Should You Use AI to Learn to Code? A Developer's Guide
AI tools can speed up code generation for learners - at the cost of needed problem-solving skills. Discover the best way to learn hands-on coding with AI help.
Mar 26, 2025 • 9 Minute Read
AI is reshaping software development, changing not only how we write code but also how we learn it. AI-powered coding assistants can speed up the process by generating code snippets, assisting with debugging, and even refactoring code. Many developers report significant productivity boosts—but what does this mean for beginners?
In the past, learning to code meant searching Google, reading documentation, and combing through Stack Overflow for answers. Today, AI tools can troubleshoot code in seconds. But does that mean traditional problem-solving skills are no longer necessary?
Types of AI tools for coding
First, let's talk about what AI assistance looks like currently for using in software development. AI coding tools generally fall into three categories:
- Code completion – Suggests code as you type, integrated into IDEs (e.g., GitHub Copilot).
- Code generation and refactoring – Generates entire functions or refactors code based on prompts.
- Debugging and error correction – Identifies and fixes issues within your code.
Many tools blur the lines between these categories. For example, GitHub Copilot can assist with all three functions inside VS Code. While these tools save time for experienced developers, beginners risk relying on them too much—leading to knowledge gaps. If you don’t fully understand AI’s output, you could introduce bugs you can’t fix.
So, should beginners use AI when learning to code?
Should you use AI while learning to code?
Some wonder if learning to code is even worth it, given AI’s rapid advancements. My answer? Absolutely! AI can assist with coding, but understanding the fundamentals is still essential. Developers who deeply understand coding will always be valuable.
From a teacher’s perspective, I love GenAI for writing code and courseware. It’s great for creating quick exercises and sample solutions. But…I don’t encourage students to rely on AI when first learning to code. And no, it’s not because:
- I want them to suffer like I did when learning to code.
- I’m afraid they’ll learn too fast and make me obsolete.
- I’m against GenAI in general.
None of these are true. The issue is that many students leave coding boot camps without actually learning how to code. It’s like copying homework—sure, you’ll pass, but you won’t master the subject.
What you miss when overusing AI
- Writing algorithms yourself
- Debugging manually
- Reading documentation
- Understanding different syntaxes and language differences
Exercises and beginner projects may seem simple enough for AI to solve instantly. You might think, “Great, I did it!” But without engaging in the problem-solving process, you’re skipping the actual learning. AI is a tool—not a substitute for critical thinking.
How to use AI effectively when learning to code
The key to using AI effectively while learning is to strike the right balance. Here’s how to use AI wisely:
- Manually write code first – Always type out your code before consulting AI. This ensures you engage with the logic and syntax, making it easier to identify gaps in your understanding.
- Use AI for explanations – When you have questions while following along, ask the AI for additional explanations, like breaking down steps in a tutorial or clarifying concepts from documentation.
- Compare AI solutions – If AI suggests a different approach, analyze it to understand the reasoning behind it.
- Ask for alternatives – Request alternative ways to solve a problem, and ask the AI to explain each step. This helps deepen your understanding.
- Refactor with AI’s help – When working on a project, use AI to suggest refactorings and identify areas that could be improved according to best practices.
The feedback you get is incredibly useful, especially for beginners. In the past, you’d have to wait for a more experienced colleague or pay for a tutor to provide such quick insights
Practical example: filtering even numbers in JavaScript
Let’s say you want to create a JavaScript function that filters out even numbers from an array, leaving only the odd ones. Try writing the code without AI first. After you’ve written your initial version (even if it’s incorrect), we’ll use AI to fix the issues, learn from the process, and refactor.
