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:
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.
- Attempt a simple document-question-answering prototype.
- Use GenAI to understand the generated code.
- Discover why document extraction and chunking matter.
- Learn about embeddings and retrieval when the application needs them.
- Introduce evaluation to check answer quality.
- Improve reliability, security, and user experience.
- 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:
- Verify my current capability through practical questions.
- Define clear evidence of successful arrival.
- Propose a realistic learning route.
- Begin with a small, meaningful application.
- Teach the necessary concepts along the way.
- Give me one actionable step at a time.
- Review my work and identify gaps.
- Reroute when needed.
- 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.
