Discover how to move beyond AI-generated answers and create learning experiences that develop real understanding, independent thinking, practical skills, and meaningful evidence of progress.
AI Can Explain Almost Anything. But Does That Mean We’re Learning?
Imagine you’re trying to learn a new skill.
You open an AI assistant and ask a question.
Within seconds, you receive a detailed explanation, several examples, and a step-by-step solution.
Everything makes sense.
You feel confident.
But the next day, when you try to solve a similar problem without AI, you struggle.
What happened?
You understood the explanation while reading it, but you hadn’t necessarily developed the ability to apply the knowledge independently.
This is one of the most important challenges facing AI-assisted education.
Access to information is not the same as understanding. And understanding an explanation is not the same as being able to use what you’ve learned.
As artificial intelligence becomes increasingly integrated into education, we need to think carefully about how people learn, how educators teach, and how AI can support both without replacing the thinking that makes learning meaningful.
This is the thinking behind my new interactive resource:
Vibe Learning & Teaching Studio
https://vibelearning.ca/tools/vibe-studio
The studio brings together two complementary ideas:
Vibe Learning: How learners can use AI, structured practice, feedback, and reflection to develop real skills.
Vibe Teaching: How educators can design experiences that encourage curiosity, meaningful effort, understanding, and independent application.
The resource includes:
- 32 in-depth concept chapters.
- 8 interactive decision-replay labs.
- 6 practical learning and teaching projects.
- A lesson design workspace.
- Learner and educator perspectives.
- Personal notes, bookmarks, and progress tracking.
- Downloadable lesson plans and learning portfolios.
- A complete printable guide.
But the real value isn’t the number of chapters or activities.
It’s the opportunity to rethink how we learn and teach in a world where answers are increasingly easy to obtain.
Let’s explore the ideas behind this approach and how you can start applying them today.
1. The Biggest Problem with AI-Assisted Learning: The Illusion of Understanding
AI can make difficult concepts appear simple.
That’s one of its greatest strengths.
But it can also create an illusion of understanding.
Consider this example.
A learner asks AI:
“Explain JavaScript promises.”
The AI provides a clear explanation.
It includes examples of asynchronous operations, .then(), and async/await.
The learner reads everything and thinks:
“I understand promises now.”
But can the learner:
- Predict the execution order of asynchronous code?
- Explain what happens when a promise rejects?
- Identify an error in an asynchronous function?
- Build a working example independently?
- Apply the concept in an unfamiliar situation?
Those questions test a different level of understanding.
Recognition versus independent capability
There are several levels of engagement with knowledge.
| Level | What the learner can do |
|---|---|
| Recognition | Understand an explanation when seeing it |
| Recall | Explain an idea without looking it up |
| Application | Use the idea to solve a familiar problem |
| Analysis | Investigate mistakes and explain why they occurred |
| Transfer | Apply the idea in a new situation |
| Creation | Build something meaningful using the knowledge |
These are useful distinctions for designing practice, not a rigid universal hierarchy.
A learner might demonstrate one ability while still needing support with another.
The important question is:
What can the learner actually do without the original explanation in front of them?
A practical technique: Explain before checking
The next time you learn something with AI, try this:
- Read the explanation.
- Close or hide the response.
- Explain the concept in your own words.
- Create an example.
- Compare your explanation with a reliable source.
- Identify what you missed.
- Try again.
This introduces retrieval practice and self-explanation into the learning process.
Instead of simply recognizing information, you’re actively reconstructing it.
That makes gaps in understanding easier to identify.
2. Vibe Learning: AI as a Learning Partner, Not an Answer Machine
I developed the Vibe Learning concept in 2025 as a structured approach to AI-assisted learning.
The idea emerged from a simple observation.
AI was making it easier than ever to create software, explore ideas, and generate solutions through natural-language interaction.
But what if we applied that same conversational, experimental approach to learning?
Not to eliminate effort.
Not to bypass understanding.
But to make learning more personalized, interactive, and practical.
