How do I turn what I have learned into something I can actually do?
Many learners understand terms such as prompts, tokens, embeddings, RAG, agents, and APIs without having built anything meaningful using them.
Others use AI tools every day without understanding the concepts that would help them use those tools more effectively.
Learn as You Do connects practical accomplishment with conceptual understanding.
The objective is not to accumulate terminology. It is to accumulate capability.
The Learning Model
DO #001 Complete one small, useful task.
↓
LEARN #001 Discover one underlying AI concept.
↓
DO #002 Use the previous learning and add a new capability.
↓
LEARN #002 Understand the next concept through experience.
↓
DO MORE → LEARN MORE → ACCOMPLISH MORE
Every episode builds on earlier accomplishments.
The learner does not repeatedly start from zero. Each new concept becomes part of a growing practical toolkit.
From Literacy to Operacy
Edward de Bono introduced the concept of operacy to emphasise the importance of thinking as a practical operating skill—the ability to make things happen.
Literacy concerns reading and writing. Numeracy concerns working with numbers. Operacy concerns effective action.
This provides a useful foundation for rethinking AI education.
AI literacy helps people understand artificial intelligence.
AI operacy helps people use artificial intelligence to accomplish things.
Learn as You Do applies this distinction to modern AI learning.
The aim is to develop people who can recognise opportunities, choose suitable tools, construct useful workflows, evaluate results, and turn possibilities into accomplishments.
AI operacy is the proposed educational extension used in this series, not a term attributed to de Bono himself.
How Every Episode Works
Each episode is designed as a short practical learning experience, typically requiring 10–20 minutes.
Stage
What Happens
1. DO
Complete one useful AI task.
2. SEE
Observe and inspect the result.
3. LEARN
Understand one underlying concept.
4. EXTEND
Connect the new learning to previous skills.
5. APPLY
Use the capability in a practical situation.
6. ACCOMPLISH
Save a tangible result and identify the next step.
Every episode introduces one primary new concept, demonstrates its practical use, and produces an output the learner can retain.
The First 30 Learning Experiences
The initial pathway moves from everyday AI use to increasingly sophisticated AI applications.
Phase 1: Start Using AI
Improve a rough message — Prompts
Adapt a message for an audience — Context
Generate alternative versions — Output variation
Specify an exact output structure — Structured outputs
Create reusable instructions — Prompt templates
Phase 2: Work with Information
Summarise a long document — Context windows
Ask questions about a document — Grounding
Check an AI-generated answer — Hallucinations
Create a source-backed briefing — Retrieval
Organise reference material — Knowledge bases
Phase 3: Build Knowledge-Based AI
Find related information — Embeddings
Build a document assistant — RAG
Compare AI-generated answers — Model evaluation
Build a quality checklist — Evaluation rubrics
Create a reusable AI assistant — Custom assistants
Phase 4: Explore Multimodal AI
Create an infographic — Image generation
Extract information from an image — Multimodal AI
Transcribe a recording — Speech-to-text
Generate narration — Text-to-speech
Produce an educational video — Multimodal workflows
Phase 5: Build AI Workflows
Automate a repeated task — Workflow automation
Connect applications — APIs
Enable an assistant to use tools — Tool calling
Connect external capabilities — MCP
Build a multi-step assistant — AI agents
Phase 6: Make AI Reliable
Review and approve AI actions — Human-in-the-loop
Monitor AI behaviour — Observability
Test difficult scenarios — Evals
Protect sensitive information — AI security
Publish a tested assistant — Deployment
This is only the beginning. Future learning pathways will extend into machine learning, neural networks, transformers, fine-tuning, vector databases, agent orchestration, AI engineering, responsible AI, governance, and emerging technologies.
The Power of Cumulative Learning
Consider how a learner might gradually build a personal AI work assistant.
Episode 001: Write a professional message.
Episode 002: Adapt the message for different audiences.
Episode 005: Save reusable instructions.
Episode 007: Use reference documents to improve accuracy.
Episode 014: Add quality checks.
Episode 015: Turn the workflow into an assistant.
Episode 025: Extend it into a multi-step AI agent.
What began as a simple writing task gradually becomes a useful AI application.
That is the difference between collecting lessons and building capability.
Every tenth episode will also introduce a Milestone Challenge, inviting learners to combine earlier skills into an independent accomplishment.
Let's Try Episode #001
DO: Turn a Rough Thought into a Professional Message
Open an AI assistant such as ChatGPT and enter this prompt:
Task: Rewrite the following rough message into a clear, professional message.
Audience: College principal
Purpose: Invite consideration of an AI course
Tone: Respectful and concise
Length: Under 100 words
Rough message: "We have AI course. Students can learn new AI things. Please consider for your college."
Provide the improved version and explain three changes you made.
SEE: Examine the Result
Observe how AI improves the language, organisation, tone, and clarity.
Does the message communicate the intended purpose? Has the AI introduced any claims that were not supplied? Would you actually send it?
LEARN: What Is a Prompt?
A prompt is the input or instruction provided to an AI model to guide its response.
A useful prompt may specify the task, audience, relevant context, constraints, and desired output.
Prompting is not simply asking a question. It is a practical way of directing AI toward a particular result.
EXTEND: Change the Audience
Repeat the exercise, but change the audience from a college principal to a prospective student.
Compare the two outputs. What changed? Why?
