Learnability as a Service #001 — Design Your Personal Learning Journey with AI

AIYING | LEARNABILITY AS A SERVICE #001

Learnability as a Service: Design Your Personal Learning Journey with AI

Tell AI where you are, where you want to go, and what you want to accomplish along the way.

From AI Awareness to Accomplishment

Learning Has a Navigation Problem

Suppose you want to learn AI engineering.

You search for courses, watch videos, collect articles, and bookmark tutorials.

Soon you have an abundance of learning resources. But one question remains:

Where should I begin, and what should I do next?

The problem is not always a shortage of information. Sometimes it is the difficulty of navigating from current capability to desired accomplishment.

This suggests a different way of using generative AI.

1. PO: GenAI Is a Learning Navigation Service

PO: Learning should work like requesting a ride.

Specify your current location. Specify your destination. Let the system propose a route.

In mobility, the starting point is a physical location. In learning, it is your present capability.

In mobility, the destination is a place. In learning, it is something you want to understand, create, perform, or accomplish.

The route is not a road. It is a sequence of learning activities, experiments, feedback, and practical milestones.

Unlike a navigation application, an AI assistant cannot automatically know your true capability or guarantee that a proposed learning route will work.

It needs your input, feedback, and judgment.

2. The Learning Navigation Model

CURRENT CAPABILITY → DESIRED ACCOMPLISHMENT

Diagnose → Route → Attempt → Feedback → Reroute → Arrive

Current capability

What can you already do independently? What have you tried? Where do you get stuck?

Desired accomplishment

What specific outcome do you want to produce? How will you know you have succeeded?

Route

Which small tasks and concepts connect your starting point to the outcome?

Feedback and rerouting

What happened when you attempted the task? What needs to change in the learning plan?

Arrival

Can you demonstrate the target capability rather than merely describe it?

3. Try It Yourself: Build Your Learning Route

Open ChatGPT or another conversational AI assistant.

Choose a small, realistic destination. For example:

Starting point: I can use spreadsheets but have never created a chart.

Destination: I want to create a chart showing monthly expenses and explain what it reveals.

Step 1 — Tell AI your starting point

Prompt 1 — Find My Starting Point

Act as my practical learning navigator.

My current capability is: [describe what I can already do].

My desired accomplishment is: [describe the result I want to produce].

Ask me up to five diagnostic questions, one at a time, to understand my starting point.

Do not assume I know concepts I have not demonstrated.

Step 2 — Ask for the shortest useful route

Prompt 2 — Plan My Learning Route

Based on my answers, design a practical learning route to my destination.

Divide it into small milestones. Each milestone must include:

1. One action I can perform
2. One concept I need to understand
3. One observable result
4. One test of independent capability

Start with the smallest achievable task. Do not give me a long course syllabus.

Step 3 — Begin the first milestone

Perform the first task using your selected application.

If you are learning spreadsheet charts, you can use Google Sheets .

Create a small table with fictional monthly expenses, then attempt to produce a chart.

Step 4 — Report what happened

Prompt 3 — Reroute Me

I attempted the first milestone.

Here is what I accomplished: [describe the result].

Here is where I struggled: [describe the difficulty].

Adjust my next learning step based on this evidence.

Explain only what I need to know next.

Step 5 — Test arrival

Create another chart using a different set of fictional data.

Attempt it without step-by-step AI instructions.

If you can produce and explain the chart, you have evidence of progress toward your learning destination.

4. What Makes This Different from a Course?

A conventional course usually provides a predefined sequence of lessons.

A learning navigation approach begins with the learner's present capability and a specific destination.

The route can change as the learner attempts tasks and receives feedback.

This does not replace structured education, expert instruction, or essential foundations.

It offers an additional method for practical, self-directed learning.

5. The Real Opportunity: Learnability as a Service

Imagine a learning service that does not begin by asking which course you want to enroll in.

Instead, it asks:

• What can you do today?
• What would you like to accomplish?
• What resources and time do you have?
• What have you already attempted?
• How will you demonstrate success?

AI could help assemble a personalized route, propose practical activities, explain concepts, and adapt recommendations as progress is reported.

Such a system would still require reliable assessments, appropriate safeguards, and human oversight where needed.

The idea is to make learning more responsive to the individual and the accomplishment.

6. Today's Accomplishment

Create your own Learning Navigation Card.

My starting capability: __________

My desired accomplishment: __________

My first milestone: __________

What I attempted: __________

What I discovered: __________

My next step: __________

Your first accomplishment is not completing an entire learning journey.

It is defining a destination, creating a practical route, and beginning the first milestone.

The Bigger Insight

Mobility helps us reach places. Learnability helps us reach capabilities.

Generative AI creates an opportunity to make learning more navigable, adaptive, and connected to accomplishment.

The learner still chooses the destination, evaluates the route, and does the learning.

AI can become a useful navigator. It should not replace the learner as the driver of the journey.

AIYING — From AI Awareness to Accomplishment

Learnability as a Service explores how generative AI can help people navigate from their current capabilities to meaningful accomplishments.

AIYING | Ravindra Dastikop

Meta Skills #002 — Learning How to Learn: The Skill That Makes Every Other Skill Easier in the AI Age

AIYING | META SKILLS #002

Learning How to Learn: The Skill That Makes Every Other Skill Easier in the AI Age

When answers become abundant, knowing how to learn becomes more important than simply collecting answers.

What If Learning Itself Became Your Most Important Skill?

Imagine two people facing a technology they have never used before.

The first asks: "Where can I find a complete course?"

