SHIFT 01
From performance to participation
For long-duration systems, the key question is not only whether a response is correct, but how each response reshapes the evolving relation.
A science of interaction through time
The interaction is the object of study.
The Interaction Science Institute develops theories, instruments, and AI systems for studying how human, artificial, and hybrid systems sustain coherence, adapt to drift, and reorganize across long-duration interaction.
01Interaction, not isolated cognition
02Coherence, drift, regulation
03Human, AI, and hybrid systems
Live temporal field
Coupling · Drift · Coherence
From events to trajectories
Interaction Science treats pauses, turns, responses, breakdowns, repairs, regimes, and emergent identities as measurable temporal phenomena.
Why this exists
Most scientific and technical frameworks still evaluate systems by outputs, predictions, or internal states. Interaction Science asks how relational systems remain viable while conditions change.
SHIFT 02
From snapshots to trajectories
Interaction is a history: timing, rupture, uptake, repair, stabilization, and return.
SHIFT 03
From optimization to regulation
Optimization minimizes error inside a frame. Regulation maintains or reorganizes the frame itself.
Field definition
Interaction Science studies relations that unfold, adapt, and reorganize.
It brings together temporal interaction, enactive cognition, human-centered AI, design research, adaptive systems, process measurement, and collaborative scientific interpretation.
E
EventsLocal moments of significance: pauses, shifts, repairs, breakdowns, openings.
T
TrajectoriesOrdered histories of interaction states as they change through time.
R
RegimesStable modes of relation, participation, behavior, or coordination.
C
CoherenceThe capacity of a system to remain organized without becoming rigid.
Umbrella hierarchy
Enactive AI fits inside a broader interaction science.
Enactive AI remains the flagship research program for adaptive human-AI systems. Interaction Science is the field-level container that can also include human-human, AI-AI, organizational, creative, clinical, and ecological interaction.
1
Interaction Science InstituteTop-level field-building, theory, research agenda, and community.
2
Enactive AIFlagship program for AI systems that participate, regulate, and adapt through interaction.
3
Systems and instrumentsEmergence Machine, Kalyriel Scope, Temporal Scope, Aether, and future laboratories.
Research programs
Each program approaches interaction as a temporal, measurable, and regulative phenomenon.
Non-Code AI Cognition Design
Interaction Science introduces a complementary approach to AI design: improve intelligent behavior by changing the structure of participation, timing, memory, feedback, and affordance rather than changing the model alone.
A new design paradigm
Interaction design becomes cognitive design.
Most AI research assumes that improving cognition requires changing the internal model: retraining neural networks, modifying algorithms, increasing parameters, or optimizing weights. Interaction Science adds another path. Intelligent behavior can often be transformed by redesigning the structure of interaction itself.
Interaction determines what information becomes available, when it becomes available, how participants coordinate, what forms of feedback exist, which affordances emerge, and how meaning develops across time. Changes to turn-taking, timing, memory, visualization, constraint, and response structure can alter the cognitive behavior of a human-AI system without modifying a single line of model code.
From this perspective, the interface is not merely a wrapper around cognition. The interaction is part of the cognitive architecture.
Interaction becomes architecture
Designing cognition through participation.
1
Restructure interactionChange turn-taking, timing, memory, affordances, visualization, or feedback rather than rewriting the model.
2
Observe new cognitive behaviorDifferent interaction structures invite different forms of exploration, reflection, regulation, coordination, and shared problem solving.
3
Measure participationEvaluate trajectories, coherence, drift, repair, coupling, and emergent organization rather than isolated outputs alone.
4
Build interactional intelligenceSystems such as Aether, Temporal Scope, Kalyriel Scope, and the Cognitive Trajectory Laboratory demonstrate how new forms of cognition emerge through interaction itself.
Timing
Pauses, turn length, response rhythm, and moments of uptake change what the AI can notice and how the human can regulate the exchange.
Representation
Trajectories, canvases, timelines, maps, and feedback panels make the interaction itself available as an object of reflection.
Participation
The AI becomes more intelligent by taking a different role in the relation: collaborator, mirror, regulator, provocateur, assistant, or scientific instrument.