Vibe Learning focuses on keeping the learner actively involved.
The learner should be asking questions, making predictions, solving problems, evaluating feedback, and building something meaningful.
The Vibe Learning practice cycle
<escape>ORIENT → UNDERSTAND → PREDICT → APPLY → FEEDBACK → RECALL → CONNECT → TRANSFER</escape>
Let’s examine what this means.
Orient: Define what you want to accomplish.
Understand: Build an initial explanation or mental model.
Predict: Anticipate what will happen before seeing the answer.
Apply: Use the concept in a practical task.
Feedback: Compare your result with evidence or useful critique.
Recall: Retrieve the idea without relying on the original explanation.
Connect: Relate the concept to what you already know.
Transfer: Apply it in a new situation.
This process shifts learning from information consumption to active participation.
Example: Learning JavaScript array methods
Suppose you’re learning filter().
A passive approach might involve reading five examples and moving on.
A Vibe Learning approach could look like this:
Understand: Learn that filter() creates a new array containing elements that satisfy a condition.
Predict: Examine this code and predict the result.
const numbers = [3, 8, 12, 5, 20];
const result = numbers.filter(
number => number > 10
);
console.log(result);Apply: Write your own filter for products under $50.
Feedback: Test your solution with different inputs.
Recall: Explain how filter() works without looking at notes.
Connect: Compare filter() with map().
Transfer: Build a searchable list of products that updates based on user-selected criteria.
The learner is no longer simply reading about JavaScript.
They’re developing practical capability.
3. Vibe Teaching: Design Learning Experiences That Require Thinking
Vibe Learning focuses on the learner.
Vibe Teaching focuses on the decisions educators make to support that learning.
An educator can use AI to generate:
- Lesson plans.
- Examples.
- Quizzes.
- Explanations.
- Rubrics.
- Classroom activities.
- Feedback suggestions.
But generating instructional material is only part of teaching.
A more important question is:
What will the learner actually do with this material?
Consider two approaches to teaching the same concept.
Approach A: Explanation-first teaching
The instructor explains a concept.
Students watch an example.
The instructor demonstrates the solution.
Students answer questions about what they saw.
Approach B: Thinking-centered teaching
The instructor introduces a meaningful problem.
Students make an initial prediction.
They attempt a solution.
They compare their reasoning with evidence.
They receive targeted support.
They revise their work.
Finally, they apply the concept to a new problem.
Both approaches can have value.
But the second approach creates more opportunities to observe and develop independent thinking.
A simple lesson-design question
Before preparing any lesson, ask:
What will learners be able to do at the end that they couldn’t do at the beginning?
Then ask:
What activity will provide convincing evidence that they can do it?
Those two questions help move lesson planning away from simply covering content.
They focus attention on capability.
4. The Power of Productive Struggle
One of the most important decisions in teaching is knowing when to help.
Too little support can leave learners frustrated and confused.
Too much support can remove the thinking required to develop skill.
This creates an instructional challenge.
How can we help learners without doing the work for them?
Example: A learner encounters a JavaScript error
Imagine a student writes:
const scores = [70, 85, 92];
const doubled = scores.map(score => {
score * 2;
});
console.log(doubled);The result is:
[undefined, undefined, undefined]The learner asks AI:
“Why doesn’t my code work?”
One response would be to immediately provide the corrected code.
But an alternative is to guide the learner through a sequence of hints.
A four-level hint ladder
Hint 1: Direct attention
“Look carefully at what your arrow function returns.”
Hint 2: Ask a diagnostic question
“What happens when an arrow function uses braces but doesn’t include a return statement?”
Hint 3: Narrow the problem
“Compare an arrow function with an expression body to one with a block body.”
Hint 4: Provide explicit support
“Add return score * 2; inside the callback.”
This approach gives the learner an opportunity to investigate before receiving the complete answer.
It also makes support adjustable.
A learner who needs only the first hint can continue independently.
Another learner may benefit from more explicit guidance.
