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Chapters

This textbook is organized into 13 chapters covering 214 concepts.

Chapter Overview

  1. AI Foundations — What Every Educator Needs to Know — Introduces the core AI vocabulary every stakeholder needs before any strategy discussion can begin — covering machine learning, large language models, generative AI, prompts, hallucination, AI agents, and the critical distinction between public and private knowledge.

  2. Measuring the AI Capability Curve — Grounds the course's urgency in hard, published data by examining the METR long-tasks study and its finding that AI task horizons double roughly every four to seven months.

  3. Building Your AI Strategy — Establishes the strategy vocabulary and decision-making frameworks that underpin everything that follows: strategic planning, knowledge organisations, digital transformation, the Generative AI Center of Excellence model, return on investment, use-case identification, build-versus-buy, vendor selection, and executive sponsorship.

  4. Generative AI, Intelligent Textbooks, and the Content Revolution — Surveys the content-generation landscape — text and image generation, AI tutoring, conversational AI, retrieval-augmented generation, open-source and local models, and declining cost — then introduces the intelligent-textbook stack: adaptive content, interactive simulations, open educational resources, the ten-thousand-textbook assumption, curriculum alignment, AI content generation, and concept learning graphs.

  5. The Idea Funnel — Gathering, Registering, and Evaluating Ideas — Walks the first half of the course's idea-funnel spine: introducing the funnel concept itself, the one-hour AI-literacy training that seeds good ideas, idea generation, the submission form, the idea registry and its metadata, problem statements, evaluation criteria, feasibility and risk/benefit/cost scoring, scoring rubrics, expert review panels, feedback loops, and recognition awards.

  6. Selecting Projects, Assigning Resources, and Evaluating Outcomes — Completes the funnel spine by covering the downstream stages: project selection, the project portfolio, quick wins versus strategic bets, resource assignment, team formation, shared code repositories, cross-team collaboration, project evaluation, success metrics, KPIs, pipeline and funnel-stage reporting, quarterly executive reporting, lessons learned, problem and solution taxonomies, and content-quality assessment.

  7. Learning Telemetry, xAPI, and AI-Driven Personalization — Explores the data layer that makes AI-recommended learning plans possible by 2028: learning records, the xAPI standard, the Learning Record Store, learning analytics, predictive analytics, the AI-driven LMS, recommended and personalised learning paths, mastery tracking, early-alert systems, and data interoperability, portability, and student-data ownership.

  8. New Pedagogical Models — The Alpha School and Beyond — Examines how instruction itself changes when AI handles core academics: the Alpha School model and its two-to-three-hour AI-tutored morning block, pro-social learning and hands-on extracurricular afternoons, project- and team-based learning, hyperpersonalised and mastery-based progression, self-paced and blended learning, the teacher-as-mentor role shift, authentic and formative assessment, and skill development.

  9. Responsible AI — Ethics, Bias, Privacy, and Fairness — Covers the foundational risk column with equal weight to the reward side: responsible AI and ethics, algorithmic bias and fairness, data privacy, FERPA and COPPA compliance, student-data protection, hallucination risk, over-reliance and skill atrophy, academic integrity and AI detection, misinformation risk, transparency, explainability, and the human-in-the-loop principle.

  10. Academic Integrity, Equity, and Managing AI Risk — Completes the risk picture and pivots to equity: AI safety, vendor lock-in, the risk register, the risk/reward tradeoff, student well-being, and screen-time concerns — then addresses the central equity question with the digital divide, educational equity, device and broadband access, Title I and under-resourced schools, resource disparity, equity-impact scoring, AI-access inequality, and inclusive design.

  11. AI Governance, Policy, and Change Management — Addresses making strategy durable through governance, policy, and change management: centralised vs.

  12. The Agentic AI Workforce in Education — Introduces the near-term reality that every educator and student will manage dozens of named AI agents: personal AI agents, agent personas (name and personality), the agent workforce, task assignment, multi-agent coordination and orchestration, agent governance, human-agent collaboration, and four concrete example agents — progress monitoring, parent communication, term planning, and critical thinking.

  13. Strategic Planning — SWOT, Roadmaps, and the Capstone Strategy — The culminating chapter draws every thread together: conducting a SWOT analysis and its four quadrants, institutional archetypes, gap analysis, the strategic roadmap, and — as the capstone deliverable — producing a board-ready AI strategy document that synthesises the idea funnel, the risk register, the governance plan, the agent workforce, and the phased roadmap toward personalised, AI-supported learning.

How to Use This Textbook

Each chapter builds on the ones before it — concepts always appear after their prerequisites, so reading in order is strongly recommended for first-time readers. Practitioners who already have a strategy background may skip to Chapter 4 after reading Chapters 1–2 for the METR capability data. Every chapter index lists the exact concepts covered and links back to any prior chapters whose content is assumed.

Note: Each chapter index includes a full concept list drawn directly from the learning graph. The "TODO: Generate Chapter Content" marker shows where detailed content will be added in the next phase.