AI coding assistants can generate code, explain errors, and help developers move through tasks faster. But if you're learning software development, there's a useful question to ask:
Is the AI helping you learn, or merely helping you finish?
Try this experiment before your next portfolio project.
If you're building your skills in Awka, you can also explore practical learning opportunities at TEKHUB.
The experiment: build, explain, break, repair
Choose a small project. A task tracker, expense calculator, or simple responsive landing page will do.
You can use an AI assistant throughout the exercise. The point is not to avoid AI; it is to test your understanding.
Step 1: Build a working version
Write the requirements before asking for code.
For example, a task tracker should let a user:
- Add a task.
- Mark a task as complete.
- Delete a task.
- See a useful message when no tasks exist.
Ask AI for help where necessary, but review the code it generates.
Step 2: Explain the important parts
Without copying the AI's explanation, try to describe:
- How the application stores and updates its data.
- What happens when a user interacts with a button.
- How the interface reflects a change in state.
- Where errors could occur.
If you cannot explain a section, treat it as a learning task.
Step 3: Break the happy path
Test inputs and situations that the original prompt may not have considered.
What happens if a task is empty? What if a user submits the same task repeatedly? What happens on a narrow mobile screen?
Try to reproduce an issue before asking AI to fix it. This encourages you to investigate rather than accept the first suggestion.
Step 4: Make one improvement
Add a feature or improve an existing one. Write down the reason for the change, implement it, and test it.
Commit your work to Git. If you use GitHub, write a README explaining the project, setup instructions, features, and known limitations.
Step 5: Evaluate your understanding
Use this checklist:
- [ ] I can explain the main code paths.
- [ ] I can reproduce at least one bug.
- [ ] I can explain why my fix works.
- [ ] I have tested more than the ideal scenario.
- [ ] I can describe what I would improve next.
You do not need to pass every item on the first attempt. The checklist shows you what to practise next.
Why this matters
A generated application may look finished while hiding problems with validation, security, accessibility, or maintainability.
The more responsibility you take for reviewing and testing the output, the more useful AI becomes as a learning tool.
The same principle applies outside coding. Data analysts should verify results, product designers should test user flows, and AI practitioners should evaluate model outputs instead of assuming they are correct.
At TEKHUB, learners can explore practical technology training in Awka, with a focus on developing skills through learning and project work.
The takeaway
Don't measure your progress by how quickly AI produces a result.
Measure it by what you understand, what you can test, and what you can improve.
Challenge for this week: Build one small project with AI assistance, then make one meaningful improvement without blindly copying a generated solution.
What project would you use for this experiment? Share your suggestions in the comments.
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