Key Concepts in Building Intelligent Textbooks
This section covers the foundational educational theory concepts for understanding and creating intelligent textbooks. This content is deliberately separated from the Tutorial content which puts a focus on the mechanics of publishing an intelligent textbook using mkdocs and MicroSims.
Five Levels of Intelligent Textbooks
A classification system for intelligent textbooks inspired by SAE's autonomous vehicle levels. Provides a common vocabulary for educators, publishers, and policymakers to evaluate and discuss AI-enhanced learning materials. You can read our full academic paper on this topic here.
Learning Graphs
A directed acyclic graph (DAG) structure representing concepts and their prerequisite dependencies. Enables personalized learning paths, adaptive curriculum optimization, and knowledge state tracking. We also provide an entire intelligent textbook on Learning Graphs here
Adaptive Assessments
Dynamically adjusted assessments that efficiently map a student's knowledge state onto a learning graph. Determines which concepts are mastered and which are ready to learn next with minimal questions.
Bloom's Taxonomy
A framework for categorizing educational goals from basic recall to higher-order thinking. Guides the design of learning objectives, assessments, and instructional strategies across cognitive levels.
Learning Theory
Evidence-based principles from Dehaene's Four Pillars and "Make It Stick" research. Covers attention management, active engagement, spaced repetition, retrieval practice, and feedback systems.
Scaffolding
Structured support that helps learners gradually develop understanding of concepts and skills. Includes progressive complexity, guided examples, prerequisite reviews, and gradual reduction of support.
Instructional Design
The systematic process of creating effective, engaging educational experiences. Focuses on learner-centered design, clear outcomes, diverse pathways, and AI personalization.
Levels of Interactivity
A taxonomy of interactive elements from static text to advanced simulations. Helps authors choose appropriate interactivity levels for different learning objectives.
From Sage to Guide
The shifting role of teachers from knowledge transmitters to learning facilitators. Explores how AI-augmented textbooks transform the relationship between teachers, students, and content.
Open Educational Resources
Freely accessible, openly licensed educational materials that can be used, adapted, and redistributed. Covers OER repositories, Creative Commons licensing, and benefits for intelligent textbook creation.
Quality Metrics
Automated and manual methods for measuring intelligent textbook scope and content quality. Includes word counts, interactivity scores, concept coverage, and usage analytics.
Best Practices
Guidelines for creating intelligent textbooks with MkDocs Material and MicroSims. Covers repository structure, content organization, and technical implementation patterns.
Ontology Questions
100 domain-agnostic questions that drive the design of a textbook ontology. Helps define metadata, structure, and relationships for intelligent textbook systems.
Skill Dependency Diagram
Visual representation of dependencies between intelligent textbook creation skills. Shows the workflow phases from planning through content generation and deployment.