A reusable AI coaching prompt
Act as a patient learning coach.
I am learning [TOPIC].
My current problem is:
[PROBLEM]
My attempt is:
[ATTEMPT]
Do not immediately give me the solution.
First:
1. Ask what I expected to happen.
2. Help me identify the most relevant concept.
3. Give one small hint.
4. Wait for my next attempt.
5. Increase the level of support if needed.
6. Explain the solution after I have tried.
7. Give me a related problem to solve independently.
Your goal is to strengthen my understanding,
not simply complete the task.This is one example of using AI to support learning rather than replace it.
5. Make Thinking Visible: How Do We Know Someone Understands?
One of the challenges of teaching is that understanding isn’t directly observable.
We can observe answers, explanations, decisions, and completed work.
But those are pieces of evidence.
A correct answer doesn’t always mean the learner understands the process.
For example, a student might produce a working application using AI-generated code.
The application works.
But can the student explain how it works?
Can they identify a bug?
Can they modify a feature?
Can they build something similar without the original instructions?
These questions help distinguish successful output from demonstrated capability.
Five forms of learning evidence
1. Explanation
Can the learner explain the idea accurately?
2. Prediction
Can the learner anticipate what will happen?
3. Application
Can the learner use the idea to complete a task?
4. Correction
Can the learner recognize and fix a mistake?
5. Transfer
Can the learner apply the idea in a different context?
These forms of evidence can be incorporated into both independent learning and formal teaching.
Example: Teaching CSS Flexbox
Instead of asking students only to define justify-content, give them a practical challenge.
Create a navigation bar with three links.
Ask learners to:
- Predict how
justify-content: space-betweenwill position the links. - Run the example.
- Explain the result.
- Change the layout to center the links.
- Fix a deliberately broken layout.
- Apply the technique to a card component.
The resulting work provides much richer evidence of understanding than a definition alone.
6. Decision-Replay Learning: Practise Judgment, Not Just Recall
Some of the most important skills in education involve making decisions.
An educator may need to decide:
- When to provide a hint.
- When to introduce a worked example.
- How to respond to an incorrect answer.
- Whether a learner needs more practice.
- How to assess AI-assisted work.
- How to support learners with different needs.
These decisions rarely have one universally correct answer.
They depend on context.
This is why realistic scenarios can be useful for professional learning.
Example: A learner is relying too heavily on AI
Imagine you’re teaching a programming course.
A student submits working code but cannot explain how it works.
You suspect the student has relied heavily on AI-generated solutions.
What should you do?
Option A: Prohibit the student from using AI for future assignments.
Option B: Ask the student to explain, modify, and debug the submitted code.
Option C: Accept the working code as sufficient evidence of learning.
Each decision has consequences.
Option A may reduce some forms of dependence but may not teach responsible AI use.
Option B provides an opportunity to assess understanding and identify gaps.
Option C may overlook whether the learner can independently apply the relevant skills.
The best response may depend on course policies, accessibility needs, assessment objectives, and the nature of the assignment.
The key is to examine the reasoning behind the decision.
The decision-replay process
A useful scenario activity follows this structure:
SITUATION → DECISION → CONSEQUENCE → REFLECTION → REPLAY
The learner examines a situation.
They choose an action.
They investigate the consequences.
They reflect on the reasoning.
Then they replay the situation with a different choice.
This approach encourages learners to compare alternatives rather than memorize a single response.
Inside the Vibe Learning & Teaching Studio
The studio includes 8 decision-replay labs.
Each presents a realistic authored scenario with two decisions, consequences, and opportunities to replay alternatives.
These are designed learning scenarios, not predictions about how real students will behave.
The purpose is to practise educational judgment and consider different approaches.
7. A Practical Framework for Designing Better Lessons with AI
Let’s turn these ideas into a complete lesson-design process.
Imagine you’re teaching JavaScript functions.
Your learners already understand variables and basic expressions.
You have 45 minutes.
Your goal is for learners to create and test a reusable function.