The second asks: "What can I try today, and what must I learn to make it work?"

Both approaches can be useful. But the second person has turned the immediate problem into a learning opportunity.

Generative AI makes this approach increasingly accessible. It can explain concepts, suggest experiments, provide examples, and help diagnose mistakes while someone works.

The interesting question is no longer only what do I need to know?

It is also: how can I become better at learning whatever I need next?

1. What Is a Meta Skill?

A meta skill is a capability that supports the development and application of other skills.

Examples include:

• Learning how to learn
• Asking productive questions
• Recognizing assumptions
• Evaluating evidence
• Adapting to unfamiliar situations
• Reflecting on experience

Knowing how to use a spreadsheet is a skill. Knowing how to approach an unfamiliar software tool is a transferable meta skill.

Knowing one programming language is useful. Knowing how to learn another language, test your understanding, and debug errors is a capability that can be reused.

In the AI age, these transferable capabilities deserve deliberate attention.

2. PO: Stop Learning Subjects. Learn Learning Moves.

PO: The most important thing to learn is not a subject, but how to move from not knowing to being able to do.

This provocation does not mean subject knowledge is unnecessary.

It invites us to examine the process through which unfamiliar knowledge becomes usable capability.

Instead of asking only which course to complete, we can ask which learning moves help us make progress.

A learning move is a small, deliberate action that changes our understanding or ability.

Examples include attempting a task, asking a better question, comparing alternatives, testing a hypothesis, explaining a concept, or correcting an error.

3. The Learning-to-Learn Loop

NOTICE → QUESTION → EXPLORE → TEST → REFLECT → TRANSFER

NOTICE — Recognize the learning gap

Identify something you want to accomplish but cannot yet do independently.

QUESTION — Define what you need

Turn uncertainty into a specific question.

EXPLORE — Use AI to investigate

Ask for explanations, examples, alternatives, and the minimum concepts needed to proceed.

TEST — Apply the explanation

Attempt the task and observe the result. Check important claims independently.

REFLECT — Examine your learning

Identify what worked, what failed, and what you understand better.

TRANSFER — Use it somewhere else

Apply the learning to a different situation. This helps reveal whether you have developed a reusable capability.

4. A Practical Experiment: Learn an Unfamiliar Tool

Suppose you want to create a simple visual dashboard but have never used an online diagramming application.

Open diagrams.net , a browser-based diagramming tool.

Your challenge is to create a diagram showing the six stages of the Learning-to-Learn Loop.

Step 1 — Attempt before requesting instructions

Explore the interface for three minutes. Try adding a shape, inserting text, and connecting two shapes.

Notice what you can accomplish without help.

Step 2 — Identify the gap

Write down one operation you cannot yet perform. For example:

How do I connect six shapes with arrows and align them neatly?

Step 3 — Ask AI for targeted assistance

Reusable Prompt 1 — Just-in-Time Learning

I am learning to use diagrams.net.

My immediate goal is to create a six-stage process diagram with connected shapes.

I can already add shapes and text, but I need help connecting and aligning them.

Give me only the next three actions. Explain the purpose of each action. Do not give me a complete tutorial.

Step 4 — Complete the diagram

Create the six stages: Notice, Question, Explore, Test, Reflect, and Transfer.

Connect them in sequence. If useful, add a final arrow returning from Transfer to Notice.

Step 5 — Test independent understanding

Without referring to the AI instructions, create a second diagram showing:

Problem → Idea → Experiment → Feedback → Improvement

If you can do this, you have begun transferring your learning beyond the original task.

5. Ask AI to Help You Reflect

Reusable Prompt 2 — Learning Reflection

I have just completed a task using an unfamiliar digital tool.

Ask me five short questions, one at a time, to help me identify:

• What I initially did not know
• What I discovered through experimentation
• What assistance I needed
• What I can now do independently
• Where I can apply the learning again

After my answers, summarize the reusable learning strategies I demonstrated. Do not invent achievements I have not reported.

6. The Difference Between Getting Help and Learning

There is an important distinction.

AI can complete a task for you without necessarily improving your ability to complete similar tasks.

If you simply copy the result, you may gain an output but little understanding.

If you inspect the process, ask questions, make changes, test the result, and repeat the task independently, you create opportunities for learning.

AI assistance creates an opportunity to learn. Independent capability is the evidence that learning has taken place.

7. What Might You Discover?

This experiment may reveal that:

• You already know more than you expected.
• A precise question can be more useful than a long general tutorial.
• Mistakes reveal specific learning needs.
• AI explanations must sometimes be adapted to the tool's current interface.
• Repeating a task independently is a useful test of practical understanding.

These are possible observations, not guaranteed outcomes.

8. Today's Accomplishment

Create two diagrams using a tool you have not used before.

The first should be completed with targeted AI assistance.

The second should be attempted independently.

Then record:

1. One new operation you learned
2. One mistake you corrected
3. One learning strategy you can reuse

You have not simply learned a diagramming tool. You have practised learning how to learn.

The Bigger Insight

Knowledge remains important. Foundations remain important. Expertise remains important.

But in a world where tools and technologies change rapidly, the ability to approach unfamiliar challenges becomes increasingly valuable.

The goal is not to eliminate preparation. It is to connect preparation more closely to meaningful action.

The most reusable learning outcome may be the ability to learn again.

AIYING — From AI Awareness to Accomplishment

Meta Skills for the AI Age explores transferable capabilities that help people learn, think, adapt, and accomplish meaningful work with AI.

AIYING | Ravindra Dastikop