The first wave of AI programming tools focused on generation.
Ask for a function. Get the function. Ask for a component. Get the component. Ask for an entire application. Get a surprisingly large portion of the application.
The next problem is harder:
How do we know whether the generated software is actually good?
That is where AI code review becomes interesting.
Generation is becoming cheap. Evaluation is becoming valuable.
Coding agents are increasingly capable of handling longer, more complex tasks. The industry is moving toward workflows where developers orchestrate agents rather than manually writing every line. Anthropic: 2026 agentic coding trends report
But generating code is only half the problem.
Someone still has to determine whether the result satisfies the requirement.
And asking another AI, "is this code good?", is not enough.
The reviewer needs a precise target.
The task becomes the specification
Consider a requirement: users must not be able to access another user's private project.
That gives the reviewer something concrete to evaluate.
Did the implementation enforce ownership? Are all relevant routes protected? Can a user manipulate an ID to access another record? Are database queries scoped correctly? Are there race conditions? Does the behavior remain correct after errors?
Now the AI reviewer is not judging code in the abstract. It is evaluating an implementation against a requirement.
That is a much more useful problem.
The commit becomes the evidence
Now combine the requirement with a Git commit.
The learner works on a defined task. The implementation produces a diff. The reviewer checks the diff against the requirement. Tests provide additional evidence. The learner receives feedback. Then the next task begins.
This creates a powerful educational loop: task, implementation, commit, review, feedback, next task.
The process is important because the AI does not need to replace the learner. It can act as a second pair of eyes.
AI review can become a teacher
An AI that writes code for you is an accelerator. An AI that helps you understand why your code is wrong can be a teacher.
The difference is enormous.
- ◆what is wrong
- ◆why it is wrong
- ◆what requirement it violates
- ◆what concept the learner needs to understand
- ◆what evidence would prove the fix works
A useful review should explain all of the above. It should not simply replace the learner's work with a finished implementation.
That distinction matters even more for beginners. If the AI always fixes the problem, the learner may never learn how to solve it. If the AI explains the problem and forces the learner to correct it, the feedback becomes part of the learning process.
The future of programming education
Programming education is moving toward a world where code generation is cheap. That makes judgment, verification, and feedback more valuable.
The future of learning to code may therefore be less about producing code from scratch and more about developing the ability to reason about software.
That is not the end of programming education. It may be the point where programming education finally starts looking more like real software engineering.