AIYING | LATERAL AI THINKING | PO SERIES
PO: Learnability Is the New Mobility
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.