ACCOMPLISH: Save Your Work
Save both messages and the reusable prompt in a personal learning portfolio.
You have now completed your first AI task and learned your first AI concept.
Next episode: Why does the same AI produce different responses for different audiences? Discover the role of context.
Who Is This Series For?
Students: Develop practical AI skills through small, achievable tasks.
Freshers and job seekers: Build a portfolio demonstrating real AI capabilities.
Working professionals: Discover ways to improve everyday work.
Educators: Use short practical activities to introduce AI concepts in classrooms.
Developers and engineers: Progress from everyday AI usage to building and evaluating AI systems.
Institutions and learning communities: Adopt individual episodes as microlearning exercises, workshops, or team-learning activities.
No programming experience is required to begin. More technical episodes will introduce additional prerequisites when needed.
A Living Learning Series
Artificial intelligence is changing rapidly. New tools, models, capabilities, architectures, and practices continue to emerge.
A fixed curriculum can become outdated.
Learn as You Do is therefore designed as a living learning series.
New episodes will introduce relevant concepts and tools through practical tasks. Existing lessons can be updated as technologies evolve.
The series will remain guided by one principle:
Every learning experience must lead to something the learner can actually do.
The Learn as You Do Challenge
Start with one small task today.
Use AI to accomplish it.
Identify one concept that explains how it worked.
Save the result.
Tomorrow, use what you learned to do something more.
Thirty episodes. Thirty practical experiences. A growing portfolio of AI accomplishments.
Not thirty disconnected tutorials, but thirty connected steps toward greater AI capability.
The New Competence of Accomplishing with Artificial Intelligence
Think Constructively. Act Purposefully. Accomplish with AI.
We are learning how to use artificial intelligence.
We are learning how to prompt it, question it, and generate content with it.
But an important question remains:
Are we becoming better at accomplishing meaningful things?
This question introduces the idea of AI Operacy.
1. The Missing Dimension of AI Competence
Artificial intelligence is making sophisticated capabilities increasingly accessible.
A student can generate explanations. A professional can draft reports. An entrepreneur can explore business ideas. A developer can generate code. A teacher can prepare learning materials.
These capabilities are valuable. Yet generating an output is not necessarily the same as achieving an outcome.
A business plan is not a functioning business. A learning summary is not demonstrated understanding. A proposed workflow is not an improved workflow. AI-generated code is not automatically a reliable application.
The distinction is between producing something and accomplishing something worthwhile.
AI education increasingly addresses practical use, evaluation and responsible application. However, there is value in explicitly organizing these capabilities around purposeful accomplishment.
AI can help generate possibilities. Human thinking and responsible action determine what becomes of them.
The intellectual starting point is the work of Edward de Bono, widely associated with lateral thinking, deliberate thinking tools, and constructive approaches to problem solving.
De Bono coined the term operacy to draw attention to the skills needed for doing things.
In De Bono's Thinking Course, particularly the chapters Information and Thinking and Thinking and Doing, he argues that education places considerable emphasis on knowledge while giving insufficient direct attention to the skills of action.
He proposes that operacy deserves recognition alongside literacy and numeracy.
Competence
Fundamental concern
Literacy
Reading and writing
Numeracy
Working with numbers
Operacy
The skills required for doing and making things happen
De Bono distinguishes intelligence as potential from thinking as an operating skill. He also challenges the assumption that analysis, argument, judgment and knowledge are sufficient for constructive action.
Operacy includes setting objectives, considering priorities, exploring alternatives, designing courses of action, planning and dealing with consequences.
His other works provide complementary methods. Lateral Thinking develops ways of generating alternatives. PO: Beyond Yes and No explores moving beyond judgment. Six Thinking Hats provides a structure for deliberate parallel thinking. Serious Creativity connects creative thinking with purposeful design and opportunity creation.
Attribution note: Operacy is de Bono's concept. AI Operacy, as developed in this article, is a proposed contemporary extension. The seven competencies and practice cycle introduced below are original organizing frameworks inspired by his work, not frameworks attributed to de Bono.
AI Operacy is the learnable competence to combine human thinking and AI capabilities to purposefully design, execute, verify, and improve actions that accomplish meaningful outcomes.
This definition contains five essential ideas.
It is learnable
AI Operacy is not reserved for technical specialists or people considered exceptionally intelligent. Its component skills can be practiced and improved.
It combines human and artificial capabilities
AI can assist with information, ideas, simulations, analysis, content and workflows. Human participants remain responsible for appropriate objectives, judgment, accountability and oversight.
It is purposeful
Effective AI use begins by understanding what is worth accomplishing, not by selecting a fashionable tool.
It includes action and verification
Producing a plausible answer or attractive deliverable is not sufficient. Results need to be checked against appropriate criteria.
It is concerned with meaningful outcomes
The value of an accomplishment depends on context, affected people, resources, risks and intended benefits.
AI Literacy helps us understand and use AI. AI Operacy focuses on what we can responsibly accomplish with it.
AI Literacy and AI Operacy should not be treated as competing concepts.
Modern AI literacy frameworks already recognize practical use, critical evaluation, ethics and responsible participation. AI Operacy builds on these capabilities while placing demonstrated accomplishment at the center.