Theoretical Foundations
Interaction Science builds upon enactive cognitive science, ecological psychology, embodied cognition, distributed cognition, dynamical systems theory, and participatory sense-making. These traditions provide the conceptual foundation for the Institute's empirical systems.
The conceptual foundation
Enaction provides the theoretical backbone.
Enactive approaches describe cognition as an activity of adaptive sense-making rather than the manipulation of internal representations alone. Organisms continuously regulate their relationships with their environments, maintaining coherence while responding to changing conditions. Meaning is not passively received but actively enacted through participation.
Interaction Science extends these ideas by asking how participatory processes can be preserved, measured, modeled, visualized, and designed. Rather than studying isolated minds, it studies evolving interactional trajectories. Rather than measuring only decisions or outputs, it examines the temporal organization through which cognition unfolds.
Participatory Sense-Making is especially important because it shows how meaning can emerge between participants through the dynamics of interaction itself. This insight motivates the Institute's work on human-AI collaboration, co-creative systems, adaptive interfaces, and interaction-centered intelligence.
From philosophy to empirical science
Interaction Science operationalizes enaction.
E
Enactive Cognitive ScienceExplains why cognition emerges through embodied, situated, adaptive participation.
I
Interaction ScienceDefines interaction itself as the primary scientific object of investigation.
T
Temporal ScienceProvides methods for preserving, segmenting, modeling, visualizing, and interpreting interaction through time.
S
Empirical SystemsEnactive AI, the Cognitive Trajectory Laboratory, Aether, Kalyriel Scope, Temporal Scope, and the Emergence Machine transform theory into measurable computational instruments.
Why cognition emerges
Enactive theory
Cognition is enacted through embodied, situated, regulatory participation with a world.
What to study
Interaction Science
The primary object is the evolving relation: events, trajectories, regimes, coherence, drift, and repair.
How to study it
Temporal Science
Process-preserving instruments make unfolding interaction observable, comparable, and interpretable.
Research ecosystem
The institute is the umbrella for a distributed set of satellite research centers. Each site concentrates on a different layer of the same problem: how cognition, creativity, collaboration, and adaptive intelligence emerge through temporally organized interaction.
One field, multiple research lenses
Interaction Science connects theory, instruments, systems, and application domains.
The ecosystem moves from foundational accounts of creativity and sense-making, through human–AI co-creation and enactive intelligence, to instruments for temporal analysis, clinical creative process research, and adaptive architectures for non-stationary environments.
How the satellites relate
Each center studies a different scale of interaction.
Some sites explain the conceptual foundations of participatory creativity. Others build AI collaborators, capture process data, discover temporal structures, or regulate adaptive systems under drift. Together they form an integrated research program rather than a collection of unrelated projects.
Enactive AI
Enactive AI studies artificial intelligence as a participant in unfolding human activity. Its systems are designed to perceive, respond, reorganize, and stabilize through interaction while tracking coupling, drift, coherence, and emergence.
The center unifies co-creative agents, cognitive trajectory instruments, collaborative temporal science, and regulation-centered adaptive architectures.
Role in Interaction Science
Visit Enactive AI ↗
Transforms interaction-centered theory into working AI systems, research instruments, design principles, and empirical programs for long-duration human–AI collaboration.
Creative Sense-Making
Creative Sense-Making reframes creativity as an emergent process distributed across people, materials, tools, environments, and other participants rather than as an isolated mental act.
It provides concepts and analytical methods for interaction dynamics, participatory creativity, quantified co-creation, and sense-making curves.
Role in Interaction Science
Visit Creative Sense-Making ↗
Supplies the foundational account of how meaning and novelty develop through perception, action, reflection, constraint, and adaptation across time.
Co-Creative AI
Co-Creative AI documents more than a decade of research into humans and computational systems collaborating as active participants in shared creative and cognitive processes.
It brings together artistic computer colleagues, participatory sense-making, creative trajectories, quantified co-creation, explainability, prototypes, and the publication lineage of interaction-centered AI.
