Turn any AI assistant into a structured tutor with spaced repetition, mastery gates, and proof-of-learning.
I needed to learn an entirely new field from scratch — robotics, world models, embodied intelligence. Not casually. Deeply enough to hold real conversations with experts.
YouTube and articles didn't work. Passive consumption, no feedback loop, no way to know if I actually understood anything. So I built a system that forces active recall, blocks advancement until I prove understanding, and tracks every concept with spaced repetition.
3 sessions in: quiz scores climbing, categorization accuracy 51.5% → 86%, founder-level explanations scoring 9/10. The system works. Fork it and learn anything.
v4 adds persona-aware teaching — the engine adapts its vocabulary, analogies, and difficulty to who you are, and threads your learning goal through every question.
Building & Learning in Public Currently learning: Spatial AI | Day 5/50 | 38 terms tracked | 5 sessions complete
╔══════════════════════════════════════════════╗
║ LEARNING SESSION RECEIPT ║
╠══════════════════════════════════════════════╣
║ Session: #5 (Day 4) ║
║ Mode: Standard ║
║ Duration: ~90 min ║
║ Date: 2026-03-20 ║
║ Streak: 1 day ║
╠══════════════════════════════════════════════╣
║ SM-2 Quiz: 7.2/10 (FIRST PASS ✓) ║
║ Strongest: World Model (9/10) ║
║ Strongest: Cross-Embodiment (9/10) ║
║ Investor Thesis: 3 companies analyzed ║
║ Community: World Labs hackathon observed ║
╠══════════════════════════════════════════════╣
║ Terms Known: 0 / 38 ║
║ Terms Learning: 38 ║
║ Overall: 8% complete (4/50 days) ║
╠══════════════════════════════════════════════╣
║ ★ AMI Labs / JEPA explanation: 8/10 ║
║ 🔧 Focus: Diffusion Policy (3/10) ║
║ Next: Day 5 — Deeper into the Stack ║
╚══════════════════════════════════════════════╝
Track your learning progress with a live Chart.js dashboard:
- Mastery curve (known vs. learning terms over time)
- Velocity trend (learning speed across sessions)
- Confidence calibration (over/under-confident detection)
- Pre-test to post-test delta
Deploy your own via GitHub Pages (Settings → Pages → main branch).
graph LR
subgraph "Foundations"
WM[World Model] -->|enables| STR[Sim-to-Real]
EAI[Embodied AI] -->|requires| P[Perception]
P -->|feeds| PL[Planning]
PL -->|drives| C[Control]
FM[Foundation Model] -->|type of| VLA
end
subgraph "Training"
RL[Reinforcement Learning] -->|variant| IL[Imitation Learning]
IL -->|uses| TEL[Teleoperation]
IL -->|evolves to| DP[Diffusion Policy]
end
subgraph "Hardware"
MAN[Manipulation] -->|uses| EE[End Effector]
LOC[Locomotion] -->|constrained by| DOF[Degrees of Freedom]
MORPH[Morphology] -->|defines| EG[Embodiment Gap]
end
subgraph "Infrastructure"
DT[Digital Twin] -->|bridges| STG[Sim-to-Real Gap]
GS[Gaussian Splatting] -->|replaced| NERF[NeRF]
end
WM -.->|analogous to| FM
VLA -->|combines| P & PL & C
CET[Cross-Embodiment Transfer] -.->|solves| EG
DSA[Dual-System Architecture] -->|combines| PL & C
git clone https://github.com/hanselhansel/ai-learning-engine.git my-learning-project
cd my-learning-project- Clone the repo (above)
- Generate a curriculum: Run
/generate-curriculumand answer 8 questions — or copy an example fromexamples/and customize - Configure: Copy
CURRICULUM-CONFIG-TEMPLATE.mdtoCURRICULUM-CONFIG.mdand fill in your learner bridges and preferences - Install skills: Follow your platform's setup guide (see Platform Setup)
- Start learning: Type
/learn— type/learn-endto save and exit early
See SETUP-GUIDE.md for the full walkthrough.
