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2026 Spring Semester Senior Design Project

Project Name: Intelligent Textbooks for Electrical Engineering

Facilitator: Dan McCreary

Summary: This project will allow participating students to create a set of intelligent textbooks that embrace the modern use of generative AI. The output of this project will be the foundation of an intelligent textbook for each student who participates.  Students will gain a deep understanding of how to use Claude Code Skills that extract precise knowledge from large-language models and store this information in an open format on GitHub with a textbook rendered on GitHub pages.  No prior programming experience is required, but students must work with faculty that are subject-matter experts in a specific course.

Prerequisites

Each student must have:

  1. Their own personal public GitHub account.  A University of Minnesota GitHub account will not work
  2. Their own computer that they can install our book building tools (Windows WSL, MacOS or Linux).
  3. The $20/month Pro subscription to Claude Code (required)

Details

We propose that one of the groups in the EECD Senior seminar build a series of "Intelligent Textbooks" using generative AI.  Last year, we had two groups participate.  One group built MicroSims and did hands-on work extending the AI Racing League to use the updated Raspberry Pi 4 computers.

This year AI has become much more capable of building out not just single labs or writing small Python programs; it has also been used to generate entire Intelligent Textbooks, including:

  1. A modern and complete course description built around moving from knowledge to content generation (Bloom's Taxonomy) and giving it an objective quality assessment on a scale of 1 to 100.
  2. A detailed enumerated list of Learning Concepts for a specific course or subject matter
  3. A dependency graph of these Learning Concepts indicating the order in which they should be taught to achieve specific objectives
  4. A taxonomy of Learning Concepts allowing the classification and grouping of similar concepts
  5. A process of grouping Learning Concepts into chapters so the chapters flow naturally and are well balanced
  6. A process of generating content for each chapter including the extensive use of non-plain text elements (lists, tables, admonitions, diagrams, infographics, microsims and inline interactive assessments).
  7. Creation of high-quality MicroSims including the generation of metadata for search and reuse of MicroSims in search engines
  8. Generation of supplementary content such as a glossary of terms, FAQs and quizzes.
  9. Tools to visualize, edit, and certify  these Learning Concepts with Subject-Matter Experts
  10. Hooks to allow lesson plans to be customized to the needs of a project, group, or individual student

I propose to have each student in this project select one course at the University of Minnesota EECD department and work with a subject-matter expert to generate an intelligent textbook for this course. Note: Students will be required to use GitHub and GitHub pages to publish the content.  Although familiarity with GitHub is not required, it is necessary to complete these projects.  I will work with each student to make sure they can check in their code to GitHub.

Why This is Important

Today, many courses still use costly printed textbooks.  These textbooks are rigid and force a learner's learning order.  Static textbooks don't promote the use of adaptive learning.  Building many advanced search and indexing tools using generative AI is now possible by using Skills.  We can now objectively rate online courses on a scale of 1 to 5 and see if students prefer higher  level  textbooks.

What the Students Will Learn

After completing this project, students will be able to:

Remember

  • Identify the five levels of intelligent textbooks and their distinguishing characteristics
  • List the core components of a learning graph, including concepts, dependencies, and taxonomies
  • Recall key terminology related to generative AI-assisted instructional design
  • Name the essential tools in the intelligent textbook development stack (GitHub, MkDocs, Claude Code)

Understand

  • Explain how generative AI extracts and structures domain knowledge for educational purposes
  • Describe the relationship between learning concepts and their prerequisite dependencies
  • Summarize how Bloom's Taxonomy guides the progression from knowledge recall to content generation
  • Interpret dependency graphs to understand optimal learning sequences

Apply

  • Use Claude Code Skills to generate structured educational content from large language models
  • Implement MicroSims to create interactive simulations for complex engineering concepts
  • Configure GitHub Pages to publish and host intelligent textbooks
  • Execute the workflow for transforming learning concepts into organized chapters

Analyze

  • Analyze dependencies between learning concepts to determine optimal teaching sequences
  • Compare the pedagogical effectiveness of static textbooks versus intelligent textbooks
  • Differentiate between various content types (tables, diagrams, simulations) and their appropriate applications
  • Examine generated content for logical consistency and domain accuracy

Evaluate

  • Assess textbook quality using objective metrics on a scale of 1 to 100
  • Critique AI-generated content for technical accuracy with subject-matter experts
  • Judge the appropriateness of learning concept groupings within chapters
  • Appraise the balance and flow of chapter organization in a complete textbook

Create

  • Design a complete intelligent textbook structure for an electrical engineering course or other topic of their choosing
  • Develop custom MicroSims that facilitate understanding of domain-specific concepts
  • Construct comprehensive learning graphs with validated concept dependencies
  • Produce supplementary materials including glossaries, FAQs, and inline assessments
  • Author original educational content that integrates multiple media types and interactive elements

References

Academic Papers:

Five Levels of Intelligent Textbooks: https://osf.io/preprints/edarxiv/sh2yu_v1

MicroSims: A Framework for AI-Generated, Scalable Educational Simulations with Universal Embedding and Adaptive Learning Support: https://arxiv.org/abs/2511.19864

Sample Textbooks