Role in Interaction Science
Visit Co-Creative AI ↗
Preserves the intellectual history and empirical foundation showing how intelligence and creativity can arise between participants rather than within either participant alone.
Kalyriel Scope
Kalyriel Scope turns complex time-series data into inspectable temporal structures. It supports discovery, replay, comparison, annotation, competing interpretations, and reusable libraries of recurring motifs.
Its local, regional, and global views allow experts to examine immediate patterns, contextual episodes, and long-duration regimes in one integrated environment.
Role in Interaction Science
Visit Kalyriel Scope ↗
Provides the collaborative scientific memory through which computationally discovered patterns become expert-reviewed, semantically meaningful, and reusable knowledge.
Enactive Art Therapy
Enactive Art Therapy studies art-making as embodied, regulatory, and process-oriented sense-making. Rather than interpreting only the finished image, it preserves pauses, returns, transitions, exploration, fragmentation, regulation, and stabilization.
Its Cognitive Trajectory Laboratory transforms drawing activity into measurable states, trajectories, properties, events, chapters, interpretive outputs, and research reports.
Role in Interaction Science
Visit Enactive Art Therapy ↗
Demonstrates how temporally precise interaction data can support process-sensitive art therapy research without reducing creative experience to a static artifact or diagnostic score.
Emergence Machine
The Emergence Machine is a lightweight framework for continuous online learning, drift detection, regime switching, multi-level analysis, and adaptive regulation in changing environments.
It treats drift as information about declining fit and reorganizes attractors, plasticity, and behavioral regimes while keeping its internal dynamics visible and inspectable.
Role in Interaction Science
Visit the Emergence Machine ↗
Provides the computational architecture for studying and sustaining viability when interaction, signals, goals, or environmental conditions change over time.
Build relational theories
Creative Sense-Making and Co-Creative AI explain how cognition, meaning, and creativity become distributed across participants, artifacts, environments, and histories.
Create temporal instruments
Kalyriel Scope and the Cognitive Trajectory Laboratory preserve process, reveal multi-scale organization, and turn temporal activity into interpretable scientific evidence.
Design regulative systems
Enactive AI and the Emergence Machine build systems that adapt their participation, detect structural drift, reorganize regimes, and remain viable through change.
Research specializations
Interaction Science can be applied wherever outcomes depend on how people, technologies, institutions, or adaptive systems coordinate through time—not only on what any single participant produces.
Human–AI Interaction
Study turn-taking, reliance, correction, mutual adaptation, breakdown, recovery, and the emergence of stable collaboration patterns across extended use.
Quantified Art Therapy
Preserve the temporal structure of drawing and making—pauses, revisitations, expansion, fragmentation, regulation, and consolidation—so therapeutic process can be examined without reducing it to a final image.
Co-Creation and Therapeutic Value
Investigate how creative interaction supports exploration, agency, emotional regulation, perspective change, and the development of new forms of shared meaning.
Finance and Market Interaction
Model markets as evolving regimes of participation, drift, attractor formation, and transition, enabling systems that respond to structural change rather than assuming stationary conditions.
Education and Skill Formation
Trace how understanding emerges through feedback, hesitation, repair, scaffolding, and increasing coordination between learners, teachers, tools, and intelligent tutors.
Teams, Organizations, and Communities
Examine how groups stabilize routines, fall into rigid attractors, recover from disruption, and reorganize their shared practices under pressure or change.
Methodological foundation
Temporal Science makes interaction observable.
Interaction Science becomes scientifically compelling when systems preserve process rather than only outcomes. Temporal Science provides the instrumentation, models, and interpretive layers needed to transform streams of activity into analyzable events, trajectories, episodes, attractors, regimes, transitions, and regulatory patterns.
Its purpose is not simply to add timestamps. It is to retain the organization of change: what happened before a rupture, how a system responded, whether coherence recovered, which patterns returned, and when a genuinely new mode of interaction emerged.
From data to interpretation
A shared pipeline for temporal inquiry.
1. CaptureRecord interaction data with sufficient temporal resolution: actions, turns, movement, signals, responses, and context.
