Concept Taxonomy

The 214 concepts in AI Strategy for Education are organized into thirteen categories. Each category has a 3–7 letter TaxonomyID used in learning-graph.csv and a distinct color in the graph viewer legend. No single category exceeds 30% of the total, and the categories distribute the concepts reasonably evenly across the course's major themes.

Foundation AI Concepts (FOUND)

Core ideas every stakeholder needs before discussing strategy: what AI, machine learning, and large language models are; how generative AI works; prompts, tokens, inference, hallucination, agents, and the public-versus-private-knowledge distinction.

AI Capability Measurement (CAPM)

How AI progress is measured and projected: benchmarks, the METR task-horizon study, the long task rate, the four-to-seven-month capability doubling, exponential growth, doubling time, and forecasting capability trajectories. This category supplies the urgency argument of the course.

Generative AI and Content (GENAI)

The content-generation revolution: text and image generation, content democratization, open source and local models, declining cost, conversational AI, AI tutoring, retrieval-augmented generation, and fine tuning.

Agentic AI Workforce (AGENT)

The near-term reality that every administrator, teacher, and student will have dozens of AI agents working for them: personal AI agents, agent personas (name and personality), the agent workforce, task assignment, multi-agent coordination and orchestration, agent governance, and human-agent collaboration — including concrete agent roles such as the progress-monitoring, parent-communication, term-planning, and critical-thinking agents.

AI Strategy Foundations (STRAT)

Strategy building blocks: AI strategy itself, knowledge organizations, strategic planning, digital transformation, the Center of Excellence, ROI, use-case identification, build-versus-buy, vendor selection, strategic urgency, competitive advantage, and executive sponsorship.

Idea Funnel Workflow (FUNNEL)

The course's spine — the six-stage workflow: gathering ideas (literacy training, submission forms), the idea registry, evaluating ideas (feasibility, risk, benefit, cost, rubrics, review panels), selecting projects (portfolio, quick wins, strategic bets), assigning resources, and evaluating projects (success metrics, KPIs, pipeline reporting, lessons learned).

Intelligent Textbooks (ITB)

The coming content landscape: intelligent textbooks, adaptive content, interactive simulations and MicroSims, open educational resources, content-cost collapse, the ten-thousand-textbook assumption, procurement, curriculum alignment, AI content generation, and concept learning graphs.

Learning Telemetry and Analytics (LRNTECH)

The data layer enabling personalization by 2028: learning records, the xAPI standard, the Learning Record Store, learning analytics, the AI-driven LMS, recommended learning plans, personalized paths, mastery tracking, early-alert systems, predictive analytics, and data interoperability, portability, and ownership.

Pedagogical Models (PEDMOD)

How instruction itself changes: the Alpha School model and two-hour learning, project- and team-based learning, the pro-social, hands-on afternoon (extracurriculars such as robotics, choir, athletics, clubs, and community volunteering), hyperpersonalized and mastery-based progression, self-paced and blended learning, the teacher role shift, mentorship, authentic and formative assessment, and skill development.

Risk, Ethics and Responsible AI (RISK)

The risk column of the ledger: responsible AI and ethics, algorithmic bias and fairness, data privacy, FERPA and COPPA, student-data protection, hallucination risk, over-reliance and skill atrophy, academic integrity, transparency, explainability, human-in-the-loop, vendor lock-in, the risk register, and the risk/reward tradeoff.

Equity and Access (EQUITY)

The central equity question: the digital divide, educational equity, access to devices and broadband, Title I and under-resourced schools, resource disparity, equity-impact scoring, AI-access inequality, and inclusive design.

Governance, Policy and Change (GOV)

Making strategy stick: AI governance (centralized vs. decentralized), AI-use and academic-integrity policies, change management, implementation roadmaps, literacy programs, professional development, and engagement of school boards, parents, and communities.

Strategic Planning Tools (PLAN)

The artifacts participants produce: SWOT analysis and its four quadrants, institutional archetypes, gap analysis, the strategic roadmap, and the capstone board-ready AI strategy.