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.
Accomplishment Learning™: Do We Need a New Learning Model for the AI Era?
A Question
For centuries, education has followed a familiar pattern:
Learn → Practice → Apply → Accomplish
First, we learn concepts.
Then we practice them.
Eventually, we apply them to real situations.
This model made sense when information was scarce.
Teachers had knowledge. Books contained knowledge. Universities organized knowledge.
If you wanted to learn something, you first had to acquire information.
But what happens when information becomes abundant?
What happens when AI can explain, summarize, generate, compare, tutor, and assist on demand?
Perhaps the most important question is no longer:
"How do I acquire knowledge?"
Perhaps the more important question becomes:
"How do I turn available intelligence into meaningful accomplishment?"
The AI Era Changes the Learning Environment
Imagine a student who wants to write a better email.
Traditionally, the student might first learn:
Communication theory
Email structure
Professional writing
Audience analysis
Only later would they write the email.
Today, the student can ask AI for assistance immediately.
The challenge is no longer access to information.
The challenge becomes:
Defining the objective
Asking useful questions
Evaluating suggestions
Making decisions
Producing an outcome
In other words, the challenge shifts from information acquisition to intelligent accomplishment.
A Different Starting Point
Instead of beginning with content, what if learning began with accomplishment?
Instead of:
Learn → Practice → Apply
What if we tried:
Accomplish → Reflect → Learn → Upgrade
The learner starts with a meaningful challenge.
The accomplishment becomes the teacher.
The reflection becomes the lesson.
The capability emerges from the experience.
A Small Experiment
Consider a simple challenge.
A student has applied for an internship.
Two weeks have passed.
No response.
The student wants an update.
Traditional learning might teach communication skills first.
An accomplishment-oriented approach starts differently:
Challenge:
Create an email that increases the chance of receiving a response.
The learner then follows a process:
Think
What am I actually trying to accomplish?
Not "write an email."
But:
"Receive a status update."
Ask
Use AI, a mentor, a book, or another source of intelligence.
Judge
Evaluate the suggestions.
Are they clear? Are they useful? Are they likely to work?
Accomplish
Create and send the final email.
Reflect
What made the result better?
Extract
What communication principles were discovered?
The learner ends up learning communication, prompting, evaluation, and judgment—but through accomplishing something meaningful.
The TAJ Learning Method™
One possible structure is:
Think → Ask → Judge → Accomplish
Supported by reflection and continuous improvement.
The idea is simple:
Don't use AI to replace thinking.
Use AI to amplify accomplishment.
Is This Really New?
Not entirely.
It shares similarities with:
Problem-Based Learning
Experiential Learning
Action Learning
Apprenticeship Models
Situated Learning
However, there may be an important difference.
Most earlier models emerged in a world where information was relatively scarce.
The AI era introduces a new reality:
Intelligence is increasingly available on demand.
The challenge is no longer obtaining information.
The challenge is transforming available intelligence into outcomes.
This may require a stronger emphasis on:
Framing
Questioning
Judgment
Accomplishment
Why This Matters
Many learners today are overwhelmed.
They consume:
Videos
Courses
Podcasts
Articles
Tutorials
Yet often struggle to convert knowledge into action.
Perhaps the gap is not information.
Perhaps the gap is accomplishment.
What if learning systems were designed around real accomplishments rather than content delivery?
What if every lesson began with a meaningful outcome?
What if reflection happened after action instead of before it?
A Working Definition
Accomplishment Learning™ is the idea that learners develop capability by pursuing meaningful outcomes, using available intelligence, reflecting on the experience, and extracting transferable principles.
The goal is not merely to know more.
The goal is to become more capable of accomplishing meaningful things.
An Invitation
This is not a finished theory.
It is a working idea.
A hypothesis.
An experiment.
I would love feedback from:
Educators
Trainers
Coaches
Managers
Parents
Students
AI practitioners
Questions:
Does this approach resonate with you?
Where might it work well?
Where might it fail?
What examples have you seen that resemble this?
Is accomplishment a useful organizing principle for learning in the AI era?
Perhaps the future of learning is not simply about acquiring knowledge.
Perhaps it is about learning through accomplishment.