Step 1: Define the learning outcome
Avoid vague objectives such as:
“Understand JavaScript functions.”
Instead, define an observable capability:
“By the end of the lesson, learners will be able to create a JavaScript function that accepts parameters, returns a value, and handles at least one unusual input.”
This gives you something concrete to assess.
Step 2: Start with a meaningful problem
Ask:
“How could we calculate a discount for 100 different products without repeating the same calculation?”
Give learners time to suggest solutions.
Step 3: Ask for an initial attempt
Before demonstrating the complete solution, invite learners to write a function.
They may produce something like:
function discount(price) {
console.log(price * 0.9);
}This is a useful starting point.
It performs a calculation, but it prints the result instead of returning it.
Step 4: Introduce a targeted concept
Explain the difference between console.log() and return.
Then demonstrate:
function calculateDiscount(price, rate) {
return price * (1 - rate);
}Step 5: Provide guided practice
Ask learners to calculate discounts for several prices.
Then introduce a new requirement:
“What should happen if the discount rate is negative?”
This encourages learners to think about input validation.
Step 6: Add independent application
Ask learners to build a simple product price calculator.
They should use a function that accepts a price and discount rate.
Step 7: Gather evidence
Have learners:
- Explain their function.
- Demonstrate two valid inputs.
- Identify an invalid input.
- Describe how they would improve the solution.
Step 8: Reflect and transfer
Ask:
“Where else could you use this pattern?”
Possible examples include tax calculations, shipping estimates, and progress percentages.
The lesson now includes explanation, practice, feedback, and independent application.
AI can help prepare materials, but the learner still performs the meaningful work.
8. Using AI to Critique a Lesson Plan
AI can be especially useful as a planning partner.
Rather than asking AI to generate an entire lesson and accepting the result, ask it to critique a plan you’ve already designed.
This keeps the educator responsible for instructional decisions.
Reusable lesson critique prompt
Act as an instructional design reviewer.
I will provide a lesson plan.
Review it for:
1. Clarity of learning outcomes.
2. Opportunities for learner participation.
3. Appropriate prerequisite knowledge.
4. Opportunities for independent attempts.
5. Quality and timing of feedback.
6. Evidence of understanding.
7. Accessibility and learner support.
8. Opportunities for transfer.
9. Appropriate use of AI.
10. Realistic timing.
Do not simply praise the lesson.
Identify:
- Three strengths.
- Three weaknesses or risks.
- Missing opportunities for learner thinking.
- One improvement to the practice activities.
- One improvement to the assessment.
- One independent transfer challenge.
Explain the reasoning behind your recommendations.
LESSON PLAN:
[PASTE PLAN]The Vibe Learning & Teaching Studio includes a lesson-design workspace that can assemble an AI critique prompt from your plan.
The studio itself does not send the plan to an AI service. You copy the prompt into your chosen AI assistant, after removing any private or sensitive information.
9. Why Reflection Is Essential to Meaningful Learning
Completing a task doesn’t automatically mean you’ve learned everything the task could teach you.
Reflection helps turn experience into insight.
After a learning activity, ask:
What was I trying to accomplish?
What did I actually do?
What worked?
What didn’t work?
What did I misunderstand?
What would I change next time?
Can I apply this somewhere else?
These questions encourage learners to examine their own process.
Example: Reflecting on a coding challenge
Suppose you built a JavaScript search filter.
Your first attempt failed because you compared strings without considering capitalization.
You fixed the problem using toLowerCase().
A useful reflection might be:
“My original solution assumed users would enter text with the same capitalization as the stored data. I learned to normalize both values before comparing them. Next time, I’ll consider case sensitivity when designing search functionality.”
That’s more valuable than simply recording:
“Completed the search filter.”
It captures a reusable lesson.
