Apply Before You Learn: The New Learning Skill in the Age of AI
How lateral thinking and generative AI can transform learning from preparation for action into learning through action.
Series: Meta Skills in the Age of AI & Lateral Thinking
1. The Learning Assumption We Rarely Question
For generations, education and professional development have followed a familiar sequence:
LEARN → PRACTISE → APPLY
First, acquire knowledge. Then develop proficiency through practice. Finally, apply what you have learned to solve a real problem.
This sequence has served us well. In many disciplines, particularly those involving safety, complex foundations, or professional responsibility, it remains essential.
But is it the only possible sequence?
Consider a graduate who wants to create an AI-powered application. The conventional route may begin with programming fundamentals, databases, APIs, machine learning, and software engineering.
Months later, the graduate might attempt to build an application.
Yet the desire to build something meaningful was present from the beginning.
Why must application always wait until learning is complete?
2. Enter Lateral Thinking: Challenge the Sequence
Edward de Bono's lateral thinking encourages us to examine established patterns rather than merely improve our performance within them.
One of its important devices is PO, a deliberate provocation that allows us to explore an idea without immediately judging whether it is correct or practical.
Let us apply this to learning.
PO: APPLY BEFORE YOU LEARN.
What if application were the starting point of learning rather than its final destination?
This is not a recommendation to abandon knowledge, foundations, or preparation.
It is a deliberate reversal intended to produce new possibilities.
Instead of asking:
"What must I learn before I can begin?"
We ask:
"What can I begin doing, and what must I learn to make progress?"
The change appears small. Its implications can be substantial.
3. Movement: From Curriculum-Driven to Accomplishment-Driven Learning
In lateral thinking, provocation is followed by movement. We do not stop at the unusual statement. We use it to discover a workable concept.
The principle emerging from our provocation is simple:
A desired accomplishment can help determine the learning journey.
Consider two approaches.
Conventional Sequence
Application-First Sequence
Choose a subject
Choose an accomplishment
Follow a predefined syllabus
Identify the knowledge needed
Practise selected exercises
Attempt a real task
Apply after preparation
Learn, test, and improve during application
Demonstrate acquired knowledge
Demonstrate both understanding and accomplishment
The second approach does not eliminate structured learning. It changes how learning needs are discovered and prioritized.
4. Generative AI Changes What Is Possible
Application-first learning is not entirely new. Apprenticeships, project-based education, experiential learning, and learning by doing have long demonstrated the value of practical experience.
What is new is the accessibility of generative AI as an interactive learning assistant.
AI can help a learner:
Clarify the desired accomplishment.
Identify missing knowledge and prerequisites.
Break a complex task into manageable steps.
Explain unfamiliar concepts when they become relevant.
Generate examples and practice exercises.
Review work and suggest improvements.
Explore alternative approaches.
Reflect on what has actually been learned.
Previously, a learner might have needed a teacher, mentor, textbook, and multiple resources to coordinate this process.
Generative AI can help bring many of these functions into one interactive workflow.
However, AI explanations and generated solutions can be incorrect. Learners must still verify important information, test results, and develop independent understanding.
5. A Practical Example: The Graduate Who Wanted to Build a Chatbot
Imagine a graduate with no programming experience who wants to build a chatbot that answers questions about a college course.
The conventional response might be:
"Learn Python, APIs, databases, and AI engineering first."
Our application-first response is different.
Begin with the smallest useful version of the chatbot.
Step 1: Define the accomplishment
The chatbot should answer five frequently asked questions using a small set of approved course information.
Step 2: Discover the learning requirements
Ask AI to identify the minimum concepts, tools, and skills required to build a simple prototype.
Step 3: Build a small version
Begin with a no-code prototype or a simple program, depending on the learner's starting capability.
Step 4: Learn at the point of need
When the learner encounters an unfamiliar concept, such as an API, the immediate task creates a reason to understand it.
Step 5: Verify understanding
The learner must explain how the chatbot works, what information it uses, where errors can occur, and what limitations remain.
Step 6: Improve the accomplishment
Test the chatbot with real questions, identify failures, and improve its responses.
The outcome is not merely a prototype.
The learner has also developed a process for discovering, acquiring, and applying knowledge.
That process is the meta skill.
6. The Deeper Meta Skill: Learning to Learn
Learning to learn is not simply the ability to remember information quickly.
It includes the ability to:
Recognize what one does not know.
Identify what is worth learning.
