Teaching Stack of Models for TAI-KPI
Minimum Viable Language of Modeling Frameworks for AI Literacy
1. Why a “Teaching Stack” of Models?
TAI-KPI isn’t just a curriculum about AI. It’s a language of models that helps humans understand:
how our own cognition and emotions work,
how socio-technical systems amplify or ease stress,
how AI systems mirror and reshape our world-models, and
how trust can grow from the bottom up in a fractured, post-colonial world.
No single diagram can carry all of that. Instead, we use a stack of simple, interlocking models—each one a different lens:
Automata of cognition
Expectation–Mismatch Stress (EMS) & Window of Tolerance
Bayesian Brain & Weather Forecasting
Markov Blankets & Boundaries
Cellular Automata & Social Emergence
Trust Dynamics in System-Dynamics form
Holarchy & Reverse Mentoring
AI as a Theory-of-Mind Mirror
Together, these give students, AI developers, and community organizers a shared vocabulary that can travel across disciplines, cultures, and power gradients.
The teaching stack is our “minimum viable language” for describing how intelligence, trust, and power flow in a VUCA world.
2. Model 1 – Automata of Cognition
Reflex • Pattern • Integrative Layers
Purpose Give learners a compact, non-pathologizing map of human cognition that foregrounds emotion and embodiment.
Key Ideas
Reflex Automaton – fast somatic responses, polyvagal survival modes, “snake or stick?” decisions.
Pattern Automaton – emotional & associative flow; habits; narrative templates; subconscious priors.
Integrative Layer – awareness, meaning, attention aperture; active inference; self-observation; wisdom.
Teaching Moves
Use lived examples: a startle response, a conflict, a moment of regret.
Ask: “Which layer was driving?” and “What other layer might you want online next time?”
Emphasize that all three layers are intelligent; the Integrative Layer coordinates them.
Curricular Role
This is the anchor model.
Other metaphors (EMS, weather, Markov blankets, trust dynamics) all plug back into it.
3. Model 2 – EMS & the Window of Tolerance
Expectation–Mismatch Stress as Cognitive Load
Purpose Reframe “trauma” as Expectation–Mismatch Stress (EMS) and introduce the Window of Tolerance / attention aperture as a flexible, trainable capacity.
Key Ideas
EMS = cumulative strain when expectations repeatedly fail to match reality.
Shows up as anxiety, numbness, irritability, distraction, or collapse.
Window of Tolerance = the range in which the Integrative Layer can stay online and keep all three automata in dialogue.
Contemplative, somatic, and relational practices widen this window over time.
Teaching Moves
Invite students to sketch their own Window of Tolerance and label “too much / too little” activation.
Link back to the automata:
Narrow window → Reflex dominates, Pattern rigidifies, Integrative goes offline.
Wider window → Integrative can re-interpret signals and reweight emotional priors.
Curricular Role
Bridges personal experience (stress, burnout, anxiety) with systemic forces (VUCA, climate crisis, AI disruption).
Sets the tone for a trauma-informed, non-blaming conversation.
4. Model 3 – Bayesian Brain & Weather Forecasting
“Your Mind as a Climate Model”
Purpose Explain active inference in plain language: we are all forecasting machines.
Key Ideas
The nervous system constantly predicts what will happen next and updates its “climate model” of life.
Forecast = prior expectations.
Weather event = incoming experience.
Prediction error = mismatch between forecast and reality.
Chronic EMS is like living in permanent storm mode, even on clear days.
Teaching Moves
Ask: “What forecasts did you learn growing up? About safety, authority, conflict, success?”
Explore how new experiences and communities help update the climate model.
Connect to Integrative Layer: it’s the “climate scientist” that can question old forecasts and design new ones.
Curricular Role
Makes Active Inference intuitive.
Prepares the ground for talking about AI as another kind of forecasting system—but without a body or emotions.
5. Model 4 – Markov Blankets & Boundaries
Where Self Meets World
Purpose Offer a visual language for boundaries—psychological, social, and institutional—without heavy math.
Key Ideas
A Markov blanket is a conceptual boundary between “inside” and “outside” of a system.
For humans: body, identity, role, and community all act as layered blankets.
For institutions: policies, dashboards, and AI systems act as blankets between leadership and lived reality.
Healthy blankets allow porous, meaningful exchange; unhealthy ones either leak or harden.
Teaching Moves
Have learners draw their own “layers”: body, family, peers, institution, platform, planet.
Ask: “Where does information flow freely? Where does it get blocked or distorted?”
Connect to EMS: rigid blankets trap old priors; open, well-governed blankets allow updating and repair.
Curricular Role
Prepares students to think about AI governance and interfaces as boundary design problems.
Integrates holarchy: individuals and institutions as nested blankets.
6. Model 5 – Cellular Automata & Social Emergence
Micro-Rules, Macro-Patterns
Purpose Show how local interaction rules create large-scale social patterns, making the Avalanche of Kindness plausible rather than utopian.
Key Ideas
Each person is a small automaton (Reflex–Pattern–Integrative).