First step: try writing the code yourself. This might be what you land on:
function filterEvenNumbers(numbers) {
const result = [];
for (let i = 0; i < numbers.length; i++) {
if (numbers[1] % 2 === 0) {
result.push(numbers[i]);
}
}
return result;
}
// Test it
const myArray = [1, 2, 3, 4, 5, 6];
console.log(filterEvenNumbers(myArray));
// Expected output: [1, 3, 5]
// Actual output: [1, 2, 3, 4, 5, 6]
Even though it’s not working yet, coding the solution yourself helps you practice loops and conditionals and spot errors like off-by-one mistakes or typos.
Our function isn’t returning the expected result—nothing is being filtered. You might suspect your condition is off. Here’s how you’d interact with an AI tool:
Copy and paste your code into your AI assistant and ask: “Why does this return [1, 2, 3, 4, 5, 6] instead of [1, 3, 5]? Is there a mistake in my conditional or loop?” The AI might point out that you used numbers[i] % 2 !== 0, which filters odd numbers instead of even, and that you accidentally used [1] instead of [i], plus reversed the === condition.
After reviewing the AI’s explanation, you fix the condition, and your code works (hurray!).
Now that your basic function is working, you might ask AI, “Is there a more concise way to filter even numbers?”
The AI might suggest using Array.prototype.filter():
function filterEvenNumbers(numbers) {
return numbers.filter(num => num % 2 === 0);
}
Test the code it suggested. You see that the output is back to [2, 4, 6], but thanks to the previous explanation, you now know that this is because of the === instead of !==, and you can fix it yourself.
Still, the tool helped you improve your code! By comparing the AI’s solution to your original, you learn about methods like .filter() and how they simplify your code. If you’re still learning JavaScript or coming from another language background, you might ask AI, “How does .filter() work under the hood?” The AI might explain that .filter() iterates over each element and returns a new array with only those elements for which the callback returns true.
Finally, always test your code manually. If you rely too much on AI, you risk missing errors. In this case, it’s working!
AI vs. hands-on coding: striking the right balance
There’s no shortcut to learning core concepts—you have to write, debug, and struggle through errors to truly understand coding. AI can assist, but it shouldn’t be your first resort.
Think of AI as a mentor. If you had a senior developer sitting next to you, you wouldn’t ask for help on every tiny issue (at least, I hope not!). You’d Google, read documentation, and attempt solutions first. Use AI in the same way.
Advantages and limitations of AI tools for learning
AI is great for learning, but it’s important to be aware of its limitations:
Advantages:
- Speeds up debugging and troubleshooting.
- Provides explanations when you're stuck.
- Offers insights even without a mentor.
Limitations:
- AI provides statistically best responses, not always correct ones.
- It can mislead you if you don’t verify its suggestions. You need to notice errors and adjust accordingly.
- Some companies prohibit AI tools due to security risks. Pasting code into an AI tool gives an external company access to possibly proprietary code, which could be a risk if your company forbids this.
Being aware of these limitations helps you use AI effectively without becoming over-reliant.
Mastering code in the AI era
AI is a powerful tool for learning to code, but like any tool, it needs to be used wisely. Over-reliance can weaken critical thinking and problem-solving skills, while strategic use can accelerate learning. Writing code manually first helps solidify the fundamentals, while AI can serve as a mentor—offering explanations, debugging insights, and refactoring suggestions.
At the same time, AI tools are rapidly advancing, with context-aware assistants that analyze entire codebases and provide smarter suggestions. Ignoring AI could become a career disadvantage—much like skipping Git or a key framework. Mastering AI-powered development is now an essential skill, and adapting to new tools is part of every developer’s growth. Just like picking the right JS library, learning AI-assisted coding is an ongoing process of experimentation and refinement.
AI won’t replace the need to learn coding fundamentals—but it can be an incredible tool when used properly. Try this: tackle a small coding project without AI first, then compare your approach with AI assistance, and see what you discover.
Looking for structured learning? Explore Pluralsight’s coding courses linked throughout the article to learn how to apply these concepts in real-world projects, or subscribe to the Pluralsight AI+ or Pluralsight Complete plans to broaden your learning journey.
Happy coding!
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