Enactive AI Future Research Initiatives and Funding Directions

1. Drift-Aware Artificial Intelligence

Adaptive Decision Systems for Dynamic Environments

This project develops AI architectures capable of maintaining effective behavior in environments characterized by structural change. Rather than relying on static models, the research investigates systems that detect structural drift and reorganize perception, interpretation, and action in real time. Applications include financial systems, organizational decision infrastructures, and distributed intelligent systems.

Potential venues:
 • NSF – Human-Centered AI
 • DARPA – Adaptive AI
 • NSF – Cyber-Physical Systems

2. Human–AI Participatory Intelligence

Modeling and Designing Systems for Collaborative Sense-Making

This project investigates how humans and AI systems coordinate behavior in shared environments. Drawing on enactive cognitive science and participatory sense-making theory, the research develops models of interaction dynamics, adaptive turn-taking, and collaborative decision processes. The goal is to design AI systems that support human reasoning rather than replace it.

Potential venues:
 • NSF – Human–AI Collaboration
 • NSF – Future of Work
 • DARPA – Human–Machine Symbiosis

3. Co-Creative Artificial Intelligence

Interactive Systems for Adaptive Creativity and Exploration

This project studies how AI systems can participate in creative processes with humans through real-time interaction. Using platforms such as co-creative drawing systems, the research investigates how adaptive AI agents regulate engagement, coordinate creative trajectories, and support human exploration. The work contributes to computational creativity, human–AI interaction, and design research.

Potential venues:
 • NSF – CreativeIT
 • NSF – Human-Centered Computing
 • NEA / interdisciplinary programs

4. Enactive AI Architectures

Designing Adaptive Cognitive Systems Inspired by Enaction

This project translates principles from enactive cognitive science into operational AI architectures. Rather than treating intelligence as static optimization, the research develops systems that regulate perception–action loops, detect environmental change, and maintain coherent engagement with evolving environments. These architectures may inform next-generation AI systems capable of operating effectively in non-stationary contexts.

Potential venues:
 • NSF – AI Foundations
 • NSF – Cyber-Human Systems
 • ONR / DARPA cognitive systems programs

5. Enactive Art Therapy Systems

Quantifying Creative Interaction for Mental Health Research

This project explores how interactive AI systems can support and study therapeutic creative processes through enactive human–AI collaboration. Traditional art therapy relies heavily on qualitative interpretation of artworks and sessions. By contrast, enactive AI architectures enable the capture and analysis of interaction dynamics during the creative process itself. These systems record temporal, spatial, and visual features of artistic expression, allowing researchers to study how emotional and cognitive states unfold through interaction.

By transforming the creative process into a measurable interaction system, this work opens new opportunities for evidence-based research in expressive therapies. The goal is to develop computational tools that help clinicians and researchers analyze patterns of creative engagement, emotional expression, and regulatory processes in therapeutic contexts.

Potential venues:

• NIH – National Institute of Mental Health (NIMH)

• NIH – National Institute on Aging (NIA) (creative engagement and cognitive health)

• NIH – BRAIN Initiative (computational approaches to behavior and cognition)

• NSF – Smart and Connected Health (SCH)

• NEA / interdisciplinary arts–health programs

The Future of Enactive AI

Artificial intelligence systems are increasingly deployed in environments characterized by uncertainty, non-stationarity, and structural change. Financial markets evolve, organizational processes reorganize, and human–AI collaborations unfold through ongoing interaction rather than static tasks. Under these conditions, intelligent behavior cannot rely solely on prediction or optimization over fixed assumptions. The research program outlined in this dossier explores an alternative foundation: Enactive AI.

In this view, intelligence emerges through continuous interaction between an agent and its environment. Systems maintain coherence not by solving problems once, but by regulating their engagement with evolving conditions over time. Perception, sense-making, and action become tightly coupled processes within an ongoing feedback loop.

Developing Enactive AI requires rethinking the architecture of intelligent systems. Rather than focusing exclusively on model accuracy or training data, the emphasis shifts toward:

Across my work, these principles are explored through experimental computational platforms such as the Emergence Machine and interactive co-creative AI systems. These platforms function as research instruments that allow enactive cognitive theories to be translated into operational architectures and empirically evaluated in real-time environments.

The broader objective of this program is to contribute to the development of next-generation adaptive intelligent systems capable of operating coherently within complex, evolving environments. As artificial intelligence becomes increasingly embedded in human institutions, the central challenge will not be prediction alone, but maintaining meaningful coordination between humans, machines, and dynamic environments. Enactive AI provides a framework for approaching this challenge.