Dimension
AI Literacy emphasis
AI Operacy emphasis
Understanding
Capabilities, limitations and risks
Applying understanding to a purpose
Interaction
Effective AI use
Designing useful human-AI activity
Creativity
Generating and evaluating content
Developing actionable possibilities
Judgment
Evaluating AI information and outputs
Making responsible decisions and verifying results
Action
Appropriate AI application
Achieving and improving meaningful outcomes
AI Operacy is therefore proposed as an outcome-oriented competence that integrates AI understanding, thinking skills, creative design and practical action.
It does not imply that every problem requires AI. Recognizing when AI is unnecessary, inappropriate or too risky is itself an important part of good AI Operacy.
The proposed framework contains seven interconnected competencies.
1. Purpose
Identify a worthwhile outcome. Clarify what needs to change, why it matters, and what success would mean.
2. Perception
Explore the situation from multiple perspectives. Identify assumptions, constraints, relevant factors, missing information and the views of affected people.
3. Possibility
Generate alternatives beyond the obvious answer. Use AI-assisted exploration together with lateral thinking techniques where appropriate.
4. Design
Construct a workable approach. Consider tasks, people, processes, resources, tools, safeguards and alternative courses of action.
5. Judgment
Evaluate options against value, feasibility, evidence, risks and consequences. Do not mistake AI confidence for reliability.
6. Action
Carry out an appropriate intervention, experiment, task or workflow with suitable human control and accountability.
7. Learning
Verify what happened, reflect on the outcome, make improvements and transfer useful lessons to other situations.
These competencies are interconnected rather than rigidly sequential. A person may return to perception after testing an idea or revisit purpose when new evidence changes the original objective.
To translate the competencies into a practical method, we propose seven repeatable activities.
FOCUS
What do we want to accomplish?
EXPLORE
What is happening, and what do we need to understand?
PROVOKE
What alternative possibilities could change our approach?
DESIGN
What workable course of action can we construct?
ACT
What appropriate action can we take?
VERIFY
What evidence shows whether it worked?
LEARN
What should we improve, repeat or reconsider?
Different thinking tools can support different activities.
Activity
Relevant de Bono methods
Focus
AGO: Aims, Goals, Objectives
Explore
PMI, CAF, OPV
Provoke
PO, Random Entry, lateral thinking
Design
Alternatives, constructive design, Concept Fan
Act
Planning, courses of action, priorities
Verify
Consequences, evaluation, deliberate judgment
Learn
Reflection, alternative exploration, improvement
The tools do not replace domain expertise, evidence, professional standards or appropriate safeguards. They provide structures for thinking more deliberately.
7. AI Operacy in Practice: A Job Seeker's Challenge
Consider a graduate preparing for employment.
The initial AI request might be:
Give me ten interview tips.
The AI might generate a useful list. But has the graduate become better prepared for an interview?
Not necessarily.
An AI Operacy approach would begin with a more specific accomplishment:
Complete a realistic mock interview, identify three weaknesses, and improve at least one answer through practice.
Applying the practice cycle
Focus: Improve performance in a particular type of job interview.
Explore: Examine the job description, required competencies and the candidate's current preparation.
Provoke: Challenge the assumption that interview preparation means memorizing ideal answers. Could practicing imperfect answers and improving them be more valuable?
Design: Create a five-question mock interview with criteria for evaluating responses.
Act: Conduct the mock interview, answer the questions and revise one response.
Verify: Compare the original and revised response against the agreed criteria, while recognizing that AI feedback may be imperfect.
Learn: Identify which improvements should be practiced again.
The accomplishment is no longer a list of advice. It is a completed practice activity with evidence of improvement.
AI supports the process. The learner performs the work.
There is an important intellectual opportunity at the intersection of de Bono's lateral thinking and modern generative AI.
Lateral thinking deliberately seeks to move beyond established patterns of perception. Generative AI can produce many possible formulations, combinations and candidate ideas.
But the two are not identical.
AI-generated novelty is not automatically the result of a deliberate lateral thinking process. Similarly, a provocative idea is not automatically useful merely because it is unusual.
Consider the provocation:
PO: AI must stop answering questions.
This is not a factual prediction or a proposal to prohibit answers. It is a deliberate provocation used to stimulate movement.
Possible movements include:
AI could help users discover better questions before answering.
AI could help users test their own reasoning.
AI could generate experiments instead of explanations.
AI could support decisions through structured alternatives rather than a single recommendation.
One possibility might lead to a prototype of an AI-assisted question-discovery workflow.
AI Operacy then asks whether the workflow is useful, how it should be designed, how it can be tested, and what evidence would justify adopting it.
Lateral thinking helps generate possibilities. AI can assist their exploration. Operacy connects the possibilities to purposeful action.
This relationship provides a foundation for a broader initiative connecting human creativity with AI-enabled accomplishment.
AI Operacy should be demonstrated, not merely declared.
A learner who knows many AI tools may still struggle to accomplish a useful task. Another learner who uses a single tool thoughtfully may produce a meaningful improvement.
We therefore propose assessing six dimensions:
Purpose: Was the intended outcome worthwhile and clearly defined?
Thinking: Were relevant factors, alternatives and perspectives considered?
Design: Was the approach practical and appropriate?
Action: Was something actually carried out?
Verification: Was the result checked against suitable evidence?
Learning: Was there reflection, adaptation or improvement?
Responsible use, privacy, security, fairness and human oversight should be considered throughout.
