AIYING | LATERAL AI THINKING | PO SERIES
PO: Uber Gives Mobility. GenAI Gives Learnability.
From Mobility as a Service to Learnability as a Service
What if GenAI's most important contribution to education is not generating answers, but making the ability to learn more accessible?
1. The Provocation
Uber did not invent transportation.
It changed how people access transportation.
You do not necessarily need to own a car, employ a driver, or know the roads to arrange a journey.
You specify your current location and destination. A ride-hailing service connects you with transportation.
Now consider GenAI.
GenAI did not invent learning.
But could it fundamentally change how people access the means to learn?
PO: Uber Gives Mobility.
GenAI Gives Learnability.
This is a deliberate lateral thinking provocation. It invites us to explore possibilities rather than claim that the analogy is literally or universally true.
2. Mobility and Learnability
Mobility is the ability to move from one place to another.
Learnability, in this discussion, means the practical ability to acquire and develop new knowledge, skills, and capabilities.
The two share an interesting structural similarity.
| Uber | GenAI Learning |
|---|---|
| Mobility | Learnability |
| Pickup location | Current capability |
| Destination | Desired capability |
| Route planning | Learning pathway |
| Navigation | AI guidance |
| Rerouting | Adaptive learning |
| Arrival | Demonstrated accomplishment |
Uber helps people move between physical locations.
GenAI can help people move between states of capability.
Mobility moves people between places.
Learnability moves people between capabilities.
3. The Hidden Assumption
Learning has traditionally been organised around institutions, courses, teachers, textbooks, and predefined curricula.
These remain valuable.
But an implicit assumption often persists:
To develop a capability, you must first enter a prescribed learning process.
The process may require completing lessons, following a syllabus, and passing assessments before applying the knowledge.
What if we reversed the starting point?
Instead of asking:
Which course should I study?
We ask:
What do I want to become capable of doing?
And then:
Where am I now? What is the next useful step? What must I learn to accomplish it?
4. The Lateral Thinking Movement
Edward de Bono's lateral thinking approach uses provocation and movement to escape established patterns and generate alternative possibilities.
Our PO statement is:
PO: GenAI gives learnability.
We can move from this provocation in several ways.
Movement 1: Extract the Principle
Uber separates access to transportation from the necessity of owning a vehicle.
Can GenAI help separate access to learning support from the necessity of enrolling in a complete formal course?
A learner may still need structured education, but could begin exploring a capability immediately.
Movement 2: Focus on the Difference
Transportation moves the passenger. Learning must transform the learner.
An Uber passenger can arrive without knowing the route. A learner cannot demonstrate competence merely because AI completed the task.
Therefore, an AI learning service must provide more than answers or finished outputs.
It must support practice, understanding, feedback, reflection, and independent performance.
Movement 3: Generate a New Possibility
What if learnability itself could be offered as an accessible, personalised service?
Not a catalogue of courses. Not merely a chatbot answering questions.
A system organised around helping people move from current capability to desired capability.
5. Learnability as a Service (LaaS)
We can call this emerging possibility:
Learnability as a Service
LaaS
An AI-supported learning model that helps individuals identify their current capabilities, define desired outcomes, follow adaptive learning pathways, practise relevant tasks, and demonstrate new capabilities.
This is a proposed conceptual model, not a claim that all existing GenAI tools already deliver a complete, validated learning service.
A useful LaaS system would include seven functions.
- Locate: Establish what the learner already knows and can independently do.
- Specify: Define the desired capability and observable evidence of success.
- Navigate: Recommend a realistic learning route.
- Activate: Begin with a meaningful, manageable application.
- Support: Provide explanations, practice opportunities, and feedback.
- Reroute: Adapt based on errors, progress, and changing requirements.
- Validate: Test whether the learner can demonstrate the capability independently.
The final function is essential.
Generating a successful answer is not the same as developing the capability to produce, evaluate, and improve that answer.
6. Apply Before Learn
This proposition connects to another AIYING lateral thinking provocation:
PO: Apply Before Learn.
The conventional sequence is:
Learn → Practise → Apply
The provocative alternative is:
Attempt → Discover → Learn → Improve → Accomplish
The learner attempts something useful.
The attempt reveals gaps.
Those gaps create questions.
Questions create a purpose for learning.
GenAI helps the learner investigate, practise, and improve.
In this model, learning is not eliminated. It is activated by the pursuit of a meaningful outcome.
7. Example: The Aspiring AI Engineer
Consider a graduate who wants to become an AI application developer.
Current Capability
I know basic computing. I have used ChatGPT. I have very little programming experience.
Desired Capability
I want to build an AI assistant that answers questions from documents, checks its responses, and can be demonstrated to an employer.
An AI learning navigator might begin with a small prototype rather than an extensive sequence of lectures.
The learner attempts the application with AI assistance.
