TAI-KPI Project

Transformative AI - Kindness & Prosocial Integration: AI Literacy Project

Dominance Hierarchy and Nested Hierarchy Models of Information Flows for Intelligent Systems

TAI-KPI: AI Literacy Framework

TAI-KPI is an evidence-informed educational framework designed to enhance students’ decision-making, collaboration, and AI literacy through visual modeling, interdisciplinary integration, and well-established principles of cognitive regulation and group learning.

Transformative AI – Kindness & Prosocial Integration (TAI-KPI) is an AI-literacy framework that treats humans, organizations, and AI systems as interacting information flows rather than isolated objects.

The core idea is simple:

Our wellbeing and our governance depend on how clearly information can move within us, between us, and through the AI systems we build.

As AI becomes more autonomous and “agentic,” it no longer functions as a passive tool at the edges of human decision-making. It becomes a participant in our information ecology—shaping what we see, how we coordinate, and which futures feel possible.

TAI-KPI proposes that if we want trustworthy, sustainable AI integration, we need:

• Models of information flow between agents (human ↔ human, human ↔ AI, AI ↔ AI).

• Organizational structures that support bidirectional feedback, not just top-down control.

• AI-literate humans who can see their own nervous systems, their institutions, and their tools as parts of a larger, living system.

Information Flows Paradigm Shift: Dominance Hierarchy to Nested Hierarchy (Holarchy)

AI Uncertainty in VUCA World Models

The current AI boom is not just a story about efficiency; it’s a story about uncertainty. Every week brings new capabilities, concerns for job losses, new surveillance tools, and new information hazards. For students, faculty, and leaders alike, this uncertainty may create a chronic, low-grade sense of threat: the feeling that the ground of reality is shifting faster than our nervous systems can keep up. A trauma-informed lens helps us recognize that state not as personal weakness, but as a predictable response to AI-accelerated volatility. A nested governance model builds on that insight: instead of relying on brittle, top-down control, we design scale-free feedback loops—across students, staff, and leadership—that allow the institution to sense stress early, adapt, and course-correct. TAI-KPI is an attempt to prototype that architecture in a way that is scientifically grounded, ethically cautious, and supportive of leaders.

Toroidal and Resonance Models of Consciousness

The TAI-KPI framework draws inspiration from several emerging models that attempt to link consciousness, information, and resonance (e.g., Meijer’s toroidal bipoloron and Forghani’s Resonance Frequency Coding Principle). These may be framed as exploratory, interdisciplinary models, not settled science. We reference them as useful models for possible futures—creative hypotheses that can help learners imagine more integrated, field-based models of intelligence—while grounding our pedagogy in well-established modeling frameworks, neuroscience, psychology, and complexity theory. Forghani’s Resonance Frequency Coding Principle (RFCP) offers another emerging model, treating consciousness as a multi-layer resonance field, with the brain acting as a receiver–decoder rather than the sole generator of mind. The model distinguishes “carrier” (frequency patterns) from “meaning” (how living systems interpret them), and lays out several levels of resonance—from micro-scale neuronal dynamics to interpersonal and cosmic fields. We do not treat RFCP as established fact, but we find its layered resonance architecture a useful metaphor when designing TAI-KPI’s holarchic diagrams and thinking about how AI systems participate in—and reshape—these layers of information flow.

TAI-KPI Conceptual Overview

Pedagogical Framework

TAI-KPI Modeling Frameworks Cognitive Resonance Mapping

Models of Cognition: Automata

1. Overview: Humans as Information Systems

2. Legend: How to Read the Diagrams

3. Phase 1 – Recognizing the Static

3.1 Model 1: The Rigid State Machine

3.2 Reflection & Practice

4. Phase 2 – Tuning the Receiver

4.1 Model 2: The Grounded Sensor

4.2 Somatic Practices & Metacognition

5. Phase 3 – The Resonant Network

5.1 Model 3: Holarchy & Kindness Protocol

5.2 Group Exercises & Collaboration

6. Phase 4 – AI as Cognitive Amplifier

6.1 Model 4: Collapse vs Expansion

6.2 AI Literacy & World-Model Expansion

7. Synthesis

7.1 From Dominance to Holarchy

7.2 How This Connects to the Toroidal Overview