Every time the topic of using AI for code reviews comes up, someone will eventually say that it is like letting the AI mark its own homework.
It sounds clever. It also sounds responsible. Underneath it sits a familiar discomfort about oversight, trust, and professional judgement. As an analogy, it points attention away from the part that most often fails in practice.
Disappointing AI generated code or test cases usually trace back to the same condition. The prompt was loose. It was broad. It asked for “some tests” or “a review” without defining what good looks like, what risks matter, or what constraints apply. The result is predictable. You get a large volume of artefacts that look busy and impressive while delivering very little useful assurance.
In testing, that shows up as long lists of cases that exercise obvious paths, repeat the same assertions in slightly different words, and ignore the hard parts. The gaps tend to be structural rather than subtle. They come directly from the lack of clarity in the instruction that produced them.
Seen through that lens, using AI to review AI generated output becomes far less alarming. Review work has its own shape. It involves scanning, comparing, checking for omissions, and applying criteria consistently across large volumes. That kind of analysis benefits from automation when the criteria are explicit.
Reviewing a large body of material for specific qualities, omissions, and inconsistencies is the sort of bounded work AI can support well, provided the boundaries are clear. When you tell the tool precisely what to look for, which risks matter, which assumptions should be challenged, and what counts as weak coverage, it can surface problems that humans often miss when volume and repetition set in.
This does not require trust in the AI’s judgement. It requires clarity about the lens being applied. The value comes from consistent application of that lens and from the speed at which it can be applied.
A better comparison than homework marking is spellcheck.
Spellcheck does not decide whether a document is good. It does not understand the argument or the intent. It applies explicit rules and flags potential issues for a human to deal with. When spellcheck first appeared, people were uneasy about machines judging language. Early versions were clumsy and error prone. Over time, they improved, and the practice normalised.
Today, submitting a professional document without running spellcheck does not signal integrity. It signals carelessness.
Using AI to review tests or code against explicit criteria sits in the same category of professional hygiene. It reduces noise. It highlights weak spots. It gives you something concrete to think about. The responsibility for judgement remains unchanged. It stays with the people who understand the system, the stakeholders, and the consequences of being wrong.
From a Quality Engineering perspective, this matters. Testing provides evidence, not certainty. It supports decision making. If AI helps strengthen that evidence by making gaps, assumptions, and shallow coverage visible earlier, then it is doing useful work.
The discomfort many people feel about AI reviewing AI output is understandable. That discomfort often sits alongside a second risk: large volumes of output getting accepted because they look comprehensive. The work that prevents false confidence starts with clear criteria and continues with scrutiny.
Clarity comes first. Review follows. Tools can help with both when they are used deliberately.