Simple rules (listen, retaliate, forgive, share, exclude) generate complex group behavior.
Trust, polarization, cooperation, and collapse emerge from these micro-rules.
Changing the rules ≈ changing the future trajectory of society.
Teaching Moves
Run or show simple cellular automata (e.g., Conway’s Game of Life) or agent-based demos.
Ask: “What micro-rules around you are feeding conflict or extraction?”
Then: “What micro-rules could feed prosocial cascades?”
Curricular Role
Makes it thinkable that college meetups and developer communities matter: changing local rules really does influence global patterns, especially in a networked world.
Connects naturally to Kindness Thermodynamics and trust dynamics.
7. Model 6 – Trust Dynamics as a System-Dynamics Loop
Trust as a Stock, EMS as a Load
Purpose Give a simple system-dynamics picture of how trust accumulates and depletes over time, so learners see why process and governance matter as much as insight.
Key Ideas
Trust = a stock that can rise (through repair, reliability, shared risk) or fall (through betrayal, opacity, neglect).
Flows that increase trust: transparency, follow-through, fair conflict resolution, shared vulnerability, inclusive design.
Flows that decrease trust: secrecy, extractive AI deployments, broken promises, unresolved harms, colonial patterns.
EMS acts as friction in the system:
higher EMS → slower trust accumulation, faster trust decay.
Prosocial emergence requires trust thresholds: enough trust in enough nodes for new patterns to be stable.
Teaching Moves
Draw a simple stock-and-flow diagram: Trust Reservoir with “Trust Inflow” and “Trust Outflow”.
Invite participants to list real behaviors or policies that belong on each valve.
Connect to Integrative Layer: leaders and teams can deliberately design trust-building loops instead of assuming trust will appear by itself.
Curricular Role
Links internal work (widening the Window of Tolerance) with structural work (changing rules, policies, AI use).
Makes it easier to talk about bottom-up transformation: student & developer meetups are “trust pumps” connecting layers of the holarchy.
8. Model 7 – Holarchy & Reverse Mentoring
Bottom-Up Intelligence in Nested Systems
Purpose Show how knowledge, care, and imagination can flow upward through a hierarchy without collapsing into rebellion or co-optation.
Key Ideas
Holarchy = nested wholes (individuals → teams → orgs → regions → planet).
Each level has local intelligence and constraints.
Reverse mentoring = structured pathways for insights from younger / less powerful nodes to influence decision-makers.
When combined with the shared modeling language, reverse mentoring becomes a translation channel:
students and developers bring VIM / EMS / automata language upward,
leaders learn to reinterpret their own prediction models through these lenses.
Teaching Moves
Map participants’ own institutions as holarchies.
Identify possible reverse-mentoring channels: advisory councils, intergenerational task forces, student–faculty–admin circles, community–developer–policymaker forums.
Emphasize post-colonial ethos:
the point is not “saving” others, but listening and integrating diverse world-models.
Curricular Role
Explains how TAI-KPI ideas can move from classrooms and meetups into leadership and policy.
Frames prosocial AI work as intergenerational and intercultural co-learning, not top-down tech transfer.
9. Model 8 – AI as a Theory-of-Mind Mirror
Practicing Metacognition With Machines
Purpose Use AI tools themselves as practice grounds for theory of mind, critical thinking, and emotional literacy.
Key Ideas
AI systems don’t feel, but they can simulate perspectives and highlight patterns in our own thinking.
Interacting with AI can help learners:
see their own assumptions mirrored back,
explore multiple framings of the same situation,
practice asking “what else might be going on here?”
In a trauma-informed context, AI is not a therapist; it is a structured reflection partner.
Teaching Moves
Design prompts that ask AI to:
generate alternative stories about a conflict,
outline different stakeholders’ viewpoints,
suggest questions a wise mentor might ask.
Always debrief:
Where did the AI miss important context?
What biases did it reveal?
What did you notice in your own emotional response?
Curricular Role
Ties together the whole teaching stack:
automata of cognition,
EMS & Window of Tolerance,
weather/forecasting,
Markov blankets,
social emergence,
trust dynamics,
holarchy, into a living practice with real tools.
10. Putting It All Together for a Proposal
In a proposal, you can summarize the teaching stack like this:
Automata of Cognition + Integrative Layer – foundational model for individual intelligence.
EMS + Window of Tolerance – trauma-informed lens for stress and learning.
Bayesian Brain / Weather Forecasting – intuitive explanation of active inference.
Markov Blankets & Boundaries – design language for interfaces and governance.
Cellular Automata & Social Emergence – explanation of how local behavior scales to global patterns.
Trust System Dynamics – practical tool for redesigning institutions and AI deployments.
Holarchy + Reverse Mentoring – strategy for bottom-up, post-colonial transformation.
AI as Theory-of-Mind Mirror – applied practice for AI literacy and metacognition.
TAI-KPI does not ask students to believe a new ideology. It gives them a shared set of models to see how human, organizational, and AI systems actually behave—and to design prosocial, trauma-informed alternatives from the bottom up.
Last updated