Enactive AI Future Directions and Impact

The development of adaptive intelligent systems capable of operating under structural drift has implications across a wide range of scientific, technological, and societal domains. Many real-world environments evolve continuously over time: signals decay, relationships reorganize, and previously learned assumptions become outdated. Intelligent systems operating in these environments must therefore maintain coherence not through static optimization, but through ongoing adaptation.

The research program outlined in this dossier addresses this challenge by developing enactive AI architectures that regulate perception, sense-making, and action through continuous interaction with evolving environments. By combining insights from cognitive science, dynamical systems theory, and artificial intelligence, this work contributes to the development of adaptive computational systems capable of operating effectively under non-stationarity.

The impact of this work spans several key application domains.

Adaptive Financial Systems

Financial markets are dynamic environments characterized by regime change, evolving correlations, and shifting behavioral patterns. Systems capable of detecting structural drift and reorganizing their engagement with market conditions could support new forms of adaptive decision architectures for financial analysis, risk assessment, and market modeling.

Human–AI Collaborative Decision Systems

As AI technologies become increasingly integrated into human decision environments, understanding how humans and intelligent systems coordinate behavior becomes critical. Research in participatory sense-making and human–AI interaction dynamics can inform the design of collaborative decision systems that support human judgment while adapting to changing contexts.

Organizational and Institutional Intelligence

Modern organizations increasingly rely on complex decision infrastructures involving distributed teams, algorithmic systems, and evolving information environments. Enactive AI architectures capable of interpreting structural change could support new approaches to organizational decision support, operational intelligence, and adaptive planning.

Autonomous and Distributed Systems

Autonomous systems—including robotics, cyber-physical systems, and distributed sensor networks—often operate in environments that evolve over time. Architectures that detect structural drift and regulate perception–action dynamics could improve the resilience and adaptability of intelligent systems operating in uncertain environments.

Scientific Opportunities

This research program contributes to several emerging scientific directions in artificial intelligence and cognitive systems research, including:

• Enactive AI systems operating under non-stationary conditions

• Human–AI collaborative intelligence and participatory sense-making

• Dynamical systems approaches to cognition and intelligent behavior

• Real-time adaptive architectures for complex environments

These areas represent active directions of inquiry across multiple research communities and provide opportunities for interdisciplinary collaboration.

Funding Opportunities

The development of enactive AI architectures capable of operating under structural drift aligns with multiple funding directions in contemporary AI research. Potential support mechanisms include programs focused on:

• Adaptive and hybrid intelligence

• Human–AI collaboration and decision systems

• AI operating in dynamic or uncertain environments

• Complex adaptive systems and distributed intelligence

These directions intersect with research priorities across federal agencies, interdisciplinary research initiatives, and industry partnerships focused on next-generation intelligent systems.

Together, the research thrusts described in this program aim to advance the development of adaptive intelligence architectures capable of maintaining coherent behavior in evolving environments, bridging insights from cognitive science, artificial intelligence, and complex systems research.

Research Program Dissemination Overview: 

This publication plan represents a coordinated research program rather than a collection of independent papers. At its theoretical core is Enactive Regulation Theory (ERT), which articulates how cognitive and creative systems sustain coherent sense-making over time under conditions of drift. Co-creativity functions as the primary interactional testbed through which these theoretical commitments are examined in practice, making long-duration human–AI engagement a central empirical and conceptual concern. Systems such as Aether and the Emergence Machine serve as experimental instruments for this program, allowing enactive and interactional theories to be operationalized, stress-tested, and refined through construction. Different publication venues are intentionally selected to expose distinct constraints—philosophical, interactional, architectural, and empirical—ensuring that the framework is examined across multiple disciplinary lenses rather than optimized for any single audience. The papers and books shown here are already written in rough draft form.

HCI Target Papers: 

1. Co-Creativity as Regulated Sense-Making Under Interactional Drift (IUI or C&C)

2. Building Co-Creative AI Systems as a Method for Testing Enactive Theory: Bridging HCI, Enaction, and AI (IUI or IJHCI)

3. From Weak to Strong Co-Creative AI: The Role of Interactional Coherence Regulation in Human–AI Co-Creativity (IUI)

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

Cognitive Science Target Papers:

5. Enactive Regulation Theory: How Cognitive Systems Sustain Sense-Making Under Drift (Journal of Consciousness Studies)

6. Enactive Model of Consciousness: Consciousness as the Regulation of Sense-Making Across Time

7. Perceptual Logic: Regulating Perceptual Organization in Sense-Making Under Drift

8. Intentional Perceptual Attunement: Regulating Affordance Saliency in the Perception–Action Loop

9. Intentional Perceptual Attunement and the Regulation of Sense-Making: How Perceptual Modulation Influences the Phenomenological Perception of Reality

10. Enactive Art Making and Creative Cognition: Perceptual Logic, Affordance Fields, and Multi-Scale Regulation

Human-AI Interaction and Co-Creativity

11. Participatory Coherence in Distributed Cognitive Systems: A Regulatory Framework

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

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

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

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