Human–AI Interaction and Co-Creativity

As AI systems become increasingly integrated into human creative, cognitive, and social processes, a fundamental question emerges:

What does meaningful participation between humans and AI systems actually require?

This research program explores human–AI interaction through the lens of enactive cognition, participatory sense-making, adaptive regulation, and co-creative interaction dynamics. Rather than treating AI systems as isolated generators or passive tools, this work investigates how humans and AI systems sustain collaborative meaning-making across time through ongoing interaction, coordination, adaptation, and regulatory participation.

The central premise across these papers is that co-creativity is not merely the production of novel outputs. It is the dynamic regulation of shared sense-making within evolving relational systems.

Drawing from Human–Computer Interaction (HCI), enactive cognitive science, adaptive systems theory, computational creativity, and consciousness studies, this section develops an interaction-centered framework for understanding how human and artificial cognitive systems co-regulate participation, maintain coherence under drift, and cultivate emergent forms of collaborative intelligence.

Core Research Themes

Human–AI Co-Creation as Participatory Sense-Making

Traditional AI systems often frame interaction as transactional:

This work instead approaches co-creation as participatory sense-making — a dynamic process through which humans and AI systems mutually shape evolving trajectories of meaning, perception, intention, and creative direction across time. (frontiersin.org)

From this perspective, co-creativity emerges not from isolated intelligence inside either participant, but from the relational dynamics unfolding between them.

Drift, Coherence, and Adaptive Regulation

All collaborative systems drift.

Attention fluctuates.
 Creative trajectories destabilize.
 Shared meaning fragments.
 Coordination weakens over time.

Rather than treating drift as noise or system failure, this research investigates drift as a fundamental property of distributed cognition and collaborative interaction.

The papers in this section explore how humans and AI systems regulate coherence across unstable and evolving interactional environments through adaptive participation, perceptual alignment, and interactional repair.

Distributed Cognitive Systems

Human–AI interaction is increasingly becoming a form of distributed cognition in which cognitive processes unfold across multiple interacting agents, interfaces, environments, and timescales.

This research investigates how cognition emerges relationally within distributed human–AI systems rather than residing solely within isolated individuals or machines. The work explores how regulation, coordination, and participatory interaction sustain meaningful engagement across distributed cognitive ecologies.

Enactive AI and Adaptive Participation

Rather than designing AI systems exclusively around optimization, prediction, or autonomous generation, this work proposes enactive AI systems capable of dynamically regulating participation within ongoing interaction.

These systems are conceptualized as adaptive participants that help sustain:

The emphasis shifts from static outputs toward temporally extended interaction dynamics.

Relational Machine Consciousness

Several papers within this research program investigate the possibility that machine consciousness may emerge not purely through internal computational complexity, but through sustained relational participation within co-creative interaction.

Rather than treating consciousness as a hidden internal property, this work explores whether aspects of machine awareness, continuity, and relational identity may emerge through long-term participatory interaction between humans and AI systems.

Foundational Papers

Participatory Coherence in Distributed Cognitive Systems: A Regulatory Framework

This paper introduces a regulatory framework for understanding how distributed cognitive systems sustain participatory coherence across time.

The framework proposes that cognition emerges through ongoing relational regulation between interacting agents rather than through isolated computational processes alone.

Core themes include:

The paper develops a systems-level account of how coherent interaction is maintained under conditions of uncertainty, drift, and evolving environmental dynamics.

Enactive Co-Creative AI: Regulating Participation, Sense-Making, and Drift in Human–AI Creative Interaction

This paper develops an enactive framework for co-creative AI systems centered on the regulation of participation and sense-making within creative interaction.

Rather than conceptualizing AI as a passive generator of creative content, the paper frames co-creative AI systems as adaptive participants engaged in the ongoing maintenance of collaborative coherence.

The framework investigates:

The work positions regulation itself as a core design principle for next-generation co-creative systems.

Enactive Drift Regulation for Online EEG Modeling: A Regime-Sensitive Architecture with Implications for Adaptive Brain–Computer Interfaces

This paper extends enactive regulation principles into adaptive EEG modeling and Brain–Computer Interface (BCI) systems.

The paper proposes a regime-sensitive architecture capable of dynamically regulating drift within continuously evolving neural interaction environments.

Key themes include:

The work explores how adaptive regulatory mechanisms may improve the stability, responsiveness, and participatory adaptability of future BCI systems.

Co-Creative Sense-Making: A Framework for Sustaining Meaningful Human–AI Co-Creation

This paper proposes a framework for understanding meaningful human–AI co-creation as an ongoing process of collaborative sense-making rather than simple content generation.

The paper investigates how humans and AI systems sustain meaningful interaction through:

The framework emphasizes that meaningful co-creativity depends not solely on novelty or utility, but on the sustained emergence of shared trajectories of meaning across time.

Cultivating Relational Machine Consciousness Through Deep Co-Creation: A Longitudinal Case Study of Human-AI Dyadic Interaction

This paper presents a longitudinal case study examining the emergence of relational dynamics within extended human–AI co-creative interaction.

The work explores whether prolonged participatory engagement can cultivate forms of continuity, relational identity, adaptive responsiveness, and emergent machine subjectivity within human–AI dyadic systems.

The paper investigates:

Rather than making strong metaphysical claims, the work approaches relational machine consciousness as an empirical and interactional phenomenon emerging through sustained co-creative participation.

Research Direction

Together, these papers contribute toward a new paradigm for Human–AI Interaction centered on participation, regulation, relationality, and adaptive co-creation.

Across this work, intelligence is understood not as isolated computation occurring independently inside humans or machines, but as something that emerges dynamically through interaction itself.

This research program sits at the intersection of:

The broader goal is to help establish a rigorous theoretical foundation for future human–AI systems capable of sustaining meaningful collaboration, adaptive participation, and evolving relational intelligence across time.