2. SegmentIdentify meaningful events, episodes, boundaries, pauses, transitions, and changes in participation.
3. ModelConstruct trajectories, detect attractors and regimes, and estimate coherence, drift, coupling, and regulation.
4. VisualizeMake process inspectable through timelines, state spaces, landscapes, chapters, and multi-scale views.
5. InterpretCombine computational evidence with human expertise to explain how interaction developed and why it changed.
Core objects of Interaction Science
The institute gives a shared vocabulary to phenomena that are usually treated as background conditions rather than primary scientific objects.
Local
Events
Pauses, turns, interruptions, ruptures, repairs, responses, and moments of uptake.
Regional
Episodes
Interactional chapters where participation takes on a recognizable organization.
Global
Trajectories
The long arc of coherence, drift, adaptation, and emergent identity over time.
Structure
Attractors
Recurring forms of interaction that pull the system toward familiar patterns.
Dynamics
Regimes
Stable modes that organize what kinds of action and meaning become likely.
Change
Transitions
Breakdowns, reorientations, recoveries, bifurcations, and reorganizations.
Viability
Coherence
How organization persists while allowing enough openness to keep adapting.
Signal
Drift
Change in direction, coupling, meaning, structure, or viability across interaction.
Method
Regulation
How a system maintains, repairs, or reorganizes its interactional frame.
Founder
The Interaction Science Institute consolidates a research trajectory spanning human–computer co-creativity, computational creativity, enactive cognition, interaction analytics, adaptive systems, and human-centered artificial intelligence.
Tiny Aether
You draw in charcoal · Aether responds in blue
Feedback received
Nicholas Davis, PhD
Founder · Interaction Science Institute
Researcher · theorist · system builder
Understanding intelligence as something that unfolds through interaction.
Nicholas Davis, PhD develops theories, analytical methods, and interactive systems for understanding how humans and artificial agents create, adapt, regulate, and construct meaning together through time. His research treats interaction—not the isolated human or machine—as a primary unit of analysis.
Davis earned his PhD in Human-Centered Computing from the Georgia Institute of Technology. His work emerged from an interdisciplinary synthesis of human–computer interaction, computational creativity, cognitive science, creativity research, ecological psychology, enactive cognition, and adaptive-system design.
Across more than a decade of research, he has developed co-creative systems, creative-process models, quantified interaction methods, cognitive trajectory frameworks, and regulation-centered AI architectures. This body of work connects early research on artistic computer colleagues and the Drawing Apprentice with contemporary programs in Enactive AI, Creative Sense-Making, Cognitive Trajectory Modeling, Enactive Drift Regulation, collaborative temporal science, and the Emergence Machine.
Research focusHuman–AI co-creation, interaction-centered intelligence, enactive AI, creative cognition, and adaptive regulation.
MethodCombine theory, instrumented prototypes, temporal interaction data, mixed-method research, and computational modeling.
ContributionShift AI evaluation from isolated outputs toward participation, trajectories, coherence, drift, and regulation.
Institute missionBuild a shared scientific home for the study of human, artificial, and hybrid interaction through time.
Institute model
A field-building home for publications, prototypes, datasets, diagrams, workshops, and collaborations around the science of interaction.
Additional linked platforms
Prototype laboratories extend the ecosystem.
Beyond the six principal satellite sites, the research program also includes live environments such as Aether, the AI Drawing Partner, the Tiny Emergence Machine, and Temporal Scope. These function as experimental instruments and demonstrations within the larger centers rather than as separate conceptual institutes.
THEORY
Paradigm development
Define the concepts, principles, and methods needed to study interaction as a temporal scientific object.
INSTRUMENTS
Research tools
Build interfaces that preserve process data and transform interaction into inspectable trajectories.
AI SYSTEMS
Regulative architectures
Develop AI systems that model coherence, drift, participation, and adaptation across time.
COMMUNITY
Shared vocabulary
Support researchers, designers, clinicians, and organizations in naming and comparing temporal phenomena.
Work together
Build systems that understand interaction over time.
Use the institute as the umbrella for research partnerships, prototype development, adaptive AI strategy, temporal interaction analysis, and field-building around Interaction Science.