This is a proposed formative assessment approach, not an established certification standard. Further development and testing would be needed before formal adoption.
Time: Approximately 10 minutes for an initial exercise.
Choose one small task that matters to you. It might involve learning, communication, planning, organizing information, or improving an everyday process.
Use the following prompt with an AI assistant:
I want to accomplish something useful with AI.
My situation is:
[Describe your situation.]
Help me:
1. Define one meaningful outcome.
2. Identify three possible approaches.
3. Choose a small, practical action.
4. Identify risks and what I must verify.
5. Decide how to measure success.
Ask questions if essential information is missing.
Do not assume AI is always the best solution.
Keep the action small enough to attempt today.
Then carry out the selected action, where appropriate.
Finally, answer four questions:
What did I intend to accomplish?
What did I actually do?
What result did I achieve, and what evidence supports it?
What will I do differently next time?
Your accomplishment, not the length of the AI response, is the central result.
AI Operacy is being developed as a learning initiative under AIYING — Accomplishing with AI.
Its purpose is to connect AI understanding with deliberate thinking, constructive creativity, responsible action and demonstrable outcomes.
The initiative will explore:
AI Operacy Shorts: Brief ideas that invite immediate action.
AI Operacy Insights: Thinking principles applied to everyday AI use.
AI Operacy Labs: Guided exercises with practical accomplishments.
AI Operacy Cases: Examples of purposeful human-AI collaboration.
AI Operacy Learning: Progressive skill development and assessment.
The initial competence model and learning method are proposals intended for refinement through application, learner feedback and practical evidence.
Who is AI Operacy for?
Students preparing for employment, educators developing practical learning, professionals improving their work, entrepreneurs exploring opportunities, AI practitioners designing solutions, and anyone who wants to move from AI awareness to meaningful accomplishment.
Technical expertise can be valuable, but it is not a prerequisite for beginning.
Our central proposition
AI Literacy asks: What do you understand about AI, and how can you use it responsibly?
AI Operacy asks: What can you meaningfully accomplish with AI, and how do you know you succeeded?
The future of human capability with AI should not be measured only by what AI can generate.
It should also be measured by what people can responsibly make happen.
Join the conversation
What is one meaningful thing you have accomplished with AI?
Not merely an answer you received or a prompt you wrote, but an action, improvement, decision, creation or result that made a difference.
Share your example in the comments. It may help shape future AI Operacy learning challenges.
The proposed AI Operacy framework draws conceptual inspiration from the following works by Edward de Bono:
De Bono's Thinking Course — particularly the chapters on information and thinking, thinking and doing, and deliberate thinking.
Six Thinking Hats — deliberate and parallel thinking.
Lateral Thinking: A Textbook of Creativity — alternatives, restructuring patterns and generating new approaches.
Serious Creativity — provocation, movement, creative design and opportunity generation.
PO: Beyond Yes and No — the use of PO to move beyond conventional judgment.
The Mechanism of Mind — the conceptual background to pattern-based thinking.
These works inform the intellectual foundations of the initiative. The AI-specific definition, seven competencies, practice cycle and assessment model are proposed extensions developed for contemporary AI use.
AI Operacy is not presented as an officially endorsed Edward de Bono framework or an established international competency standard.
The methodological foundation comes from
Edward de Bono's
Lateral Thinking: A Textbook of Creativity,
first published in 1970.
Three parts of the book are especially relevant
to this episode.
A. Difference between lateral and vertical thinking
De Bono distinguishes between two complementary
ways of using information.
Vertical thinking is selective and develops
a direction through logical steps.
Lateral thinking is generative.
It seeks to open new directions by changing
established ways of perceiving and organizing information.
The distinction is not that one is good
and the other is bad.
Both are necessary.
B. The generation of alternatives
In the chapter titled
The Generation of Alternatives,
de Bono explains that a particular way of looking
at something is only one of several possible ways.
The lateral search for alternatives is deliberately
different from simply finding the first satisfactory solution.
A thinker may find a promising approach and still
continue generating other possibilities.
The purpose is to loosen established patterns,
not merely to select a winner immediately.
C. Challenging assumptions
In Challenging Assumptions,
de Bono extends this principle to the basic ideas
on which our thinking rests.
We may rearrange familiar ideas without questioning
whether those ideas themselves should be reconsidered.
Ananya questioned the assumption that the assignment
required an essay.
That opened the possibility of entirely different
forms of learning and communication.
Authentic methodological sequence:
Recognize an established direction → explore alternatives
→ challenge the assumed framework → generate a new
direction → evaluate its usefulness.
This sequence is an instructional synthesis of
relevant ideas in the book, not a named five-step
framework attributed to de Bono.
De Bono also emphasizes practice.
Understanding lateral thinking intellectually
is not the same as developing the habit of using it.
Generative AI can produce useful answers quickly.
But a well-formed answer does not establish
that the original question was well framed.
If a person asks AI to improve an essay,
the system may continue improving essays
without questioning whether an essay is necessary.
This is not an unavoidable limitation of AI.
The same system can also help challenge assumptions,
generate alternatives and explore unfamiliar directions
when prompted to do so.
Conventional AI use
Lateral AI skill
Ask for an answer
Examine the question
Improve the first output
Generate different approaches
Accept the given format
Reconsider the format
Optimize for polish
Explore alternative purposes
Select quickly
Explore before judging
Produce a deliverable
Seek a useful outcome
AI-age principle:
Do not confuse the quality of an AI-generated answer
with the quality of the question that produced it.
