AKMP is an open, domain-independent framework for learning unfamiliar fields faster and developing useful practical judgment.
AKMP reduces avoidable learning time by giving every subject a deliberate progression:
- Map the Knowledge before getting lost in details.
- Build Understanding around the concepts that unlock the field.
- Practice Real Situations before reading the professional approach.
It is designed for fields where definitions are not enough: engineering, medicine, finance, leadership, operations, security, and more.
AKMP does not promise instant mastery. It accelerates orientation, focused understanding, exposure to important problems, and the development of practical judgment.
| Stage | Learner activity | Main output |
|---|---|---|
| 1. Map the Knowledge | Explore a progressive glossary organized by category and difficulty | Orientation and a complete learning map |
| 2. Build Understanding | Study mental models, relationships, examples, mistakes, and debugging approaches | Connected, usable knowledge |
| 3. Practice Real Situations | Respond to realistic roles, pressure, incomplete evidence, and tradeoffs | Practical judgment and decision-making |
After each practice case:
Answer -> Evaluate -> Identify gaps -> Refine the map -> Practice again
Many learning paths begin with isolated lessons and reveal the shape of the field slowly.
AKMP changes the order:
- Map first: see what exists and how the field is organized.
- Go deep selectively: focus effort on concepts that unlock other concepts.
- Connect ideas: learn systems and relationships instead of disconnected definitions.
- Practice before answers: make decisions without solution-shaped hints.
- Use gaps as direction: evaluation determines what to study or practice next.
The result is less noise, less accidental repetition, and a shorter path to useful competence.
Create a progressive glossary covering:
- terminology
- tools and systems
- roles and workflows
- patterns and practices
- common problems and failure modes
Organize it by:
Beginner/Basics -> Intermediate -> Advanced/Future Reference
The goal is not immediate memorization. It is to make the field visible.
Turn the important glossary terms into connected mental models.
Each deep-understanding note explains:
- what the concept is
- why it exists
- how it works
- what problem it solves
- how it connects to other concepts
- common mistakes and failure modes
- how to apply or debug it
Read the Deep Understanding System →
Place the learner inside realistic environments:
- a role with responsibility
- a company, institution, team, or client
- real operational, financial, safety, or human impact
- incomplete but meaningful evidence
- pressure, uncertainty, and tradeoffs
- a natural request to build, decide, investigate, communicate, or fix
The learner-facing case contains no hints, self-check list, rubric, or answer key. Evaluation happens only after the learner responds.
- Open a course under
courses/. - Read its glossary in order.
- Continue through deep-understanding notes.
- Answer practice cases without opening evaluator guides.
- Ask an AI, mentor, or instructor to evaluate your response.
- Read the Course Creation Guide.
- Copy the files from
templates/. - Build a progressive glossary.
- Select the concepts that deserve deep explanations.
- Write realistic practice cases and separate evaluator guides.
- Read the Evaluation Guide.
- Keep learner cases separate from evaluator guides.
- Evaluate reasoning, safety, evidence, communication, and verification.
- Use the AI Evaluator Prompt when appropriate.
The first demonstration is Docker Fundamentals.
It intentionally stays small so the framework remains easy to inspect:
Docker Glossary
-> Container Mental Model
-> Image vs Container
-> Ports and Volumes
-> Deployment and Debugging Cases
AKMP-Framework/
├── assets/ # README diagrams
├── framework/ # The AKMP method
│ ├── 01-glossary-system.md
│ ├── 02-deep-understanding-system.md
│ ├── 03-practice-system.md
│ ├── case-writing-guide.md
│ └── evaluation-guide.md
├── templates/ # Reusable course-authoring files
├── courses/
│ └── docker-fundamentals/ # Small reference implementation
├── docs/ # AI, Obsidian, and author guides
├── examples/ # Links to representative examples
└── prompts/ # AI evaluator prompt
AI can support AKMP as:
- glossary researcher
- concept explainer
- scenario writer
- interviewer or incident lead
- response evaluator
- gap detector
- follow-up case generator
AI should not reveal the evaluator guide before the learner answers.
Open ChatGPT, Claude, DeepSeek, Gemini, or another LLM that can read web links, then send one message:
Read this repository and understand the AKMP learning workflow:
https://github.com/mito0o852/AKMP-Framework
I want to learn: [TOPIC]
My current background: [WHAT YOU ALREADY KNOW]
My target: [THE LEVEL OR CAPABILITY YOU WANT]
Use AKMP to teach me. Start with Stage 1: Map the Knowledge.
Organize the glossary from Beginner/Basics to Intermediate and Advanced/Future Reference.
Do not move to the next stage until I tell you I am ready.
Example:
Read this repository and understand the AKMP learning workflow:
https://github.com/mito0o852/AKMP-Framework
I want to learn: Monte Carlo methods.
My current background: I am a backend, AI, and ML engineer with basic probability knowledge.
My target: Build a strong practical understanding and apply Monte Carlo to finance and ML.
Use AKMP to teach me. Start with Stage 1: Map the Knowledge.
Organize the glossary from Beginner/Basics to Intermediate and Advanced/Future Reference.
Do not move to the next stage until I tell you I am ready.
If the model cannot open GitHub links, give it the repository README and the three files under framework/ directly.
- Map broadly before studying deeply.
- Organize difficulty progressively.
- Prefer mental models over memorization.
- Practice before seeing answers.
- Make cases feel like real life, not exams.
- Evaluate reasoning, not keyword repetition.
- Convert mistakes into the next learning step.
- Treat advanced concepts as future hooks, not pressure.
Contributions are welcome, including:
- framework refinements
- templates
- glossary maps
- deep-understanding notes
- realistic practice cases
- evaluator guides
- complete courses
Please read CONTRIBUTING.md before submitting changes.
AKMP is in early development. The framework and first demonstration course are available, but the method is expected to improve through real learner feedback and contributed courses.
Released under the MIT License.