sketching apprentice: Supporting conceptual shifts in sketch ideation. In International Conference on-Design Computing and Cognition (pp. 721-738). Springer, Cham.

Karimi, P., Grace, K., Maher, M. L., & Davis, N. (2018). Evaluating creativity in computational co-creative systems. arXiv preprint arXiv:1807.09886.

Grace, K., Maher, M. L., Davis, N., & Eltayeby, O. (2018). Surprise walks: Encouraging users towards novel concepts with sequential suggestions. In Proceedings of the 9th International Conference on Computational Creativity (ICCC 2018). Association for Computational Creativity.

Mahmood, T., Butler, E., Davis, N., Huang, J., & Lu, A. (2018). Building multiple coordinated spaces for effective immersive analytics through distributed cognition. In 2018 International Symposium on Big Data Visual and Immersive Analytics (BDVA) (pp. 1–11). IEEE.

Karimi, P., Maher, M. L., Grace, K., & Davis, N. (2018). A computational model for visual conceptual blends. IBM Journal of Research and Development, 63(1), 5:1–5:11.

2019

Davis, N., Siddiqui, S., Karimi, P., Maher, M. L., & Grace, K. (2019). Creative Sketching Partner: A Co-Creative Sketching Tool to Inspire Design Creativity. In ICCC (pp. 358-359).

Karimi, P., Davis, N., Maher, M. L., Grace, K., & Lee, L. (2019). Relating cognitive models of design creativity to the similarity of sketches generated by an AI partner. In Proceedings of the 2019 Conference on Creativity and Cognition (pp. 259–270).

Karimi, P., Maher, M. L., Davis, N., & Grace, K. (2019). Deep learning in a computational model for conceptual shifts in a co-creative design system. arXiv preprint arXiv:1906.10188.

2020

Shaik, S. Z., Srinivasan, V., Peng, Y., Lee, M., & Davis, N. (2020). Co-creative robotic arm for differently-abled kids: Speech, sketch inputs and external feedbacks for multiple drawings. In Proceedings of the Future Technologies Conference (pp. 998–1007). Springer.

2024

Davis, N., Deshpande, M., Rezwana, J., & Magerko, B. (2024). The Five Pillars of Enaction as a Theoretical Framework for Co-Creative Artificial Intelligence. In ICCC (pp. 136-145).

2025

Davis, N., Sherson, J., & Rafner, J. (2025, June). The co-creative design framework for hybrid intelligence. In Proceedings of the 2025 Conference on Creativity and Cognition (pp. 560-572).