The following AI Thinking Lab applies this principle
to six everyday and professional situations.
These are original contemporary adaptations of
de Bono's thinking principles.
They are not exercises reproduced from his book.
Choose the application closest to your own situation.
Each includes a thinking trap, a reframed objective,
a reusable AI prompt and a small experiment.
The prompts are designed to support exploration.
They do not guarantee better decisions.
Evaluate AI-generated ideas against real requirements,
evidence and practical constraints.
APPLICATION 01 · STUDENT
The student who asked AI to improve an assignment
Situation:
You need to communicate a complex topic for a class assignment.
Hidden assumption:
The best way to demonstrate understanding is to produce
a polished written document.
Lateral shift:
Move from improving the document to reconsidering
how understanding could be demonstrated.
Try this AI prompt
I am a student preparing an assignment.
My topic is: [INSERT TOPIC].
The assignment requirements are: [INSERT REQUIREMENTS].
I want to apply Edward de Bono's lateral thinking
principle of generating alternatives.
First, identify the assumptions in my current approach.
Then propose at least five fundamentally different
ways to demonstrate understanding of this topic.
Do not give five variations of the same format.
Include one unconventional possibility.
For each alternative, explain what different
learning or communication opportunity it creates.
Do not rank the ideas initially.
After generating the alternatives, evaluate them
against my actual assignment requirements.
Recommend one small experiment I could try
before choosing my final approach.
Do not invent evidence or assume permission
to change the assignment format.
Small experiment:
Choose two different formats and create a short prototype
of each. Ask a classmate which one communicates
the central idea more clearly.
Skill demonstrated:
Distinguishing the learning objective from the assumed
form of the assignment.
APPLICATION 02 · JOB SEEKER
The job seeker who kept improving the same CV
Situation:
You have applied for several jobs without receiving
many interview invitations.
Hidden assumption:
The solution is to keep improving the CV.
Lateral shift:
Move from optimizing a document to exploring
different ways of demonstrating employability
and reaching suitable opportunities.
Try this AI prompt
I am looking for a job.
My background is: [INSERT BACKGROUND].
My target roles are: [INSERT ROLES].
My constraints are: [INSERT CONSTRAINTS].
I have mainly focused on improving my CV
and applying through job portals.
Use lateral thinking to challenge my assumption
that improving the CV is the main solution.
Identify at least three hidden assumptions.
Generate six substantially different approaches
to finding suitable opportunities.
Consider approaches such as demonstrating skills,
building evidence of capability, direct outreach,
professional networks and alternative role definitions,
but do not limit the search to these categories.
Do not invent vacancies, employers or contacts.
For each approach, explain its potential,
limitations and practical requirements.
After exploration, help me select two realistic
approaches for a seven-day experiment.
Keep recommendations consistent with
my qualifications and constraints.
Small experiment:
Compare one conventional application with one
alternative approach, such as sending a relevant
work sample to a suitable employer.
Skill demonstrated:
Reframing job search from document optimization
to credible opportunity creation.
APPLICATION 03 · PROFESSIONAL
The professional who asked AI to work faster
Situation:
You spend several hours each week preparing
recurring reports.
Hidden assumption:
The reports must continue in their present form,
only faster.
Lateral shift:
Move from accelerating the existing process
to questioning the purpose of the report itself.
Try this AI prompt
I am a professional responsible for a recurring task.
The task is: [DESCRIBE TASK].
Its intended purpose is: [DESCRIBE PURPOSE].
Its current workflow is: [DESCRIBE STEPS].
I want to use lateral thinking before
asking AI to automate the existing process.
Identify assumptions about why the task
is performed in its present form.
Generate at least five different ways
to achieve the underlying objective.
Include possibilities that eliminate,
simplify, redesign or replace parts
of the current workflow.
Do not assume automation is always desirable.
Separate speculative possibilities from
realistic near-term options.
Evaluate the options for usefulness,
accuracy, human oversight, confidentiality,
cost and implementation effort.
Recommend a small reversible pilot
that could test one promising option.
Small experiment:
Ask one report recipient which information actually
influences their decisions. Prototype a shorter
decision-focused report using non-sensitive data.
Skill demonstrated:
Reframing productivity from doing the same work
faster to achieving the purpose more effectively.
APPLICATION 04 · TEACHER
The teacher who asked AI for better explanations
Situation:
Students are struggling to understand a difficult concept.
Hidden assumption:
The teacher needs a clearer or longer explanation.
Lateral shift:
Move from improving explanations to exploring
different ways students could construct understanding.
Try this AI prompt
I am teaching the following concept:
[INSERT CONCEPT].
My learners are: [AGE OR LEVEL].
Their current difficulty is: [DESCRIBE DIFFICULTY].
I usually explain the concept and provide examples.
Challenge the assumption that a better explanation
is necessarily the best intervention.
Generate six different learning approaches.
Consider discovery, observation, comparison,
experimentation, questioning, collaboration
and visual representation where appropriate.
Do not simply produce six rewritten explanations.
For each approach, identify what learners
would actually do and what understanding
the activity might reveal.
Indicate possible misconceptions and limitations.
Recommend one low-cost classroom experiment
with a simple way to assess learning.