Build a learning evidence journal
For each important activity, record:
| Field | Example |
|---|---|
| Concept | Case-insensitive string comparison |
| Original attempt | Compared strings directly |
| Problem | Uppercase and lowercase values didn’t match |
| Correction | Normalized both strings |
| Evidence | Search works with mixed capitalization |
| Next challenge | Handle extra spaces and partial matches |
Over time, this creates a useful record of learning.
10. The Vibe Learning & Teaching Studio: Five Ways to Explore
The studio combines learning theory, practical decisions, planning tools, and applied projects in one browser-based environment.
Here’s how to use its main areas.
A. Concept Library — 32 In-Depth Chapters
The concept library explores the ideas behind learner-led learning and thoughtful teaching.
One particularly useful feature is the ability to switch between learner and educator perspectives.
For example, consider the concept of feedback.
Learner perspective:
How can I use feedback to identify what I misunderstood and improve my next attempt?
Educator perspective:
How can I provide feedback that helps learners diagnose problems without simply giving away the answer?
The concept remains the same.
But the responsibilities and practical decisions are different.
The studio also provides chapter notes, bookmarks, and export options.
B. Decision-Replay Labs — 8 Practical Scenarios
Explore realistic educational situations.
Make decisions.
Inspect consequences.
Replay alternative approaches.
The goal isn’t to guess what the system considers the perfect answer.
It’s to develop stronger reasoning about instructional choices.
C. Lesson Designer — Turn Principles into Practice
The lesson designer helps you structure learning experiences around:
- Learner actions.
- Instructional support.
- Evidence of understanding.
- Practice and revision.
- Time allocation.
You can begin from a worked plan and adapt it to your own setting.
The coverage check identifies missing plan fields.
It does not claim to measure the overall quality of your teaching.
You can also download or print the resulting plan.
D. Practical Projects — 6 Applied Challenges
The studio includes six projects designed around producing useful learning artifacts.
Rather than stopping at an explanation, learners create something bounded and practical.
The emphasis is on documenting what was built, what was checked, and how the result connects to the underlying concepts.
E. Personal Portfolio — Keep Your Learning Evidence
The studio allows you to preserve notes, decisions, projects, and lesson plans.
Work is saved locally in your browser when storage is available.
You can download a portfolio or back up your progress for use in another browser.
This is particularly useful for educators developing a teaching practice or learners building a record of their progress.
Explore the studio:
https://vibelearning.ca/tools/vibe-studio
11. Five Practical Ways to Use the Studio
Use Case 1: Learn a Difficult Technical Concept
Choose a topic you’re struggling with.
Explore the relevant learning principles.
Design a practice session that includes prediction, application, feedback, and recall.
Create a small project that demonstrates your understanding.
Use Case 2: Improve an Existing Lesson
Take a lesson you’ve already taught.
Identify how much time learners spend listening compared with actively doing something.
Add an initial attempt, a meaningful practice activity, and a transfer challenge.
Use the lesson designer to organize the revised sequence.
Use Case 3: Develop Better AI Coaching Prompts
Examine how hints, questions, and feedback influence learning.
Create an AI coaching prompt that encourages independent attempts before revealing solutions.
Test the prompt with a real learning task.
Use Case 4: Practise Educational Decision-Making
Work through a decision-replay lab.
Record your initial choice.
Investigate the consequences.
Replay the situation.
Compare your reasoning before and after the exercise.
Use Case 5: Build a Professional Learning Portfolio
Use the studio to document improvements to your teaching or learning practice.
Collect lesson plans, reflections, project artifacts, and examples of how your decisions have changed.
This creates a more meaningful record than simply tracking how many pages you’ve read.
12. A Seven-Day Vibe Learning & Teaching Challenge
Want to put these ideas into practice?
Here’s a structured seven-day challenge.
Day 1 — Define a Real Learning Goal
Choose something you genuinely want to learn or teach.
Write an outcome that describes what successful performance looks like.
Avoid vague goals such as “learn more about AI.”
Instead:
“Create a working AI-assisted document review workflow and explain how its validation steps work.”
Day 2 — Diagnose Your Starting Point
Attempt a small task before reviewing explanations.
