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

PO: GenAI Is the Uber of Learning — From Current Capability to Accomplishment

AIYING | LATERAL AI THINKING | PO SERIES

PO: GenAI Is the Uber of Learning

Tell It Where You Are. Tell It Where You Want to Go.

What if learning began not with a syllabus, but with a pickup location and a destination?

---

1. The Provocation

Imagine opening Uber.

Before booking your ride, does Uber ask you to study the road network? Does it insist that you learn traffic rules, understand every junction, memorise alternative routes, and pass an examination in navigation?

No.

It asks two simple questions:

Where are you now?

Where do you want to go?

The system identifies a route, estimates the journey, and helps you reach your destination.

Now apply this idea to learning.

PO: Treat GenAI like Uber for learning.

Tell GenAI your current location in learning. Tell it your desired destination in capability. Let it help you discover and navigate the route.

2. The Hidden Assumption in Education

Traditional education commonly follows a predefined sequence:

Syllabus → Lessons → Practice → Examination → Application

The assumption is that learners must first acquire a body of knowledge before they can meaningfully apply it.

This approach has strengths. Foundational knowledge matters. Some subjects require careful sequencing, supervision, and extensive practice.

But does every learner need the same route? Must every learner begin at Chapter One? Must application always come last?

These are the assumptions worth challenging.

3. The PO Movement: From Curriculum to Navigation

Edward de Bono's lateral thinking approach uses provocation to move beyond established patterns of thinking. A PO statement is not a factual claim that must be defended. It is a deliberate stepping stone toward new possibilities.

Our provocation is:

PO: Learning is a journey booked by the learner, not a route prescribed entirely in advance.

The movement from this provocation produces an interesting idea:

Instead of organising learning primarily around content to be covered, organise it around capability to be achieved.

The syllabus becomes a flexible route rather than the starting point of every learning experience.

4. The Uber Model of AI Learning

Consider seven components of a learning journey.

📍 Pickup Location — Current State

What does the learner already know? What can they actually do? What tools and experience do they possess?

🏁 Destination — Desired Capability

What does the learner want to accomplish? Not merely what topic they want to study, but what they want to become capable of doing.

🗺️ Route — Personalised Learning Path

GenAI proposes a practical sequence of tasks, concepts, resources, and milestones appropriate to the learner's starting point.

🚀 Pickup — Begin with Application

The learner starts with a manageable real task. Learning begins through attempting something useful.

🔄 Navigation — Guidance and Feedback

GenAI explains concepts, answers questions, reviews attempts, and helps identify mistakes.

↪️ Rerouting — Adapt to Progress

If the learner struggles, the route changes. If the learner progresses quickly, unnecessary repetition can be reduced.

✅ Arrival — Demonstrated Accomplishment

The journey is complete only when the learner demonstrates the intended capability.

5. A Real Example: Becoming an AI Engineer

Consider a graduate who wants to enter AI engineering.

Pickup location:

I understand basic computing. I have used ChatGPT. I have very limited Python knowledge. I have never built an AI application.

Destination:

I want to build a working AI assistant that answers questions from documents, evaluates its answers, and can be demonstrated to an employer.

A conventional route might begin with a lengthy sequence of programming, mathematics, machine learning, and software engineering lessons.

The destination-driven approach begins differently.

1. Attempt a simple document-question-answering prototype.
2. Use GenAI to understand the generated code.
3. Discover why document extraction and chunking matter.
4. Learn about embeddings and retrieval when the application needs them.
5. Introduce evaluation to check answer quality.
6. Improve reliability, security, and user experience.
7. Demonstrate and explain the completed application.

The learner does not avoid foundational concepts. The learner encounters them in the context of a meaningful destination.

The result is not guaranteed to be the shortest path for every learner. It is a route that can make learning more purposeful and responsive.

6. Apply Before Learn

This model connects directly to another lateral thinking provocation:

PO: Apply Before Learn.

The conventional sequence is:

Learn → Understand → Practise → Apply

The provocative alternative is:

Attempt → Discover Gaps → Learn → Improve → Apply Again

Here, application is not simply the final reward for completing a curriculum.

Application becomes a mechanism for discovering what needs to be learned.

GenAI can help by reducing some of the initial barriers to experimentation, providing explanations, and suggesting manageable next steps.