Do not present unverified educational outcomes
as established facts.
Small experiment:
Teach one concept using a brief explanation,
then use an alternative discovery activity.
Check understanding with the same short assessment.
Skill demonstrated:
Reframing teaching from delivering information
to creating opportunities for learning.
APPLICATION 05 · HOMEMAKER
The homemaker who asked AI for a better schedule
Situation:
Daily household responsibilities feel increasingly difficult
to manage.
Hidden assumption:
Every existing task must remain, but needs
to be scheduled more efficiently.
Lateral shift:
Move from managing the existing workload
to reconsidering which activities are necessary
and how household needs can be met.
Try this AI prompt
I want to improve how household responsibilities
are managed.
My recurring responsibilities include:
[INSERT ACTIVITIES].
My main difficulties are:
[INSERT DIFFICULTIES].
My household constraints are:
[INSERT CONSTRAINTS].
Instead of immediately creating a better timetable,
help me challenge the assumptions behind
the present arrangement.
Generate six fundamentally different approaches
to meeting the household's needs.
Explore simplification, shared responsibility,
batching, reducing unnecessary work,
changing routines and other possibilities.
Do not assume every task must continue unchanged.
Consider fairness, affordability, accessibility,
family preferences and practical feasibility.
After exploring alternatives, recommend
one small change that could be tested
for a week without disrupting essential needs.
Small experiment:
Choose one recurring task and ask whether it could
be reduced, shared, redesigned or eliminated.
Test a consensual change for seven days.
Skill demonstrated:
Reframing time management as a question
of household design, not merely scheduling.
APPLICATION 06 · ENTREPRENEUR
The entrepreneur who asked AI for better marketing
Situation:
A small business is not attracting enough customers.
Hidden assumption:
The existing product and customer proposition are correct;
only the marketing needs improvement.
Lateral shift:
Move from promoting the current offer to reconsidering
the customer problem and the value being offered.
Try this AI prompt
I run or plan to start a business.
My current offer is: [DESCRIBE OFFER].
My intended customers are: [DESCRIBE CUSTOMERS].
My challenge is: [DESCRIBE CHALLENGE].
I have been considering better marketing
as the main solution.
Use Edward de Bono's principle of generating
alternatives to challenge this assumption.
Identify hidden assumptions about the customer,
problem, offer, delivery method and revenue model.
Generate at least six substantially different
ways to create value for the intended customers.
Include at least one provocative possibility
that challenges the current business model.
Do not invent customer demand or market evidence.
Separate ideas from assumptions requiring validation.
Evaluate the alternatives for customer usefulness,
feasibility, cost and major risks.
Design one inexpensive customer discovery
or prototype experiment to test a promising idea.
Small experiment:
Interview three potential customers about the problem
they actually experience before investing in another
marketing campaign.
Skill demonstrated:
Reframing business growth from selling harder
to discovering a more valuable proposition.
The following prompt can be used in almost any
personal, educational or professional situation.
Its purpose is not to produce a perfect answer.
It is to create a deliberate opportunity
to explore different directions.
I want to practise problem reframing using
Edward de Bono's lateral thinking principles.
My current situation is:
[DESCRIBE SITUATION].
The solution I am currently considering is:
[DESCRIBE SOLUTION].
My real objective is:
[DESCRIBE OBJECTIVE, IF KNOWN].
Please guide me through these stages:
1. OBJECTIVE
Distinguish what I want to accomplish
from the solution I have assumed.
2. ASSUMPTIONS
Identify at least three assumptions
embedded in my current approach.
Ask me to verify important assumptions.
3. ALTERNATIVES
Generate at least five fundamentally
different ways to approach the objective.
Do not merely vary the wording or format
of the same solution.
4. EXPLORATION
Include one unconventional or provocative
possibility. Explain what new direction
it might open without treating it as
an immediately practical recommendation.
5. DELAYED JUDGEMENT
Do not rank or reject the ideas
until we have explored their possibilities.
6. EVALUATION
Now compare the alternatives against
the actual objective, evidence,
constraints, risks and feasibility.
7. ACCOMPLISHMENT
Recommend one small, reversible,
real-world experiment to test
a promising approach.
Distinguish facts, assumptions and
speculative suggestions.
Help me think differently.
Do not make the final decision for me.
Important:
The prompt is a contemporary AIYING adaptation.
It is not an original prompt or formal framework
published by Edward de Bono.
Lateral thinking is not an argument
for rejecting every conventional solution.
Sometimes the first solution is the best one.
Sometimes an essay is exactly what is required.
Sometimes improving a CV, automating a report
or developing better marketing is entirely appropriate.
The important difference is whether the choice
has been made deliberately.
A lateral thinker explores alternative directions.
A responsible thinker then evaluates them
against the real objective, evidence,
constraints and consequences.
In the AI age, both abilities matter.
The danger is not that AI gives us the wrong answer.
It is that we may become exceptionally good at
answering a question we never needed to ask.
Before asking AI to improve something,
ask whether that something is what needs improving.
And before asking for a better answer,
consider whether there is a better question.
One action you can take today
Open an AI conversation in which you have repeatedly
asked for improvements.
Stop improving the current output.
Ask:
"What assumption am I making about the kind
of solution I need?"
Then deliberately explore five alternative directions.
You may return to your original approach.
But this time, it will be a choice rather
than an unquestioned default.
