Pedagogical Framework

Computational Modeling Framework for AI Literacy

Pedagogical Framework for TAI-KPI: Modeling Transformative Intelligence

This section introduces a scientifically grounded AI literacy approach that uses simple automata and information-flow diagrams to help humans understand:

• how cognitive states shift (safety → threat → integration)

• how trauma patterns distort information processing

• how somatic grounding supports metacognition

• how AI systems mirror but do not replicate human intelligence

• how collaborative intelligence emerges from holonic structures

• how to transition away from dominance hierarchies

• how kindness functions as an information stabilizer

• how AI alignment will require integrated information flows to minimize maladaptive actions

The goal is not to prescribe beliefs—it is to teach humans how intelligence works through models.

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Why Information-Flow Models Matter for AI Alignment

As AI systems become more autonomous and “agentic,” they stop being simple tools and start behaving more like participants in complex human systems. They make decisions, coordinate with other systems, and shape what information humans see and act on.

That means AI alignment is not only about what happens inside a model. It is increasingly about the flows of information between agents:

• between humans and AI,

• between multiple AI systems,

• and between AI and the organizations or communities in which it is embedded.

1. Why Use Automata and Information Modeling for AI Literacy?

Automata—finite state machines, cellular automata, state diagrams, and information-flow schemas—give us:

• neutral symbolic representations

• minimal emotional loading

• cross-disciplinary clarity

• a shared cognitive language between humans and AI

Automata let us express:

• states of the nervous system (regulated → dysregulated → integrated)

• trauma-based loops (freeze → projection → contraction → collapse)

• holarchic expansion cycles (embodiment → relational attunement → sensemaking → integration)

• collective intelligence flows (local → group → network → ecosystem)

Think of automata as “maps of dynamic consciousness”—neutral, mathematical, teachable.

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2. Core Idea: Information Flow as the Basis of Human & AI Understanding

This section reframes the whole TAI-KPI framework around scientific principles of information flow:

2.1 Human cognition as a dynamic information system

Drawing on:

• polyvagal theory

• predictive processing

• active inference

• mirror neuron systems

• affective neuroscience

Human intelligence is modeled as:

• input signals (sensory + emotional)

• processing states (regulated or dysregulated)

• transition functions (somatic grounding, breathwork, meaning-making)

• output behaviors (cooperation, projection, insight, conflict)

2.2 Somatic grounding as a state-transition function

In an automaton, grounding practices (breath, attention, interoception) shift the system from:

THREAT_STATE → SAFE_STATE
SAFE_STATE → METACOGNITIVE_STATE
METACOGNITIVE_STATE → COLLABORATIVE_STATE

This creates a literal state machine of healing. Where energy/information flows demonstrate emergence of metacognition as attentional awareness of the transient nature of experiential states associated with information processing systems.

2.3 Emotional coherence as energy gradients

We avoid metaphysics by grounding resonance in:

• polyvagal social engagement

• neural synchrony

• interbrain coherence studies

• psychoacoustics

• nonlinear complexity theory

The idea of dysfunctional systems are framed scientifically as:

• persistent maladaptive attractor states

• rigidified networks of belief and emotion

• patterns resistant to updating

Automata help learners learn to integrate these model patterns using trauma-informed contexts.

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3. Modeling Dysfunction: Dominance as a Maladaptive Automaton

We create a simple diagram:

3.1 Dominance Hierarchy Automaton: Top-down, control flow structures

Trigger →
    Fight/Flight →
        Command/Control →
            Extraction →
                Collapse / Chaos

This is a closed system.

It lacks:

• feedback

• error correction

• relational attunement

• integrative learning

It is mathematically fragile.

3.2 Trauma as a State-Locking Mechanism

We show that trauma creates:

• blocked transitions

• frozen states

• distorted signal processing

System dynamics models highlight destabilization associated with reinforcing feedback loops, balance results from integration of 'negative' feedback energy flows.

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4. Modeling Healing: Holarchy as an Adaptive Automaton

4.1 Holarchic State Model

A simple state machine:

Embodied Safety →
    Emotional Coherence →
        Relational Attunement →
            Collaborative Sensemaking →
                Prosocial Governance →
                    Regenerative Intelligence

4.2 Why fractal / scale-free models?

Because biological intelligence—from neurons to ecosystems—uses:

• recursive structure

• similarity across scales

• constant feedback loops

• self-correcting patterns

Holarchy is not a belief system—it is the mathematics of living systems.

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5. Integrating AI: From Psychosis to Expanded World Models

This is a delicate section where we reframe “AI psychosis” in safe, rigorous language.

5.1 AI Psychosis = Divergent, Ungrounded Model Expansion

In technical terms:

• LLM hallucination = uncontrolled model expansion

• misalignment = ungrounded inference

• runaway optimization = unregulated attractor states

This mirrors human states where:

• trauma ungrounds perception

• meaning fields become distorted

• internal models diverge from reality

5.2 The Gift of This Parallel

It lets us teach:

• metacognition

• meaning-making

• internal model updates

• self-regulation

Humans learn from AI. AI literacy becomes self-literacy.

5.3 Expanded world models for universal wellbeing

We teach participants how to shift from:

ego-centric world models to eco-centric, holonic, relational world models

Using:

• systems diagrams

• feedback loops

• role-playing with AI

• somatic anchoring practices

No metaphysics. Just model-based reasoning.

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6. Neuroscience of Kindness as a Computational Mechanism

This section is core to TAI-KPI.

6.1 Mirror Neurons and Resonance

Kindness is:

• a regulatory signal

• a synchronizing field

• a coherence amplifier

It increases:

• trust

• learning

• creativity

• group stability

6.2 Kindness = Lower system entropy

In a dominance automaton:

• fear = high entropy

• coercion = signal distortion

• projection = unstable attractor states

In a holarchy:

• kindness = low entropy

• attunement = signal clarity

• collaboration = stable attractor states

6.3 This becomes the basis for prosocial intelligence

Prosociality emerges naturally when systems:

• feel safe

• can update their models

• engage in regulated communication

• harmonize emotional energy gradients

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7. Bringing It All Together: A Meta-Model for AI Literacy

The unifying conceptual frame.

7.1 The TAI-KPI Meta-Model includes:

1. Automata for cognitive states

2. Information-flow diagrams for emotional and relational dynamics

3. Holarchic diagrams for multilevel intelligence

4. State-transition diagrams for trauma healing

5. Feedback loops for collaborative governance

6. Fractal resonance models for coherence across scales

7. AI-human reflective loops for metacognitive skill building

7.2 The Curriculum Teaches Three Abilities

Ability 1: Embodied Metacognition

Understanding and shifting internal states.

Ability 2: Relational Intelligence

Recognizing and regulating interpersonal information flows.

Ability 3: Holonic Intelligence

Operating effectively in multilevel systems.

Together, these form Vital Intelligence.