Choose an effective learning strategy.
Seek and evaluate appropriate assistance.
Monitor progress and understanding.
Transfer knowledge to unfamiliar situations.
Revise the learning approach when circumstances change.
In the AI age, this capability becomes especially important because people may encounter unfamiliar tools, tasks, and disciplines more frequently.
Instead of preparing once for a fixed professional role, learners may need to develop new capabilities repeatedly throughout their careers.
We can describe this shift as:
FROM LEARNING A SKILL
↓
TO LEARNING HOW TO BECOME SKILLED
7. An AI Prompt You Can Use Today
Try this prompt with a small project you have wanted to undertake but have postponed because you believe you lack the necessary skills.
AIYING META SKILLS PROMPT #001
I want to accomplish the following:
[Describe your desired outcome]
My current knowledge and experience are:
[Describe your starting point]
Act as my application-first learning coach.
Help me define the smallest meaningful accomplishment.
Identify the essential prerequisites and any safety or foundational knowledge I must acquire before starting.
Break the accomplishment into five manageable steps.
For each step, identify only the knowledge and skills needed at that stage.
Explain unfamiliar concepts with simple examples.
Give me a practical task before providing a complete solution.
Ask me to explain what I have understood.
Help me test the result and identify mistakes.
At the end, summarize the skills I acquired and the gaps that remain.
Do not simply complete the task for me. Help me become capable of completing and explaining it.
8. A Five-Minute Meta Skill Experiment
You do not need to begin with a complex technical project.
Choose something small:
Create a personal expense tracker.
Build a simple web page.
Analyse a small dataset.
Prepare a presentation on an unfamiliar topic.
Automate one repetitive spreadsheet task.
Spend five minutes defining the accomplishment and asking AI for the first practical step.
Then complete that first step, which may take longer.
Record three observations:
What did I discover that I needed to learn?
What did I accomplish?
What can I now do or explain without AI assistance?
The third question is especially important.
AI-assisted completion is not automatically evidence of learning.
The learner must demonstrate some increase in independent capability.
9. Judgment: When Application-First Learning Is Not Enough
A lateral provocation is useful because it opens possibilities, not because it must always be adopted literally.
Application-first learning works particularly well for exploratory projects, prototypes, familiar low-risk tasks, and situations where immediate feedback is available.
But there are important limits.
In medicine, aviation, electrical safety, cybersecurity, and other high-stakes fields, essential foundations and supervised training cannot simply be bypassed.
Even in software engineering, learners who rely entirely on AI-generated code without understanding its behavior may create unreliable or insecure systems.
The practical conclusion is therefore not:
"Never learn before applying."
It is:
"Do not assume that all learning must precede all application."
A more flexible learning model may alternate between preparation, application, reflection, and deeper study.
10. From Learning to Operacy
Edward de Bono introduced the concept of operacy to emphasize the capacity to make things happen through effective thinking and action.
In his work, thinking is not limited to analysis, criticism, or demonstrating that an answer is correct. It also involves designing possibilities, choosing courses of action, and accomplishing useful results.
This provides an important connection to our series.
Generative AI may help people access information and attempt tasks that previously required substantial preparation.
But the human challenge remains:
Can we define worthwhile outcomes, develop the necessary understanding, make sound judgments, and turn possibilities into responsible action?
Application-first learning can become one practical route toward developing that capacity.
11. The Meta Skill Advantage
Individual skills remain valuable. Deep expertise remains important. Foundational knowledge does not become irrelevant because AI can generate an answer.
What changes is the relationship between skills and the ability to acquire them.
Someone who knows one tool can perform tasks using that tool.
Someone who has learned how to learn can approach unfamiliar tools and develop new capabilities.
Someone who combines learning agility, lateral thinking, sound judgment, and AI assistance may be better equipped to move across changing professional situations.
This is the larger opportunity of meta skills.
THE META SKILL TAKEAWAY
Do not wait until you know everything before beginning something meaningful.
Begin with a suitable accomplishment. Discover what you need to know. Learn deliberately. Test your understanding. Improve through action.
The ultimate outcome is not merely completing the task. It is becoming more capable.
12. One Question to Carry Forward
Consider something you have been postponing because you believe you are not yet sufficiently prepared.
What is the smallest safe and meaningful part of it that you could begin today?
That question may reveal a new learning path.
Coming Next: Meta Skills #002
Thinking About Thinking: Who Is Managing Your Thinking When AI Does the Thinking?