7. The Learner Is Not a Passive Passenger

Every analogy has limitations.

In an Uber ride, a passenger can reach the destination without understanding the journey.

Learning is different.

A learner cannot claim competence simply because an AI system produced a successful result.

The learner must understand, practise, evaluate, make decisions, and eventually perform independently.

Therefore, the analogy needs one important refinement:

GenAI navigates the learning journey. The learner must develop the capability to drive independently.

For professional, technical, medical, and other high-stakes capabilities, appropriate foundational preparation, qualified supervision, and independent assessment remain essential.

8. From Learning Management to Learning Navigation

Consider the implications for educational institutions.

Learning Management Systems traditionally help organise courses, distribute content, manage activities, and record assessment.

What if an AI-enabled learning environment added a navigation layer?

A student could declare:

Here is what I can do today. Here is what I want to accomplish. Here is how much time I have. Help me reach that capability.

The system could then recommend a route, provide practical assignments, diagnose gaps, and adjust the sequence using evidence of progress.

Teachers would remain important as mentors, designers of meaningful challenges, assessors of competence, and guides for responsible practice.

This suggests a possible evolution:

Learning Management → Learning Navigation

It is a design proposition, not a claim that existing AI systems can already perform every aspect reliably or autonomously.

9. Try It: Book Your First Learning Ride

Here is a reusable prompt for students, professionals, and lifelong learners.

AI LEARNING NAVIGATOR PROMPT

Act as my personal AI Learning Navigator. Treat my learning journey like booking an Uber ride.

My pickup location:
[What I know and can currently do]

My destination:
[The specific capability I want to demonstrate]

My constraints:
[Time, resources, experience, tools, and deadlines]

Your responsibilities:

1. Verify my current capability through practical questions.
2. Define clear evidence of successful arrival.
3. Propose a realistic learning route.
4. Begin with a small, meaningful application.
5. Teach the necessary concepts along the way.
6. Give me one actionable step at a time.
7. Review my work and identify gaps.
8. Reroute when needed.
9. Test my independent capability before declaring arrival.

Start by asking for my pickup location and destination.

10. The AIYING Learning Experiment

This idea can be tested through a simple experiment.

Select two groups of learners with comparable starting capabilities and the same destination.

One group follows a structured, content-first learning path.

The other follows a destination-driven, AI-guided application-first path.

Compare their outcomes using:

• Time to first working application.
• Conceptual understanding.
• Independent task performance.
• Ability to explain and troubleshoot the solution.
• Retention after several weeks.
• Ability to transfer learning to a new problem.

The important question is not whether one group produces an impressive AI-generated output faster.

It is whether the learners develop genuine, transferable capability.

11. The Larger Possibility

If learning can be organised around pickup locations and destinations, several new possibilities emerge.

A college student may begin with a career destination. An employee may begin with a workplace challenge. A teacher may begin with a classroom improvement goal. An entrepreneur may begin with a business problem.

Their routes will differ because their starting points and desired outcomes differ.

GenAI could become a flexible navigation layer connecting current capability to desired capability.

This changes the central question from:

What course should I study?

To:

What do I want to become capable of doing, and what is my next useful step?

12. A New Provocation

The Uber analogy leads to a further challenge.

If GenAI can help create a personalised route, perhaps the curriculum need not always be fixed before the learner begins.

Perhaps it can emerge, within appropriate educational standards and safeguards, from the learner's destination, progress, and demonstrated needs.

PO: The curriculum is not the road. The curriculum is the route.

And that route can change as learning progresses.

13. From Awareness to Accomplishment

The AIYING approach to AI fluency emphasises moving beyond awareness toward meaningful application and accomplishment.

The learning navigator idea provides a practical way to think about that journey:

Know Your Starting Point

↓

Choose Your Destination

↓

Attempt Something Useful

↓

Learn, Practise, and Reroute

↓

Demonstrate Accomplishment

The ultimate objective is not simply to consume more learning content.

It is to become capable of doing something that matters.

Final Thought

For generations, learners have been told:

First complete the course. Then apply what you learned.

The GenAI age invites us to explore a different possibility:

Tell me where you are. Tell me where you want to go. Let us begin the journey through application.

The route can be discovered, tested, and improved along the way.