- Pre-testing — test before teaching for stronger memory traces
- SM-2 spaced repetition — algorithmic review scheduling with ease factors
- Socratic method — ask-then-reveal, never lecture-first
- Interleaved practice — cross-session concept mixing
- Mastery-based progression — gates with escape valve (never blocks indefinitely)
- Portfolio-grade exercises — every drill produces a real artifact
- Confidence calibration — metacognition tracking (over/under-confident detection)
- 7-concept cap — depth over breadth per session
- Weekly synthesis — integration challenge every 5th session
- Config-driven — CURRICULUM-CONFIG.md separates content from engine
- Learning velocity dashboard — trend tracking across sessions
- Flex sessions — 5 modes: Micro (5m), Quick (15m), Standard (60m), Deep (120m), Synthesis
- Content compression — skip/compress mastered concepts
- Difficulty scaling — harder when ahead, easier when struggling
- Velocity-based pacing — adjust session based on learning speed
- Spaced exercise repetition — SM-2 for drills, not just terms
- Teach-back mode — explain to a colleague for 90% retention
- Concept linking — relationship graph between concepts
- Mid-lesson retrieval — quick recall prompts during Socratic lessons
- Growth reflection — revisit Day 1 artifacts to see progress
- Auto-generated sessions — web research for fresh content
- Warm-start migration — preserve progress when upgrading
- Curriculum generator — guided "what do you want to learn?" skill
- Session variety — debate, case study, deep-dive, reverse quiz
- Analytics Dashboard — HTML with Chart.js (mastery curve, velocity, confidence)
- Session Summary Card — workout-receipt per session
- Peer Teaching Simulation — skeptical colleague with follow-up questions
- Concept of the Day — surprise retrieval at session open
- Curriculum Gallery — pre-built examples (finance, PM, ML, spatial AI)
- Micro-Review Mode — 5-min coffee-break review
- Mermaid Concept Map — auto-generated visual knowledge web
- Learning Streak & Milestones — don't-break-the-chain motivation
- Learner Personas — 5 teaching styles: Elementary, Teen, Adult Beginner, Professional, Expert
- Objective Threading — 6 goal templates thread your WHY through every question
- Adaptive Refinement — auto-detects if teaching level needs adjustment
- First-Run Detection — auto-redirects new users to curriculum setup
- Interactive Placement Test — 3-question diagnostic auto-assigns your persona
- Session Tone Preview — see how the engine will teach you before starting
- Multi-Objective Blending — weighted goal mixing (60% career + 40% investing)
- Portfolio Auto-Generator — objective-aware portfolio showcase page
- Cross-Curriculum Transfer Credits — SM-2 mastery carries across curricula via canonical IDs
- Weekly Learning Digest — weekly summary with upcoming topics and spicy questions
| Mode | Time | Best For |
|---|---|---|
| Micro | 5 min | Coffee break, quick SM-2 review |
| Quick | 15-20 min | Morning warmup, light practice |
| Standard | 50-65 min | Full learning cycle (default) |
| Deep Dive | 90-120+ min | Teach-back, concept linking, deep exploration |
| Synthesis | 60-75 min | Every 5th session, integration challenge |
| Block | Micro | Quick | Standard | Deep | Synthesis |
|---|---|---|---|---|---|
| Concept of the Day | x | x | x | x | x |
| Pre-Test | - | - | x | x | - |
| SM-2 Quiz | x | x | x | x | x |
| Socratic Lesson | - | x (1-2) | x (5-7) | x (7) | - |
| Interleaved Practice | - | - | x | x | x |
| Portfolio Exercise | - | - | x | x | x |
| Teach-Back | - | - | - | x | - |
| Concept Linking | - | - | - | x | x |
| Mastery Check | - | - | x | x | x |
Pre-built curricula ready to fork:
| Curriculum | Duration | Sessions | Target Audience |
|---|---|---|---|
| Spatial AI | 10 weeks | ~50 | Technical PM, BD, investors |
| Financial Modeling | 4 weeks | ~20 | Business professionals, analysts |
| Product Management | 6 weeks | ~30 | Engineers transitioning to PM |
| Machine Learning | 8 weeks | ~40 | Software engineers learning ML |
skills/learn/
├── SKILL.md # ~170 lines: session flow PROCESS only
├── references/
│ ├── sm2-algorithm.md # SM-2 rules + canonical IDs + transfer credits
│ ├── flex-session-blocks.md # 5 mode definitions, block composition table