When AI can generate explanations, recommendations, and solutions, an important human capability is knowing when to question, redirect, or change the thinking process itself.
The next episode explores metacognition, deliberate thinking, and the role of human direction in AI-assisted work.
About the Series
Meta Skills in the Age of AI & Lateral Thinking is an AIYING learning and thought-leadership series exploring the transferable human capabilities needed to learn, think, create, decide, and accomplish in an AI-enabled world.
The series draws inspiration from Edward de Bono's thinking methods and develops contemporary applications involving generative AI.
Original de Bono concepts and new AI-age interpretations are distinguished throughout the series.
AIYING
Think Differently. Accomplish More.
Conceptual References
Edward de Bono — Lateral Thinking: A Textbook of Creativity.
Edward de Bono — Serious Creativity.
Edward de Bono — De Bono's Thinking Course.
Edward de Bono — Po: Beyond Yes and No.
Editorial note: The application-first learning framework and its generative AI applications are contemporary interpretations developed for this series, not methods attributed directly to Edward de Bono.
What Happens When Anyone Can Start Learning Anything?
Physical mobility changed where people could go.
Could AI-enabled learnability change what people
can become capable of doing?
1. The Provocation
For centuries, mobility has shaped human opportunity.
Where people lived influenced where they could work,
study, trade, and build their futures.
Roads, railways, automobiles, and aviation expanded
the geographical reach of opportunity.
More recently, ride-hailing platforms changed
how people access transportation.
Now consider a different kind of mobility.
What if the next important frontier is not simply
moving people between places, but helping people
move between capabilities?
PO: Learnability Is the New Mobility.
What if the ability to acquire new capabilities
became as accessible and convenient as
requesting a ride?
This is a lateral thinking provocation,
not a prediction that learning will become
effortless or universally accessible.
Its purpose is to help us discover
new ways to organise learning,
work, and opportunity.
2. From Physical Mobility to Capability Mobility
Physical mobility helps people move
between geographical locations.
Learnability helps people acquire
new knowledge, skills, and capabilities.
The two are different, but the analogy
reveals an interesting possibility.
Physical Mobility
Capability Mobility
Current location
Current capability
Desired destination
Desired capability
Transport route
Learning pathway
Navigation
Learning guidance
Rerouting
Adaptive learning
Arrival
Demonstrated accomplishment
Mobility expands the places we can reach.
Learnability expands the capabilities we can develop.
This suggests a useful new concept:
Capability Mobility.
Capability mobility is the practical ability
to move from an existing set of knowledge
and skills to a new, demonstrable set
of capabilities.
It is not merely the ability to consume
educational content.
It is the ability to develop and demonstrate
something new.
3. The Hidden Assumption
Traditional education often assumes that
learning should follow a predetermined
sequence of subjects, lessons, and assessments.
The learner enters the educational system
and follows the route designed by
the institution.
This approach can provide important
foundations, quality assurance,
and recognised qualifications.
But must every learner follow
the same route?
Must everyone begin at Chapter One?
Must application always come
after extensive preparation?
Must the curriculum determine the
destination, or could the destination
help determine the curriculum?
These questions reveal the assumption
we want to challenge.
Learning is often organised around
the content to be delivered,
rather than the capability
the learner wants to develop.
4. The Lateral Thinking Movement
Edward de Bono's lateral thinking approach
uses provocation and movement to explore
possibilities beyond established patterns.
A PO statement is a deliberate provocation.
It is not required to be true.
Its value lies in the ideas
generated by moving forward from it.
Our provocation is:
PO: Learnability Is the New Mobility.
Movement 1: Extract the Principle
Transportation systems increase access
to places by reducing some barriers
to physical movement.
Could GenAI-supported learning systems
reduce some barriers to developing
new capabilities?
For example, learners may receive
on-demand explanations, examples,
practice exercises, and feedback.
The principle is not effortless learning.
It is more accessible learning support.
Movement 2: Focus on the Difference
A person can reach a physical destination
without learning how to navigate.
But reaching a learning destination
requires genuine change in the learner.
An AI-generated project is not proof
that the learner has acquired the
capability to create it independently.
Therefore, capability mobility must
be measured through understanding,
practice, independent performance,
and transfer of learning.
Movement 3: Generate a Possibility
What if learning systems were organised
around the movement of learners
between capability states?
Instead of asking only which courses
have been completed, we could ask:
What could the learner do before?