During development, the learner encounters new concepts:
- Python and APIs
- Document processing
- Chunking and embeddings
- Retrieval-augmented generation
- Evaluation and hallucination
- Security and responsible AI
- Testing and deployment
Each concept becomes meaningful because it contributes to the desired accomplishment.
The learning navigator helps determine which concepts require immediate attention and which can be studied later.
At the destination, the learner should be able to explain the application, troubleshoot failures, evaluate outputs, and complete a comparable task independently.
The objective is capability, not merely an AI-generated project.
8. What Could Change in Education?
If learnability becomes more accessible through GenAI, educational institutions could experiment with new models.
From Courses to Capability Journeys
Courses remain useful, but learners may also follow pathways organised around specific accomplishments.
From Standard Routes to Adaptive Routes
Learners with different starting capabilities may receive different sequences of tasks, explanations, and practice.
From Content Completion to Capability Evidence
Progress could be measured not only by lessons completed but by what learners can independently demonstrate.
From One-Time Education to Continuous Learnability
Professionals could repeatedly define new destinations as technologies, jobs, and workplace requirements change.
From AI Answering to AI Guiding
AI systems could be designed to ask questions, identify misconceptions, recommend exercises, and encourage independent reasoning, rather than immediately completing every task.
9. A Necessary Caution
The analogy is powerful, but it has limits.
Uber provides a transportation service. It does not necessarily develop the passenger's ability to drive.
GenAI, by contrast, must help develop the learner's own capability.
If learners become dependent on AI for every decision, explanation, and action, the system may increase task completion without increasing learnability.
Therefore, a credible LaaS model must include independent practice, assessment, feedback quality, and safeguards against incorrect or misleading AI guidance.
Some disciplines also require formal instruction, supervised experience, professional accreditation, and rigorous foundational preparation.
Learnability as a Service should complement these requirements rather than bypass them.
10. An AIYING Experiment
Can we test the idea?
Consider a small experiment with learners who have comparable starting skills.
Give both groups the same capability goal.
One group follows a conventional content-first pathway.
The other uses an AI-guided, application-first pathway.
Compare:
- Time to first useful application
- Conceptual understanding
- Ability to work independently
- Retention after several weeks
- Ability to transfer skills to new problems
- Confidence and motivation
The results may reveal where destination-driven learning works well, where it needs additional structure, and where traditional instruction remains essential.
That would move the provocation from an interesting idea toward an evidence-informed educational practice.
11. Try Learnability as a Service
Here is a practical prompt you can use with a GenAI assistant.
MY LEARNABILITY NAVIGATOR
Act as my AI Learning Navigator.
Treat my learning journey like an Uber ride, but remember that your goal is to help me develop independent capability.
Pickup location:
[What I currently know and can do]
Destination:
[What I want to become capable of doing]
Constraints:
[Time, tools, resources, and deadlines]
Please:
- Assess my starting capability.
- Clarify my desired outcome.
- Define measurable arrival criteria.
- Recommend a practical route.
- Start with one small application.
- Explain concepts when they become necessary.
- Review my attempts and identify mistakes.
- Adapt the route as I progress.
- Test my independent capability.
- Recommend the next destination.
Do not simply complete tasks for me. Help me become capable of completing and evaluating them myself.
12. The Larger Possibility
Mobility changed how people access places.
Could learnability change how people access opportunities?
A graduate seeking employment, an employee adapting to new technology, an entrepreneur developing a product, or a teacher exploring new methods may all have different learning destinations.
GenAI could help each person discover a practical route from their current capability to a desired outcome.
This leads to a larger proposition:
The next important innovation in AI education may not be more content generation. It may be better capability navigation.
The challenge is to design systems that make people more capable, not merely more dependent on AI assistance.
13. The Next PO
The idea of Learnability as a Service opens another provocative possibility.
PO: The learner owns the destination. The curriculum follows.
What if the learner's desired accomplishment became the organising principle, and the curriculum became a dynamically constructed route?
What would happen to course design, assessment, teaching, certification, and lifelong learning?
These questions deserve exploration.
Final Thought
Uber gives people access to mobility.
GenAI offers the possibility of making learnability more accessible.
The distinction matters.
The goal is not simply to transport a learner to an answer.
The goal is to develop the learner's ability to reach new destinations with increasing independence.
PO: Uber Gives Mobility.
GenAI Gives Learnability.
Mobility moves people between places.
Learnability moves people between capabilities.
Tell GenAI where you are.
Tell it where you want to go.
Begin with application.
Learn along the way.
Demonstrate your accomplishment.
AIYING | Choose Work That Matters
Join the Conversation
If GenAI could help you navigate one learning journey today, what would your destination be?
Would you choose a new technical skill, a professional capability, or something you have always wanted to create?
Share your pickup location and destination.
Perhaps the future of learning begins with those two simple questions.