Primary source:
Edward de Bono,
Lateral Thinking: A Textbook of Creativity.
First published by Ward Lock Education, 1970;
Penguin Books reprint, 1990.
Relevant sections of the uploaded edition:
Introduction
Difference between lateral and vertical thinking
The Generation of Alternatives
Challenging Assumptions
The original book supports the distinction
between generative and selective thinking,
deliberate alternative generation,
challenging established patterns,
and practising lateral thinking as a skill.
Contemporary interpretation:
The applications to generative AI, prompting,
job search, teaching, productivity,
household management and entrepreneurship
are original AIYING adaptations.
Evidence boundaries:
The Ananya story is fictional.
The prompts and exercises are proposed
learning activities, not empirically
validated interventions.
No claim is made that de Bono anticipated
modern generative AI or endorsed
the applications presented here.
A simple four-part framework for writing purposeful AI prompts.
Introduction: Why Do AI Instructions Matter?
Artificial Intelligence can assist us with learning, writing, explaining, planning, organizing information, and generating ideas.
But getting a useful result often depends on communicating what we need.
An instruction or request given to an AI system is commonly called a prompt.
A prompt can be a question, instruction, description, or combination of these.
Consider two requests:
VAGUE REQUEST
"Help me study."
CLEARER REQUEST
"Help me prepare for a seventh-grade science examination on the water cycle. Explain the main stages simply and give me five practice questions."
Both prompts ask for assistance, but the second describes the task more clearly.
It gives AI an audience, subject, purpose, and expected output.
Effective prompting begins with clear thinking about the result you want to accomplish.
What Will You Learn?
By the end of this lesson, you should be able to:
Explain what an AI prompt is.
Identify the goal of an AI request.
Provide useful context.
Write specific instructions.
Describe the desired output format.
Compare a vague prompt with an improved prompt.
Review and refine AI-generated responses.
Apply safe and responsible prompting practices.
1. What Is an AI Prompt?
A prompt is the input you provide to an AI system to guide its response or behavior.
For a conversational AI assistant, a prompt might be:
❓
A Question
"What is evaporation?"
✍️
An Instruction
"Summarize this paragraph."
🎨
A Description
"Create an illustration of a garden."
📋
A Task Request
"Organize these notes into categories."
A prompt does not have to be long or complicated.
A short instruction can work well for a simple task.
Additional details become useful when the task requires a particular audience, structure, style, or outcome.
2. The Four-Part Prompt Framework
A beginner-friendly way to write clear AI instructions is:
PART 1
🎯 GOAL
What do you want to accomplish?
↓
PART 2
📋 CONTEXT
What background information would help AI understand your needs?
↓
PART 3
⚙️ INSTRUCTIONS
What specifically should AI do?
↓
PART 4
📝 FORMAT
How should the answer be presented?
GOAL → CONTEXT → INSTRUCTIONS → FORMAT
Use these four elements when they are relevant. More detail is not automatically better; include what the task actually needs.
3. Practical Demonstration: Explaining the Water Cycle
Imagine a seventh-grade student preparing for a science examination.
The student wants to understand the water cycle and prepare for revision.
The Basic Prompt
"Explain the water cycle."
This request identifies a topic, but it does not explain the audience, the purpose, or the desired learning material.
The Improved Prompt
COPY THIS PRACTICAL PROMPT
Explain the water cycle to a seventh-grade student preparing for a science examination. Use simple language, describe the four main stages, provide one everyday example, and conclude with five revision questions.
Let's Examine the Four Parts
Framework Element
Information in the Prompt
Goal
Understand the water cycle and prepare for revision.
Context
Seventh-grade student preparing for a science examination.
Instructions
Explain the four stages using simple language and an example.
Format
A beginner-friendly explanation followed by five revision questions.
The improved prompt provides clearer guidance about what a useful response should contain.
However, it is still important to review the scientific explanation against a reliable textbook.
4. Prompt Improvement Through Feedback
Even a carefully written prompt may produce an answer that needs refinement.
Suppose the explanation is too technical.
You can continue the conversation with a follow-up instruction.
FOLLOW-UP PROMPT 1
"Explain evaporation using simpler language and one example from everyday life."
If the response is too long:
FOLLOW-UP PROMPT 2
"Summarize the explanation into five short revision points."
If you want additional practice:
FOLLOW-UP PROMPT 3
"Create five multiple-choice questions about the water cycle. Provide the answer key separately."
This is an example of iterative prompting: reviewing an output and providing feedback to improve its usefulness.
More prompting does not guarantee greater factual accuracy. Verify important claims independently.
5. Prompt Examples for Everyday Life
The four-part framework is useful for a wide variety of tasks.
Example A — Writing an Email
"Help me write a polite email to my teacher requesting clarification about a homework assignment. Keep the tone respectful, use simple language, and limit it to 100 words. Do not invent any details."
Example B — Planning a Study Session
"Create a 45-minute study plan for a student reviewing three science topics. Include time for revision, practice questions, and a short break. Present it in a simple timetable."
Example C — Organizing Notes
"Organize the following non-confidential meeting notes into three categories: decisions, actions, and open questions. Do not add information that is missing. Use bullet points."
Example D — Creative Ideas
"Suggest ten low-cost ideas for a school environmental awareness campaign. The activities should be suitable for students aged 12–15. Present each idea with a one-sentence explanation."
