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Course Description

Title: Designing and Building Intelligent Textbooks: A Step-by-Step Guide

Summary: This course is a 10-week college level course for students that would like to create intelligent textbooks using generative AI. It assumes minimal background in education theory and programming. At the end of this course students will be able to generate their own level-2 interactive textbooks hosted for free on GitHub Pages.

Intended Audience: Our audience is primarily college students that are curious about the future of education. Knowledge of how to create prompts is helpful but not required. Knowledge of how to type commands into a command line interface is also helpful.

Prerequisites: - Students must have a strong understanding of keyboarding skills, how to use a computer mouse and how web browsers work including the concepts of bookmarks and clicking on hyper-text links. - Students should be familiar with the components of a textbook and their value in a course. - Students should be able to use a web browser and install software on their local computer. - Students will be required to install GitHub and Visual Studio Code on their computer.

Learning Objectives (Using Bloom's 2005 Taxonomy):

Remember (Recall facts and basic concepts)

Students will be able to: - Define terms such as Intelligent Textbook, Learning Concepts, Concept Graphs, Learning Graphs, Content, MicroSim - List Five Levels of Intelligent Textbooks - What protected student data is and why it is required for personalization - Identify the level of any textbook based on its interactive characteristics

Understand (Explain ideas or concepts)

Students will be able to: - Explain the five levels of intelligent textbooks - Explain why levels 3-5 must protect student privacy - Explain the steps in generating a textbooks - Explain the purpose of a learning graph as the guardrails of content generation - Explain why level 2 textbooks will be available at low cost to students around the world - Explain the difference between Concept nodes in a graph and Content nodes - Summarize the overall approach to using generative AI to generate content and simulations - Summarize the challenges of breaking up walls of text - Compare and contrast static textbooks with different levels of intelligent textbooks

Apply (Use information in new situations)

Students will be able to: - Solve the problems of organizing concept learning dependencies - Demonstrate the use of generative AI to create Concepts, Chapters and MicroSims - Use tools such as GitHub, GitHub Pages, VSCode, mkdocs, the material theme, Python and shell scripts

Analyze (Draw connections among ideas)

Students will be able to: - Analyze a learning graph for quality, completeness and consistency - Differentiate between the different type of textbooks such as history, math, science and logic - Examine the effectiveness of interactivity at engauging students

Evaluate (Justify a decision or course of action)

Students will be able to:

  • Evaluate a given intelligent textbook for completeness and quality metrics
  • Critique the ability of large-language models (LLMs) to generate quality content and code
  • Assess the quality of concepts, content and learning aids such as a glossary of terms, FAQs and quizzes

Create (Produce original work)

Students will be able to: - Design a learning graph including foundational prerequisite concepts, outcome concepts and capstone project nodes - Develop and test new interactive MicroSims using generative - Construct full textbooks working with subject-matter experts

Concepts Covered

  1. Foundation Concepts: Markdown, Build process, Textbook Structure, MicroSims, Instructional Design
  2. Core Concepts: Mkdocs, Material Theme, LLMs, Generative AI, Learning Graph
  3. Advanced Concepts: xAPI, Learning Record Store, Reinforcement Learning

Concepts Explicitly Excluded

  • Specific APIs for Learning Management Systems (Canvas) - varies based on the learning organization
  • Advanced JavaScript variations like TypeScript - too deep into computer science
  • Libraries to support specific types of content like circuits, chemistry or maps - to specific to a field
  • Advanced A/B testing to measure the effectiveness of interactive content
  • Reinforcement learning
  • Recommendation engines

Sample Capstone Projects

  1. Project Name: Custom Textbook from Scratch
  2. Description: Each student will build a custom textbook using only content they create
  3. Skills Demonstrated: Ability to start with a template and add content

  4. Deliverables: A working textbook on GitHub pages

  5. Project Name: Textbook Review

  6. Description: Review an existing textbook for
  7. Skills Demonstrated: Run metrics, look for consistency and completeness

  8. Deliverables: Written evaluation of a textbook including pros and cons

  9. Project Name: Textbook Upgrade

  10. Description: Work with an existing textbook and upgrade its level
  11. Skills Demonstrated: Run metrics, look for consistency and completeness
  12. Deliverables: Written evaluation of a textbook before and after upgrade

Interactive Elements (MicroSims)

  • Estimated Number of MicroSims: 5-10
  • Types of Simulations: Infographic, Interactive visualizations, Problem-solving scenarios, Concept explorers, graph traversal
  • Primary Library: p5.js, vis-network.js

Assessment Strategy

  • Formative Assessments: Students will take self assessment quizzes throughout the course to measure their own learning
  • Summative Assessments: The final assessment will be their presentation of their book to the group or instructor
  • Self-Assessment Tools: Interactive multiple-choice Quizzes, short essays, reflection prompts

Learning Path Structure

  • Estimated Chapters: 12
  • Estimated Total Concepts: 200
  • Estimated Completion Time: 100
  • Suggested Pace: 6-10 hours per week, two hour-long group discussion, per week

Real-World Applications

After completing this textbook, students will be able to: - Assess the quality of a course and give it an overall AI level score of 1-5 - Understand the tools used to create an intelligent textbook and how each tool works - Create a new textbook using a template - Understand the dynamics of the publishing industry including copyright law and creative commons licensing - Be able to help improve the interactive quality of a textbook

Success Metrics

Students will know they have mastered the material when they can: - Quickly assess the level of a textbook - Install the - Run a quality metric report on a textbook that counts the number of chapters, sections, paragraphs, words, concepts etc. - Create a high-quality report that describes a textbook

Technical Requirements

  • Software/Tools Needed: GitHub Account, Visual Studio Code (VSCode), Generative AI Tool (Anthropic Claude, ChatGPT)
  • Hardware Requirements: Local computer should have a minimum of 8GB RAM
  • Internet Connectivity: Required

Concept Metrics