Record what you can already do.
Identify gaps and assumptions.
Day 3 — Explore the Concepts
Use the studio’s concept library.
Read from the learner perspective.
Then switch to the educator perspective.
Compare how the same principle changes depending on your role.
Day 4 — Practise a Decision
Complete one decision-replay lab.
Record your reasoning.
Replay the situation using a different approach.
Explain what changed.
Day 5 — Design a Learning Session
Use the lesson designer to create a focused learning experience.
Include an initial attempt, guided support, independent practice, feedback, and reflection.
Day 6 — Build an Artifact
Complete a practical project.
Produce something you can demonstrate or explain.
It might be a lesson plan, a coding project, a learning activity, or a feedback framework.
Day 7 — Review the Evidence
Look back at your original goal.
What can you now do?
What evidence supports that conclusion?
What still requires practice?
What would be a useful next challenge?
Export your notes or portfolio so you can continue building on your progress.
13. The Future of AI in Education Should Be More Human, Not Less
AI will continue to become faster, more capable, and more accessible.
It will produce increasingly sophisticated explanations, lessons, exercises, and solutions.
But that doesn’t eliminate the need for human learning.
In many ways, it makes the quality of our learning processes even more important.
If answers are instantly available, learners need opportunities to evaluate them.
If code can be generated automatically, developers need to understand what it does.
If lesson plans can be created in seconds, educators need to judge whether those plans create meaningful learning.
If AI can explain almost anything, we need to ask whether learners can explain, apply, and extend those ideas themselves.
The important skill isn’t simply knowing how to obtain an answer.
It’s knowing how to think with the information, challenge assumptions, make decisions, and create something useful.
That’s the direction I believe AI-assisted education should take.
AI can support the learning process. But the thinking, judgment, and understanding should remain with the learner.
14. Start Exploring Vibe Learning & Teaching Studio
I’ve developed the Vibe Learning & Teaching Studio as a practical resource for anyone interested in learning more effectively or designing better educational experiences.
Whether you’re a teacher, instructional designer, developer, student, workplace trainer, or independent learner, the goal is the same:
Move beyond passive information consumption.
Develop stronger understanding.
Practise meaningful decisions.
Build useful artifacts.
And create evidence of what you can actually do.
Explore the free Vibe Learning & Teaching Studio:
https://vibelearning.ca/tools/vibe-studio
Discover the Vibe Learning framework:
Explore more interactive learning tools:
Final Thoughts: Make Room for Human Thinking
The most important question in AI-assisted education isn’t:
“How much content can AI generate?”
It’s:
“How can we use AI to help people become more capable, independent thinkers?”
That requires thoughtful learning design.
It requires practice.
It requires useful feedback.
It requires opportunities to make mistakes, revise ideas, and apply knowledge independently.
And it requires educators and learners to remain active participants in the process.
This is the purpose behind Vibe Learning and Vibe Teaching.
Not to remove effort from learning.
But to make that effort more purposeful, engaging, and productive.
Learn with intention. Teach for understanding. Make room for human thinking.
About the Author
Laurence Svekis is a Google Developer Expert specializing in Google Workspace, Google Champion, author, developer, and online educator who has taught 3 million+ students worldwide.
With more than 20 years of experience in web development and education, Laurence focuses on JavaScript, Google Apps Script, artificial intelligence, web development, and practical AI-assisted learning.
He is the creator of the Vibe Learning framework, developed in 2025, and Vibe Teaching, an educator-focused approach to designing learning experiences that preserve human thinking, judgment, and independent understanding.
Through DiscoveryVIP and VibeLearning.ca, Laurence creates interactive learning tools, educational resources, and practical guides that help people develop real-world skills.
Join the Conversation
Do you think AI is helping people learn more deeply—or making it easier to avoid the thinking required to learn?
How do you balance AI assistance with independent practice?
And if you’re an educator, what’s one change you’ve made to your teaching because of AI?
I’d be interested to hear your experiences.
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