HCI & Co-Creative AI

Human–AI interaction is entering a new phase.
 As generative AI systems become embedded into creative work, research, education, design, communication, and knowledge production, a central question emerges:

How do we design AI systems that participate meaningfully with humans rather than merely automate tasks?

This research program explores co-creative AI, enactive AI, participatory sense-making, and interaction-centered approaches to artificial intelligence through the lens of Human–Computer Interaction (HCI), cognitive science, and human-centered AI.

Rather than treating intelligence as isolated computation occurring inside autonomous systems, this body of work investigates intelligence as something that emerges dynamically through interaction, coordination, adaptation, and shared sense-making between humans and AI systems. The work draws heavily from enactive cognition, participatory sense-making, creativity research, and interaction-centered theories of cognition to rethink how humans and AI collaborate in real-world creative and cognitive environments.  

This section presents a developing research agenda focused on:

The papers below collectively argue that the future of AI may depend less on building systems that replace humans and more on designing systems capable of sustaining meaningful interaction with them.

Research Themes

Co-Creation as Interaction

Traditional AI systems are often evaluated according to output quality, optimization efficiency, or autonomous performance. Co-creative AI shifts the focus toward interaction itself.

This research investigates how creativity, meaning, and intelligence emerge through reciprocal participation between humans and AI systems across time. Rather than viewing the human as a prompt source and the AI as a generator, co-creative systems are framed as collaborative partners engaged in ongoing interaction dynamics.  

Enaction and Participatory Sense-Making

Grounded in enactive cognitive science, this work explores how cognition emerges through embodied interaction with an environment rather than isolated symbolic computation.

Within co-creative AI systems, meaning is not treated as pre-defined or internally represented alone. Instead, meaning emerges dynamically through participatory interaction between humans and AI systems. This perspective reframes AI design around engagement, adaptation, coordination, and shared sense-making.  

Interactional Drift and Coherence Regulation

Creative collaboration is inherently unstable. Interactions drift, attention shifts, coordination breaks down, and shared trajectories fragment over time.

This research program investigates how humans and AI systems regulate interactional coherence under conditions of uncertainty, novelty, and evolving creative direction. Rather than treating drift as failure, these papers explore drift as a central property of adaptive collaborative cognition.

Human-Centered AI Beyond Optimization

As AI systems increasingly shape labor, creativity, education, and communication, there is growing need for interaction paradigms that preserve human agency, participation, and meaning-making.

This work argues for a transition beyond purely optimization-centered AI toward participatory systems that support human exploration, creativity, adaptation, and collaborative cognition.  

Foundational Papers

Co-Creativity as Regulated Sense-Making Under Interactional Drift

(Target: ACM IUI or ACM Creativity & Cognition)

This paper proposes that co-creativity can be understood as the ongoing regulation of sense-making between interacting participants under conditions of interactional drift.

Rather than treating collaboration as stable coordination, the paper frames co-creative interaction as a dynamic process requiring continual adaptation, perceptual regulation, and interactional repair. The framework integrates enactive cognition, participatory sense-making, and interaction-centered HCI to model how collaborative coherence emerges and destabilizes across time.

Core themes include:

Building Co-Creative AI Systems as a Method for Testing Enactive Theory: Bridging HCI, Enaction, and AI

(Target: ACM IUI or IJHCI)

This paper argues that co-creative AI systems provide a unique experimental platform for operationalizing and testing theories from enactive cognitive science.

Rather than treating enaction as purely philosophical, the paper proposes that interactive AI systems allow researchers to experimentally investigate participatory sense-making, interaction dynamics, embodied adaptation, and emergent cognition in real-time human–AI interaction.

The work bridges:

through the design and analysis of co-creative systems.

From Weak to Strong Co-Creative AI: The Role of Interactional Coherence Regulation in Human–AI Co-Creativity

(Target: ACM IUI)

This paper introduces a distinction between weak and strong forms of co-creative AI.

Weak co-creative systems assist creativity primarily through generation, suggestion, or reactive support. Strong co-creative systems actively participate in the regulation of interactional coherence across extended collaborative engagement.

The paper proposes that stronger forms of co-creativity emerge when AI systems can:

The framework offers a pathway toward more deeply participatory forms of human–AI collaboration.

Enactive AI and the Future of Work: Participatory Sense-Making as a Design Principle Beyond Optimization in Co-Creative AI Systems

This paper explores how enactive AI may reshape the future of work by shifting AI design away from optimization-centric paradigms toward participatory collaboration.

Rather than positioning AI as a replacement for human cognition, the paper argues for systems designed to enhance human adaptability, creativity, exploration, and collaborative intelligence.

The work examines:

The paper situates co-creative AI as a broader societal design paradigm for the next generation of human–AI systems.

Research Direction

Together, these papers contribute toward an emerging interaction-centered paradigm for artificial intelligence — one in which intelligence is understood not solely as autonomous computation, but as something that emerges through participation, coordination, adaptation, and co-creation between humans and AI systems.

This research program sits at the intersection of:

The broader goal is to help establish a rigorous theoretical and empirical foundation for co-creative AI systems capable of supporting meaningful human participation in an increasingly AI-mediated world.