What can the learner do now?
What new problems can they solve?
How independently can they perform?
What destination could they pursue next?
This changes the focus from
content consumption to capability development.
5. GenAI as a Capability Mobility Enabler
GenAI can potentially support
capability mobility in several ways.
Personalised Starting Points
Different learners bring different
knowledge, experience, and confidence.
An AI learning navigator can ask
questions and suggest practical tasks
to help identify a learner's
current capability.
Destination-Driven Learning
Instead of beginning with a long
list of topics, the learner defines
a meaningful outcome.
For example:
I want to build a working AI assistant
that can answer questions from documents.
The learning pathway can then be
organised around the knowledge
and skills required for that outcome.
Learning Through Application
GenAI can help learners attempt
manageable tasks before they have
mastered every underlying concept.
The attempt reveals knowledge gaps,
which create reasons to learn.
Adaptive Guidance
If the learner struggles,
the system can offer additional
explanations, examples, and practice.
If the learner demonstrates understanding,
the pathway can progress.
Continuous Capability Development
Learning does not have to end
with graduation or certification.
Individuals can identify new destinations
as their interests, careers,
and technologies change.
6. Possibility One: From Educational Access to Capability Access
Access to educational content
has expanded dramatically.
Books, videos, open courses,
and online learning platforms
make enormous amounts of information available.
But information availability
does not automatically produce capability.
Learners still need motivation,
practice, feedback, resources,
and opportunities to apply
what they have learned.
GenAI could help bridge some
of these gaps.
The central question becomes:
How do we move from access to learning content
toward access to meaningful capability development?
This is an important distinction.
The objective is not simply
to make more information available.
It is to help more people
turn information into usable capability.
7. Possibility Two: From Fixed Careers to Capability Mobility
Consider an accountant who wants
to automate financial reporting.
Or a teacher who wants to design
AI-supported learning activities.
Or a graduate who wants to become
an AI application developer.
Each already possesses some relevant
knowledge and experience.
They may not need to begin
their education entirely from scratch.
An AI-guided learning journey could
help identify transferable skills,
knowledge gaps, and practical
next steps.
The learner develops additional
capabilities that may support
new professional opportunities.
This suggests a shift:
Traditional Career Model
Qualification → Job → Experience
↓
Capability Mobility Model
Existing Capability → New Learning →
Demonstrated Capability → New Opportunities
This does not mean qualifications
become irrelevant.
Professional standards, credentials,
and employer requirements
will continue to matter.
But the ability to acquire
and demonstrate new capabilities
could become increasingly important
alongside existing qualifications.
8. Possibility Three: From Skill Gaps to Capability Transitions
Technological change creates
new demands for knowledge and skills.
Workers may discover that their
existing capabilities no longer
match emerging workplace requirements.
Traditional reskilling programmes
can be expensive, time-consuming,
or difficult to access.
Could GenAI help individuals
identify adjacent capabilities
and develop them through
targeted learning journeys?
Imagine a professional who
understands business processes
but has limited programming experience.
With appropriate guidance,
they might learn to prototype
simple automations, evaluate
AI-generated outputs, and work
more effectively with technical teams.
The objective is not to replace
professional education or experience.
It is to make capability transitions
more practical and accessible.
Employment outcomes will still depend
on economic demand, opportunity,
credentials, and many other factors.
9. Possibility Four: Educational Institutions as Capability Mobility Platforms
Imagine a college where every student
begins a learning journey
with two questions:
Where are you now?
Where do you want to become capable of going?
The institution helps students
define realistic destinations.
Teachers, mentors, courses,
laboratories, projects, and AI
guidance are combined into
structured learning journeys.
Students receive appropriate
foundational instruction
and opportunities for application.
Their progress is assessed
through evidence of understanding
and independent accomplishment.
In this model, the educational
institution does not disappear.
Its role may become even more important
in designing meaningful learning
experiences, ensuring quality,
providing mentorship, and validating
capability.
The institution evolves from
primarily organising content
toward also supporting
capability mobility.
10. Learnability as a Service
The previous AIYING provocation,
"Uber Gives Mobility. GenAI Gives Learnability,"
introduced the possibility of
Learnability as a Service (LaaS).
LaaS can be understood as a proposed
AI-supported model for helping
individuals move from their
current capabilities to
desired capabilities.
A practical model could follow
seven stages:
1. Locate
Establish the learner's current capability.
2. Specify
Define the desired capability and success criteria.