│ ├── compression-logic.md # Velocity thresholds, compression rules
│ ├── teach-back-protocol.md # Peer simulation, scoring rubric
│ ├── mastery-gates.md # Gate logic, streaks, milestones
│ ├── concept-linking.md # Relationship tracking, Mermaid generation
│ ├── learner-personas.md # 5 persona definitions with teaching rules (NEW)
│ ├── objective-threading.md # 6 goal templates + multi-objective blending (NEW)
│ ├── adaptive-refinement.md # Auto-detection + adjustment rules (NEW)
│ └── placement-test.md # 3-question diagnostic placement (NEW)
└── templates/
├── session-summary-card.md # Receipt-style session summary
├── analytics-dashboard.html# Chart.js dashboard
├── session-state-schema.md # v4 state field definitions
├── portfolio-showcase.html # Objective-aware portfolio page (NEW)
└── weekly-digest.md # Weekly learning digest template (NEW)
SKILL.md says "Read references/sm2-algorithm.md for update rules" — Claude loads on demand. Quick/Micro modes never load teach-back or concept-linking files. This saves ~40% tokens vs monolithic skills.
You: /learn
AI: [reads CURRICULUM-CONFIG.md → SESSION-STATE.md → CURRICULUM.md]
AI: "What mode? Micro (5m) | Quick (15m) | Standard (60m) | Deep (120m)"
You: Standard
AI: [reads learner persona: Professional, goal: Career/Job Prep]
=== Session 12 of ~50: How Robots See (Perception) ===
--- CONCEPT OF THE DAY ---
Quick recall: What's the difference between a world model and a scene graph?
--- PRE-TEST (5 min) ---
Q1: What does a LiDAR sensor measure?
You: [attempt — probably wrong, and that's good]
"We'll come back to this during the lesson."
--- SPACED REPETITION QUIZ (10 min, SM-2) ---
Term 1: What is a VLA?
You: [answer]
Confidence (1-5): 4
Result: Correct. Ease: 2.7 → 2.8. Next review: 8 days.
--- SOCRATIC LESSON (25-30 min, max 7 concepts) ---
"Given your investing background — how would a robot 'see' a cluttered kitchen counter?
What sensors would it need, and what's the unit economics of those sensors?"
You: [reason about it, drawing on investment frameworks]
"Good analysis. From a job interview angle — here's how you'd explain sensor fusion..."
[Connects to your pre-test answer]
--- INTERLEAVED PRACTICE (10 min) ---
Problem mixing today's Perception with last week's VLA architecture...
--- PORTFOLIO EXERCISE (10-15 min) ---
"Draft a LinkedIn post explaining why LiDAR + cameras > cameras alone"
[Saved to portfolio/]
--- MASTERY CHECK ---
Score: 7.5/10. Above threshold. Advancing to Session 13.
--- SESSION SUMMARY CARD ---
┌─────────────────────────────────┐
│ Session 12 Complete │
│ Mode: Standard (58 min) │
│ New concepts: 5/7 │
│ SM-2 reviews: 8 (6 correct) │
│ Streak: 4 days │
│ Velocity: 1.2x (above avg) │
└─────────────────────────────────┘
[Updates SM-2 tracker, velocity dashboard, commits to git]
This engine implements techniques from learning science research:
- Pretesting effect — errors before learning create stronger memory traces
- Spaced repetition (SM-2) — algorithmic scheduling beats cramming by 2-3x
- Socratic method — generation before instruction improves retention by 30-50%
- Interleaved practice — mixing topics beats blocked practice for long-term retention
- Teach-back — explaining to others achieves 90% retention (vs 10% lecture)
- Confidence calibration — metacognitive awareness improves learning efficiency
- Testing effect — retrieval practice is more effective than re-reading
- Zone of Proximal Development — teaching at the right difficulty level (not too easy, not too hard)
- Expertise reversal effect — scaffolding that helps beginners can HARM experts
- Goal-oriented learning — threading objectives through content increases engagement and retention
| Platform | Skill Format | Auto-Trigger | Setup Guide |
|---|---|---|---|
| Claude Code / Cowork | SKILL.md in .claude/skills/ |
Yes (/learn) |
platforms/claude-code/ |
| OpenAI Codex | SKILL.md in .agents/skills/ |
Yes (/learn) |
platforms/openai-codex/ |
| Cursor AI | .mdc rules or .cursorrules | Partial (must reference files) | platforms/cursor-ai/ |
| Any AI assistant | Read the SKILL.md as instructions | Manual (paste the protocol) | Copy SKILL.md contents into your prompt |
The core methodology is plain markdown. The skills are automation wrappers. You can use this with ChatGPT, Gemini, or any LLM by manually following the SKILL.md protocol.