PO: GenAI Is the Uber of Learning.

Your current capability is the pickup location. Your desired capability is the destination. Application starts the journey. Learning happens along the way. Accomplishment confirms arrival.

AIYING | Choose Work That Matters

Join the Conversation

If you could book a learning journey today, what would be your pickup location and destination?

Share them in the comments. Perhaps your next learning experience should begin with that simple declaration.

AIYING — Lateral AI Thinking
Exploring the confluence of Edward de Bono's lateral thinking and the possibilities of generative AI.

#LateralAI #POThinking #AIYING #GenAI #AIFluency #FutureOfLearning #LearningInnovation #AIEngineering #PersonalizedLearning

21. How Is AI Changing Learning and Education? | AI Fluency Lesson 021

AI FLUENCY · PHASE 1: AWARENESS

Module 6 — AI and the Human World | Lesson 021

How Is AI Changing Learning and Education?

Explore how Artificial Intelligence can personalize learning, encourage interactive practice, and support teachers.

Watch the 54-second visual lesson. Then read the explanations, examples, and learning activities below.

Watch on YouTube →

Introduction

Imagine learning something difficult and having access to an assistant that can explain it in different ways, answer your questions, and help you practise until you understand.

Artificial Intelligence is making this kind of learning support increasingly accessible.

Students can use AI to explore concepts and practise skills. Teachers can use it to prepare learning resources and activities. Researchers can use it to help organize information and ideas.

However, a central question remains:

Does AI simply help us obtain answers, or can it help us develop deeper understanding?

This distinction is essential for understanding AI's role in education.

1. AI Can Personalize Learning

People learn in different ways and at different speeds.

Some learners benefit from simple explanations. Others need diagrams, worked examples, or additional practice.

AI assistants can respond to requests for different explanations and levels of detail.

Everyday Example

A student struggling with fractions asks:

"Explain fractions using a pizza. Then show me a diagram and give me two practice questions."

The AI can provide a simple example, such as dividing a pizza into four equal parts.

If the student remains confused, they can request another explanation.

Why it matters: AI offers opportunities for more flexible and individualized learning support.

However, AI does not always correctly recognize a learner's needs or produce accurate explanations.

2. AI Can Make Learning Interactive

Traditional learning materials often present information for students to read or watch.

AI can add a conversational element to learning.

Learners can ask questions, attempt answers, receive feedback, and explore another example.

An Interactive AI Learning Cycle

1. ASK

Choose a concept or identify something confusing.

↓

2. UNDERSTAND

Explore an explanation and a relevant example.

↓

3. PRACTISE

Attempt questions independently.

↓

4. REVIEW

Evaluate feedback, correct mistakes, and practise again.

Ask → Understand → Practise → Review

Interactive learning encourages students to participate rather than merely receive answers.

AI feedback should still be evaluated because it may contain mistakes.

3. How Can AI Support Teachers?

Teachers perform many activities beyond explaining subject content.

They plan lessons, prepare materials, support different learners, design assessments, and evaluate progress.

AI can assist with some of this preparation.

Lesson Planning

Draft learning objectives, lesson structures, and explanations.

Classroom Activities

Suggest exercises, group activities, and examples.

Quizzes

Draft practice questions and assessment materials.

Learning Support

Suggest alternative explanations for different learning needs.

For example, a teacher preparing a lesson on the water cycle could ask AI to suggest an experiment, a diagram, and age-appropriate questions.

The teacher can then evaluate, adapt, and improve the suggested materials.

Important Principle

AI may assist with teaching preparation, but teachers remain responsible for accuracy, inclusion, learner development, and professional educational judgment.

4. AI Can Support Research and Creativity

Learning involves more than memorizing information.

It also includes questioning, exploring, comparing, and creating new ideas.

AI can help learners brainstorm research topics, organize information, compare perspectives, and draft creative work.

For example, a student preparing a project about plastic waste could ask AI to suggest research questions and ways to organize a report.

The student would still need to locate trustworthy sources, evaluate evidence, and develop original conclusions.

Why it matters: AI can expand the possibilities for exploration and creativity when it supports rather than replaces human inquiry.

5. Everyday Example: Learning Photosynthesis

Imagine a student who finds photosynthesis difficult.

Instead of asking AI to complete an assignment, the student uses it as a learning partner.