3. Navigate
Create a realistic learning pathway.
4. Activate
Begin with a meaningful application.
5. Support
Provide explanations, practice, and feedback.
6. Reroute
Adapt the pathway as learning progresses.
7. Validate
Demonstrate independent capability.
This framework is a design proposal.
Its effectiveness would need to be
tested in real learning environments.
11. The AIYING 30-Day Capability Mobility Experiment
Can we measure capability mobility?
Consider a simple 30-day experiment.
The learner chooses one meaningful
capability destination.
For example:
Build a small AI-powered application
that solves a real problem.
Day 1: Locate
Assess existing knowledge,
skills, and experience.
Days 2–5: Activate
Attempt a simple working prototype
with AI guidance.
Days 6–15: Learn and Improve
Identify knowledge gaps.
Learn necessary concepts.
Improve the application.
Days 16–25: Extend
Add functionality, test reliability,
and solve increasingly difficult problems.
Days 26–30: Validate
Explain the application,
troubleshoot failures,
and complete a related task
without relying on AI
to supply every step.
The experiment should assess:
Practical task performance
Conceptual understanding
Independent problem-solving
Ability to explain decisions
Transfer of learning
Confidence grounded in demonstrated skill
The central question is:
How far can a learner move in capability
within 30 days, and what evidence
demonstrates that movement?
A short experiment cannot prove
long-term learning effectiveness.
Follow-up assessments would be needed
to evaluate retention and transfer.
12. Access Is Not the Same as Accomplishment
There is an important danger
in the learnability proposition.
GenAI can make it easier
to produce sophisticated outputs.
But producing an output with AI
does not necessarily mean
the user has developed
the underlying capability.
A student may generate code
without understanding it.
A professional may produce
a report without being able
to evaluate its accuracy.
A learner may complete an exercise
without being able to repeat
the process independently.
Therefore, capability mobility
must be judged by more than
AI-assisted task completion.
It must include evidence
that the learner has genuinely
developed new abilities.
AI systems can also provide
incorrect explanations,
misleading feedback,
or inappropriate learning routes.
Human oversight, reliable resources,
and appropriate assessment
remain essential.
13. The Economic Possibility
Physical mobility can expand
access to employment,
education, and markets.
Capability mobility could expand
the range of opportunities
individuals are prepared
to pursue.
The implications may extend
beyond education.
Organisations could support
continuous workforce development.
Small businesses could help
employees acquire new skills.
Professionals could adapt
to changing technologies.
Graduates could develop
portfolio evidence of
practical capabilities.
But broader economic benefits
would depend on equitable access,
effective learning design,
job availability, and
recognition of acquired skills.
The promise is significant.
The outcome is not automatic.
14. A New Measure of Educational Progress
Education often measures progress
through attendance, course completion,
examination scores, and qualifications.
These measures serve important purposes.
But what if we also measured
capability movement?
Consider three questions:
What could the learner do at the beginning?
What can the learner do independently now?
What new problems can the learner tackle?
This could provide an additional
perspective on educational effectiveness.
Rather than focusing only
on content covered,
we would also examine
capability developed.
15. The Next PO: Qualifications
The idea of capability mobility
leads to another provocative question.
Qualifications typically represent
knowledge and skills demonstrated
at a particular point in time.
But technologies, professions,
and workplace requirements change.
What if the ability to acquire
new qualifications became
an important qualification
in its own right?
PO: Your Qualification Is Your Ability
to Become Qualified Again.
Could future education assess
not only what learners know today,
but also their demonstrated ability
to develop new capabilities tomorrow?
This is the next possibility
worth exploring.
Final Thought
Mobility has helped people
reach new places.
Learnability could help people
develop the capabilities needed
to pursue new opportunities.
GenAI introduces the possibility
of making learning guidance,
practice, and feedback
more accessible.
But the real measure of success
is not how much content
AI generates.
It is how much independent
human capability develops.
PO: Learnability Is the New Mobility.
Physical mobility changes where you can go.
Capability mobility changes what you can do.
GenAI could help make the journey
between capabilities more accessible,
personalised, and continuous.
AIYING | Choose Work That Matters
Join the Conversation
If you could begin developing
any new capability today,
what would you choose?
What is your current capability?
What is your desired destination?
And what is the first meaningful
application you could attempt?
Share your thoughts.
Perhaps the future of learning
begins not with a course catalogue,
but with a destination.
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.