Notice how each prompt expresses the intended activity and includes details that help shape the response.
6. Common Prompting Mistakes
Common Mistake
Better Approach
Unclear objective
Specify what you want to accomplish.
Missing context
Include relevant audience, situation, and background.
Unspecified output
Request a suitable format or length.
Too many unrelated tasks
Separate complex work into manageable parts.
Accepting the first response blindly
Review, verify, and refine the result.
Unnecessary sensitive details
Remove private data unless sharing is appropriate and authorized.
Clear prompts improve communication. They do not eliminate AI errors.
7. A Reusable Four-Part Prompt Template
Adapt the following template to your own tasks.
COPY AND CUSTOMIZE
GOAL
I want to accomplish: [describe your goal].
CONTEXT
My situation, audience, or background is: [relevant information].
INSTRUCTIONS
Please help me by: [describe the task and important requirements].
FORMAT
Present the result as: [list, paragraph, table, outline, explanation, or another appropriate format].
If information is missing or uncertain, indicate that rather than inventing details.
Follow-Up Template
"Improve your previous response by [specific change]. Keep [what already works] and ensure the result is suitable for [audience or purpose]."
8. Practice Responsible Prompting
AI prompting is not only a communication skill. It also involves responsible decision-making.
Do not share confidential or unnecessary personal information.
Check important factual statements using reliable sources.
Review generated content before publishing or sending it.
Follow applicable educational or workplace policies.
Use human expertise for consequential decisions.
Be aware that different tools may respond differently to the same prompt.
You guide the task. AI produces a response. You evaluate whether it should be used.
Quick Knowledge Check
Try answering each question before revealing the answer.
Question 1
What is an AI prompt?
A. A request or input used to guide an AI system.
B. A guarantee that AI will provide correct information.
C. A type of computer hardware.
Reveal Answer
A. A prompt is an input, such as an instruction or question, used to guide AI.
Question 2
Which sequence represents the four-part prompt framework?
A. Search → Copy → Send → Finish.
B. Goal → Context → Instructions → Format.
C. Install → Login → Generate → Publish.
Reveal Answer
B. The framework helps clarify purpose, background, task requirements, and expected output.
Question 3
Why might you mention the intended audience in a prompt?
A. To help AI choose appropriate language and detail.
B. To remove the need for factual checking.
C. To make every answer longer.
Reveal Answer
A. Audience information can help tailor the explanation to the learner's needs.
Question 4
An AI response is too difficult. What should you do?
A. Accept it without reading.
B. Ask AI to explain it more simply and review the revision.
C. Assume the AI tool cannot answer any question.
Reveal Answer
B. Clear feedback can improve the response's accessibility and usefulness.
Question 5
Does a detailed prompt guarantee a correct answer?
A. Yes, always.
B. Only if the response is long.
C. No, important information still requires verification.
Reveal Answer
C. Better instructions can improve alignment, but AI outputs can still contain mistakes.
Question 6
Which information should you avoid sharing unnecessarily in prompts?
A. The preferred length of a summary.
B. Confidential or sensitive personal details.
C. The desired output format.
Reveal Answer
B. Protect privacy by avoiding unnecessary disclosure of sensitive information.
Your Practical AI Fluency Challenge
Complete one prompt improvement experiment.
Step 1 — Choose a Task
Select a simple activity, such as:
Understanding a concept.
Improving a short message.
Organizing study notes.
Preparing a study plan.
Generating ideas for a project.
Step 2 — Write a Basic Prompt
Write a short instruction without additional details.
Step 3 — Improve It
Rewrite the prompt using:
GOAL → CONTEXT → INSTRUCTIONS → FORMAT
Step 4 — Compare the Responses
Try both prompts in a suitable AI assistant.
Review whether the second response is more relevant, clear, and usable.
Step 5 — Reflect
Identify which changes helped and whether the AI produced any errors.
Your Learning Output: Prompt Improvement Record
My Task: __________________
Basic Prompt: __________________
Improved Prompt: __________________
Differences Observed: __________________
What I Learned: __________________
Further Improvement Needed: __________________
Success means you can explain how changing your instructions influenced the AI response.
Together, these skills help you choose suitable tasks, select appropriate tools, and communicate effectively with AI.
How Does Prompting Connect with the 4Ds of AI Fluency?
Delegation
Determine which part of your task AI should assist with.
Description
Communicate your requirements clearly.
Discernment
Evaluate the relevance and reliability of the response.
Diligence
Review, refine, verify, and use results responsibly.
Lesson 051 Completion Checklist
☐ I understand what an AI prompt is.
☐ I can define the goal of a prompt.
☐ I can add relevant context.
☐ I can write specific instructions.
☐ I can specify an appropriate output format.
☐ I compared a basic prompt with an improved one.
☐ I reviewed the accuracy and usefulness of the responses.
☐ I followed responsible prompting practices.
Lesson 051 — Key Takeaway
GOAL
CONTEXT
INSTRUCTIONS
FORMAT
Better prompting is not about using complicated words. It is about communicating what you need clearly.
COMING NEXT · LESSON 052
How Can We Improve AI Responses Through Follow-Up Questions?
Now that we know how to write clear initial instructions, the next step is learning how to guide an AI conversation through useful follow-up questions, targeted feedback, and iterative improvement.
A useful AI conversation does not have to end with the first response.
