AI Learning Platform
Notes on building AI-powered learning experiences.
29 posts · AI & LLMs
- 1
The Real Challenge: Why AI Tutors Are Not Teachers Yet
If you have ever asked a modern AI chatbot to "explain how the heart pumps blood" or "quiz me on Spanish verbs," you already know something surprising: it can…
- 2
What Instructional Design Actually Means
Imagine you hire two people to help your nephew pass a math exam. The first person knows math perfectly.
- 3
How Humans Learn: A Plain Tour of Memory
Before we can build software that helps a person learn, we have to understand the machine we are trying to help: the human brain.
- 4
Knowing vs Understanding vs Remembering a Month Later
Imagine two students. Both finish your AI tutor's full course on, say, fractions. Both clicked through every lesson, both passed every quiz at the end of each…
- 5
Cognitive Load Theory: Why Too Much at Once Fails
Have you ever sat through a lesson, nodded along the whole time, felt like you understood everything, and then realized an hour later that almost none of it…
- 6
Retrieval Practice: Why Testing Beats Re-reading
Imagine two students preparing for the same exam. Anita reads the chapter four times, highlighter in hand, until every sentence feels familiar.
- 7
Spaced Repetition: Beating the Forgetting Curve
Here is an uncomfortable truth about being human: most of what you learn today will be gone within a day or two.
- 8
Interleaving, Dual Coding & Desirable Difficulties
Here is one of the strangest truths in all of learning science: the study methods that feel the best are usually the ones that work the worst, and the methods…
- 9
Bloom's Taxonomy: The Ladder of Understanding
Imagine two students who both "studied" the water cycle. Ask the first one a question and they can recite "evaporation, condensation, precipitation" perfectly.
- 10
Mastery Learning and the 2-Sigma Problem
Imagine two students sitting in the same classroom. The teacher explains long division, the class moves on to fractions the next week, and one student quietly…
- 11
Zone of Proximal Development, Scaffolding & Worked Examples
Imagine you are teaching a child to ride a bike. Some things are already easy for them: sitting on the seat, holding the handlebars.
- 12
Feedback, Motivation & Metacognition
A tutor can pick the perfect next problem and still fail, because a learner who feels bored, judged, or hopeless simply closes the app.
- 13
Anatomy of a Great Lesson: The Layered Explanation
Imagine two teachers explaining the same idea. The first reads a perfect, precise paragraph from a textbook and moves on.
- 14
Analogies, Diagrams, Animations & Simulations
So far we have talked a lot about what a tutor teaches and when it reviews. This chapter is about how an idea gets into a learner's head in the first place —…
- 15
Practice Exercises and Adaptive Quizzes
Up to now we have mostly talked about how the tutor explains things. This chapter is about the opposite move: getting the learner to produce answers.
- 16
The Teach-It-Back Method and How AI Evaluates It
So far in this guide, the AI has mostly played the role of teacher : it explains, it quizzes, it gives hints. This chapter flips the script.
- 17
The Lesson-Scoped Tutor Chatbot
Imagine a learner is halfway through a lesson on fractions and gets confused. She wants to ask a question right now, in plain words, the way she'd lean over…
- 18
Learner Models: Teaching the Machine What the Student Knows
Imagine you hire a private tutor and, every single time they sit down with your child, they have completely forgotten the last lesson.
- 19
Knowledge Graphs and Curriculum Generation
Imagine you hand a smart but disorganized friend a giant pile of facts about Python programming and say, "Teach me to be a developer in four months." If they…
- 20
Sequencing: What Comes Next and When to Review
Imagine a learner finishes a problem. Behind the scenes, your platform has to make a quiet, constant decision: what should this person see next?
- 21
Spaced Repetition Algorithms in Practice (SM-2, FSRS)
In an earlier chapter you met the forgetting curve — the well-documented fact that we lose most of what we learn within a day unless we revisit it.
- 22
Finding and Repairing Weak Areas
A good human tutor does something that feels almost magical: they notice the exact spot where you go wrong, figure out why you went wrong, and then fix that…
- 23
Where LLMs Fit — and Where They Fail
By now you have learned a great deal about how people learn and how good tutors teach. In this chapter we meet the new tool everyone is excited about: the…
- 24
Turning a PDF into a Course: RAG for Learning
Imagine a learner drops a 300-page textbook, a messy stack of lecture notes, or a single scanned PDF onto your platform and says: "Teach me this." This chapter…
- 25
Keeping the AI Accurate and Pedagogically Sound
A large language model (LLM) — the kind of artificial intelligence that powers a chatbot — is fluent, fast, and confident.
- 26
Measuring Real Learning: Metrics That Matter
Imagine you built an AI tutor. The dashboard glows green: thousands of lessons completed, streaks climbing, hours logged. The team celebrates.
- 27
Pick a Niche: Why "Teach Everything" Fails
When you first imagine building an AI learning platform, the tempting dream is "a tutor that can teach anything to anyone ." It sounds generous and ambitious.
- 28
Business Model and the Moat
You have spent many chapters learning how to build a tutor that actually teaches. This chapter asks a different, equally important question: how does this…
- 29
Building Trust and the Long-Term Retention Promise
We have reached the end of the journey. Across this guide you learned how human memory actually works, how to design lessons, how to motivate learners, how to…