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About This Book

Welcome from Bloom

Welcome! I'm Bloom, your guide through the learning sciences. This book is a bit unusual — it teaches you the research behind how people learn and shows you how to build intelligent textbooks that put that research into practice. Whether you're an instructional designer, an educator, or just someone curious about how learning really works, there's something here for you. Let's build a mental model.

Why This Intelligent Textbook

Generative AI has made it possible for a single author or a small team to produce interactive, personalized learning experiences that once required publishing-house budgets. But capability without principle produces noise — AI can generate shallow, cognitively overloaded content just as easily as it can generate excellent material. The learning sciences provide the principled frame that separates the two. And yet, most educators have never had a practical, hands-on introduction to the field.

In the United States (2025):

  • The U.S. Bureau of Labor Statistics projects 8% growth in postsecondary instructional design and educational technology roles through 2032, well above the average for all occupations1
  • Only 27% of college faculty report formal training in evidence-based teaching practices, despite decades of research on what works2
  • The Open Education Network reports that the average undergraduate spends $1,240 per year on textbooks and course materials — a barrier that disproportionately affects first-generation and low-income students3
  • A 2024 EDUCAUSE survey found that 56% of instructors are already experimenting with generative AI for course content, but fewer than 20% ground their use in learning-sciences research4

Worldwide:

  • UNESCO's 2023 Global Education Monitoring Report estimates that 244 million children and youth are out of school, and millions more attend without learning — a crisis that scalable, high-quality open educational resources can help address5
  • The global e-learning market is projected to reach $645 billion by 2030, yet the majority of digital courseware replicates the page-turning model of print textbooks without applying what we know about memory, motivation, or transfer6
  • The OECD's PISA 2022 results show that students in countries with strong formative-feedback cultures significantly outperform peers in systems that rely primarily on summative testing7

These numbers describe a gap: the tools to build interactive learning experiences are more powerful than ever, but the research base that should guide their design remains locked inside academic journals and graduate seminars. This textbook exists to close that gap.

This book takes a fundamentally different approach. It is built on a learning graph of 230 interconnected concepts organized into prerequisite chains across 15 chapters. Concepts are introduced in the order their dependencies are established, so understanding builds naturally from chapter to chapter. Throughout the book you will find 62 interactive MicroSims — browser-based simulations that let you manipulate models, explore causal loops, and discover principles through experimentation rather than memorization. The entire textbook is open source and free — no paywalls, no access codes, no expensive annual editions. And because the book teaches you how it was built, every technique you encounter is one you can replicate for your own subject.

How to Use This Book

This textbook is designed for self-paced study. Each chapter builds on previous material, so reading in order is recommended. The book includes:

  • 15 Chapters covering the Seven Domains of Learning Sciences, cognitive architecture, intelligent textbook design, MicroSims, pedagogical mascots, graphic novels, and AI agent skills
  • 62 Interactive MicroSims embedded throughout — browser-based simulations you can manipulate to explore concepts
  • Annotated References linking to Wikipedia and authoritative sources
  • Glossary with definitions for every key concept
  • FAQ with common questions and answers
  • Learning Graph visualizing 230 concept dependencies across the curriculum
  • Search available from any page using the search bar

The Learning Graph visualizes how concepts connect across chapters. If you want to explore non-linearly or check prerequisites for a specific topic, start there.

About the Author

Dan McCreary is a semi-retired AI researcher, solution architect, and educator who has spent more than three decades helping Fortune 100 organizations reason over massive datasets. At Optum he founded the Generative AI Center of Excellence and led the team that built one of the world's largest healthcare knowledge graphs — spanning over 25 billion vertices — to unify member, provider, and patient insights. Dan's deep background in knowledge representation and systems thinking underpins the precise learning graphs and intelligent textbook workflows used throughout this course.

He is the co-author of Making Sense of NoSQL (Manning Publications), the founding chair of the NoSQL Now! conference, and a frequent keynote speaker on semantic search, ontology strategy, and AI hardware. Beyond industry, Dan has mentored students as a STEM volunteer since 2014 and now applies the same rigor to building open educational resources. You can visit the Intelligent Textbooks Case Studies to see over 70 textbooks that Dan has created or co-created with other authors.

Selected Credentials

  • B.A. in Physics and Computer Science from Carleton College
  • M.S.E.E. from the University of Minnesota
  • MBA coursework at the University of St. Thomas
  • Patent holder in semantic search and ontology management techniques
  • Advocate for large-scale Enterprise Knowledge Graph adoption across healthcare and education
  • Long-time promoter of accessible, low-cost AI-powered learning experiences

How to Cite This Book

If you reference this textbook in academic work, curriculum proposals, lesson plans, or other publications, please use one of the following citation formats.

APA (7th edition)

McCreary, D. (2026). Learning Sciences for Intelligent Textbook Design. https://dmccreary.github.io/learning-sciences/

Chicago (17th edition)

McCreary, Dan. 2026. Learning Sciences for Intelligent Textbook Design. https://dmccreary.github.io/learning-sciences/.

MLA (9th edition)

McCreary, Dan. Learning Sciences for Intelligent Textbook Design. 2026, dmccreary.github.io/learning-sciences/.

BibTeX

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@book{mccreary2026learningsciences,
  title     = {Learning Sciences for Intelligent Textbook Design},
  author    = {McCreary, Dan},
  year      = {2026},
  url       = {https://dmccreary.github.io/learning-sciences/},
  note      = {Interactive intelligent textbook}
}

To cite a specific chapter, append the chapter number and title — for example:

McCreary, D. (2026). Chapter 1: Foundations of Learning Sciences. In Learning Sciences for Intelligent Textbook Design. https://dmccreary.github.io/learning-sciences/chapters/01-foundations/

License

This work is released under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0). You are free to share and adapt the material for non-commercial purposes as long as you give appropriate credit and share your adaptations under the same license.

References

  1. U.S. Bureau of Labor Statistics. (2024). Occupational Outlook Handbook: Instructional Coordinators and Educational Technologists. https://www.bls.gov/ooh/education-training-and-library/instructional-coordinators.htm ↩

  2. Stains, M., et al. (2018). Anatomy of STEM teaching in North American universities. Science, 359(6383), 1468–1470. https://en.wikipedia.org/wiki/Evidence-based_education ↩

  3. Open Education Network. (2024). Textbook Affordability Report. https://open.umn.edu/opentextbooks ↩

  4. EDUCAUSE. (2024). 2024 EDUCAUSE Horizon Report: Teaching and Learning Edition. https://www.educause.edu/horizon-report ↩

  5. UNESCO. (2023). Global Education Monitoring Report 2023: Technology in Education. https://en.wikipedia.org/wiki/Global_Education_Monitoring_Report ↩

  6. Research and Markets. (2024). Global E-Learning Market Outlook 2024–2030. https://en.wikipedia.org/wiki/E-learning ↩

  7. OECD. (2023). PISA 2022 Results (Volume I): The State of Learning and Equity in Education. https://en.wikipedia.org/wiki/Programme_for_International_Student_Assessment ↩