Pair this with a voice dictation tool for conversational learning:
- Quiz answers: Explain terms out loud. Builds verbal recall for interviews.
- Socratic responses: Reason verbally before the AI reveals. Activates different memory pathways.
- Portfolio exercises: Dictate your LinkedIn posts and memos at 170+ WPM.
Recommended: WisprFlow (macOS), Apple Dictation (free), or Whisper (self-hosted).
Q: Do I need Claude Code specifically? No. The methodology is plain markdown. Claude Code and OpenAI Codex have the best automation, but any LLM works.
Q: How long does it take to set up?
Generate a curriculum: 10 minutes with /generate-curriculum. Configure: 5 minutes. Start learning: immediately.
Q: What's new in v3? Flex sessions (5 time modes), content compression, difficulty scaling, teach-back with peer simulation, concept linking with Mermaid maps, session summary cards, analytics dashboard, streak tracking, milestones, auto-generated sessions, curriculum generator, and curriculum gallery. See MIGRATION-GUIDE.md for full details.
Q: Can I use this for non-technical topics? Yes. Finance, law, history, language, music theory — any structured learning works.
Q: What if I miss a day? The engine uses session numbers, not calendar days. Pick up where you left off. SM-2 recalculates review priorities automatically.
Q: Can a kid use this? Yes. The engine has 5 persona levels from Elementary (ages 8-12) to Expert. An Elementary learner gets game analogies, gentle hints, and simple exercises. A Professional gets full jargon, business cases, and hard Socratic questions. Set your persona in CURRICULUM-CONFIG.md or let the placement test auto-assign it.
Q: How does it know my level? Three ways: (1) Self-assessment during onboarding, (2) A 3-question placement test that evaluates your actual responses, and (3) Adaptive refinement that monitors your session performance and suggests adjustments if your level changes.
Q: What's new in v4? Persona-aware teaching (5 levels), goal-threaded learning (6 objectives), adaptive refinement, interactive placement test, multi-objective blending, portfolio auto-generator, cross-curriculum transfer credits, and weekly learning digests. The engine now adapts to WHO you are, not just WHAT you're learning. See MIGRATION-GUIDE.md.
Q: How do I upgrade from v1 or v2? See MIGRATION-GUIDE.md. Your progress is preserved — terminology, position, and session history all carry forward.
your-curriculum/
├── CURRICULUM.md # Master lesson plan (read-only reference)
├── CURRICULUM-CONFIG.md # Your personalized settings + learner profile + objectives
├── SESSION-STATE.md # Progress tracker (auto-updated)
├── progress.md # Daily learning log
├── COMMUNITIES.md # Community engagement tracker
├── portfolio/ # Portfolio artifacts + analytics dashboard + showcase
├── sessions/
│ ├── summaries/ # Per-session receipt cards
│ ├── digests/ # Weekly learning digests
│ └── archive/ # Archived state history
├── assessments/ # Phase gate results
├── swot/ # SWOT self-assessments
└── build-project/ # Portfolio project files
PRs welcome. Areas where help is needed:
- Additional platform adapters (Windsurf, Aider, etc.)
- Example curricula for different topics
- Improvements to the SM-2 algorithm or confidence calibration
- New reference modules for the hub-and-spoke architecture
- Translations of the template files
MIT
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