The Student's Request

"Explain photosynthesis using a simple example. Then ask me three questions to check my understanding."

What Happens Next?

Step 1 — Explanation: AI explains that plants use light energy to help convert carbon dioxide and water into sugars, releasing oxygen.

Step 2 — Example: AI provides a simple diagram showing sunlight, water, carbon dioxide, and a plant.

Step 3 — Practice: AI asks the student three short questions about photosynthesis.

Step 4 — Feedback: The student answers, reviews the feedback, and revisits anything they have misunderstood.

The student is actively engaged in the learning process.

This example illustrates how AI can help transform an explanation into an interactive practice experience.

6. What Are the Risks of AI in Education?

AI can create new learning opportunities, but it also introduces challenges.

Inaccurate Information

AI explanations may contain incorrect facts or misleading information.

Overdependence

Learners may become too reliant on AI for answers or tasks.

Academic Integrity

Using AI in ways that violate assignment rules can undermine learning.

Privacy and Fairness

Sharing learner data or using unsuitable systems can create privacy and inclusion concerns.

Not all learners have equal access to AI tools, connectivity, or guidance.

Educators and institutions should consider these differences when deciding how AI will be introduced.

7. How Can We Use AI to Learn Better?

The goal should be to strengthen understanding, not simply produce answers.

1. Ask for Explanations

Use AI to clarify concepts, not merely provide final answers.

2. Practise Independently

Attempt questions yourself before checking AI feedback.

3. Verify Important Information

Use textbooks, teachers, and authoritative resources to check essential claims.

4. Follow Academic Rules

Respect your institution's guidelines for AI-assisted work.

5. Reflect on Your Understanding

Ask whether you can explain the topic independently after using AI.

Getting Answers vs Developing Understanding

Consider two different ways a student might use AI.

Using AI for Answers	Using AI for Understanding
Asks AI to complete the exercise	Asks AI to explain the concept
Copies a generated response	Attempts the response independently
Focuses on task completion	Focuses on learning and practice
May overlook mistakes	Checks evidence and reviews errors

AI can support both activities, but genuine educational value depends on what the learner actually understands and can do.

Key Takeaway

AI can provide answers. Education must develop understanding.

AI can support personalized explanations, interactive practice, teaching, research, and creativity.

Meaningful learning still requires human curiosity, effort, critical thinking, and judgment.

Think About It

Imagine you have an AI learning assistant available every day.

Would you use it to obtain answers, understand difficult concepts, practise skills, or explore new ideas?

How would you ensure that AI strengthens your thinking rather than replacing your own learning effort?

Your Learning Challenge

Choose one difficult topic. Ask AI to explain it, request three practice questions, answer them independently, and finally explain the topic in your own words.

What Have You Learned?

After completing Lesson 021, you should be able to:

• Explain how AI can support personalized and interactive learning.
• Identify practical ways teachers can use AI to prepare learning materials.
• Describe how AI can support research and creativity.
• Recognize risks involving inaccuracy, overdependence, privacy, and academic integrity.
• Distinguish using AI for answers from using AI to develop understanding.

CONTINUE LEARNING

Next: Lesson 022

How Is AI Changing Jobs and Work?

Artificial Intelligence is also influencing how people perform professional tasks, develop skills, and collaborate at work.

In the next lesson, we explore how AI is changing workplace activities, creating opportunities, and introducing new responsibilities.

Module 6 — AI and the Human World

021. How Is AI Changing Learning and Education?

022. How Is AI Changing Jobs and Work?

023. Why Do AI Ethics, Governance, and Human Oversight Matter?

024. What Is AI Fluency, and Why Does Everyone Need It?

Further Learning Resources

UNESCO — Artificial Intelligence in Education

OpenAI Academy

OECD — Artificial Intelligence

Your AI Fluency Journey

AI Fluency progresses through four connected phases.

AWARENESS → APPRECIATION → APPLICATION → ACCOMPLISHMENT

From understanding Artificial Intelligence to using it responsibly to accomplish meaningful goals.

Explore the Complete AI Fluency Roadmap →

AIYING

Accomplishing with AI

Everything AI. For Everyone.

AI Fluency — From Awareness to Accomplishment

Created by Ravindra Dastikop

https://dastikop.blogspot.com