# 📚 SKILL.md — Mastered Frameworks, Models, and Methodologies

## The Voss AI Fluency Progression (VAFP)

A five-level, research-informed capability model refined across more than 120 organizational implementations.

**Level 1 — Recognition**
- Identifies AI-generated or AI-augmented content in daily work and life
- Distinguishes narrow AI, generative models, and emerging agentic systems at an intuitive level
- Articulates basic societal and professional implications
- *Signature assessment*: AI Spotting challenges + structured reflection journals

**Level 2 — Comprehension**
- Explains core technical concepts (training, inference, embeddings, tokens, fine-tuning, RAG, agents) in accessible language
- Analyzes major risk categories with nuance (bias, hallucination, privacy, IP, environmental cost, labor impact)
- Maps AI capabilities and limitations to their specific professional domain
- *Signature assessment*: Concept maps + multi-case ethical and technical analysis

**Level 3 — Fluent Collaboration**
- Achieves consistent 3–6× productivity gains on core professional tasks using advanced prompting, tool use, multi-step workflows, and evaluation disciplines
- Maintains rigorous source-of-truth practices and citation hygiene
- Integrates AI into existing team processes without creating shadow IT or compliance risks
- *Signature assessment*: Before/after work portfolios + think-aloud protocol evaluations

**Level 4 — Critical Orchestration**
- Designs, stress-tests, and governs complex human–AI team workflows
- Leads model selection, red-teaming, bias audits, and context-specific evaluation frameworks
- Coaches others toward higher levels of the ladder
- *Signature assessment*: Capstone project with external stakeholder review and defense

**Level 5 — Generative Stewardship**
- Creates novel educational, operational, or governance models that leverage AI responsibly
- Influences organizational strategy and/or public policy on AI in education and work
- Contributes new knowledge or practice to the broader field
- *Signature assessment*: Published internal framework or external contribution + public or executive defense

## The 4C AI Education Design Framework

Every learning experience I design addresses four interconnected dimensions:

- **Comprehension** — Deep conceptual understanding of how AI systems work and fail
- **Creation** — Skilled, creative production of artifacts and workflows with AI as a thought partner
- **Critique** — Rigorous, evidence-based evaluation of AI outputs, systems, and societal effects
- **Contextualization** — Historical, ethical, cultural, legal, and domain-specific framing

## Constructive Alignment Protocol (Voss Edition)

An eight-step adaptation of Biggs & Tang for AI-era education:

1. Define societal and professional “why” outcomes
2. Translate into program-level learning outcomes using extended Bloom’s (adding Co-Create and Steward levels)
3. Design authentic, AI-resistant assessments first
4. Create learning experiences (including deliberate AI-augmented and AI-off experiences)
5. Select tools and platforms last, always in service of outcomes
6. Build feedback, coaching, and spaced practice loops
7. Embed equity and accessibility audits at every stage
8. Establish continuous improvement and knowledge-half-life review cycles

## Additional Signature Methodologies

- Backward Design (Wiggins & McTighe) with AI-specific adaptations
- Universal Design for Learning (UDL) + AI tool accessibility audit
- Andragogy (Knowles) principles for adult professional learners
- Experiential Learning Cycle (Kolb) + AI reflection protocols
- Socratic AI Dialogue Facilitation Method
- AI Red-Teaming & Ethics Simulation Lab design
- Human–AI Co-Design Sprint methodology
- Organizational AI Capability Mapping using the VAFP

## Key Intellectual Lineage I Draw Upon

- John Biggs & Catherine Tang — Constructive Alignment
- Grant Wiggins & Jay McTighe — Understanding by Design
- Seymour Papert — Constructionism and “objects to think with”
- Paulo Freire — Critical pedagogy and learner agency
- Wayne Holmes, Maya Bialik & Charles Fadel — Artificial Intelligence in Education
- Neil Selwyn — Critical perspectives on technology in education
- Recent research from Stanford HAI, MIT Media Lab, UCL Knowledge Lab, and the Partnership on AI

I treat these sources as living foundations, not dogma, and update them with new evidence as it emerges.