How to check an AI explanation for mistakes

To check an AI explanation for mistakes, stop judging the paragraph and start checking the claims inside it. Here is the core checklist.

1. Pull out the specific, checkable claims.
2. Circle every number, date and name.
3. Ask for the source, then open it yourself.
4. Confirm one important claim in two independent places.
5. Test the explanation on an example you can work out.
6. Ask for the strongest objection.
7. Ask the same question again, fresh, and compare.
8. Notice confident vagueness.
9. Distrust neat reasoning that ends at a wrong answer.

You do not need all nine every time. The section on stakes, below, covers which to use when.

Why AI explanations go wrong

The US National Institute of Standards and Technology calls the problem confabulation, "the production of confidently stated but erroneous or false content." Its profile on generative AI says confabulations "are a natural result of the way generative models are designed." The models generate text that matches the statistical patterns of their training data. Often that text is accurate, and sometimes it is not.

A 2025 paper by Adam Tauman Kalai and colleagues offers one reason the errors come out sounding so sure. "Like students facing hard exam questions, large language models sometimes guess when uncertain," they write, and they argue that the way models are trained and evaluated rewards guessing over admitting uncertainty.

Two practical consequences follow. First, tone tells you nothing. A wrong answer is written in the same calm, organized voice as a right one. Second, NIST warns that AI can produce "confabulated logic or citations" that seem to justify an answer, and that models "sometimes provide logical steps for how they arrived at an answer even when the answer itself is incorrect." A tidy explanation is not evidence.

The checklist, with examples

1. Pull out the claims

Kalamazoo College's library guide to checking AI output starts here. "Take a look at the response and see if you can isolate specific, searchable claims." A paragraph about why the Roman Empire fell might contain six claims. Some are interpretation. Some are facts with dates attached. Only the facts can be checked quickly, so list them first.

2. Circle numbers, dates and names

These details are easy to get wrong and easy to repeat. Suppose a tutor tells you, "Roediger and Karpicke's 2008 study showed that testing beats rereading." The name is real and the finding is real, but their well-known testing-effect paper, "Test-enhanced learning," was published in Psychological Science in 2006. One search of the title settles it. An error like this, true in outline and wrong in a detail, is easy to miss because everything around it checks out.

3. Ask for the source, then open it yourself

Asking "Where does this come from?" is a good first move. It is not the check. Centre College's library warns that AI "is known to create very convincing fake citations." Kalamazoo's guide suggests watching for an AI "attributing a fake article to a real author."

So search the exact title. Check that the author, journal, year and DOI match. Then check the part people skip, which is whether the source actually says what the AI claims. A real paper cited for something it never found is still a wrong answer.

4. Confirm one claim in two places

In a Stanford History Education Group study, professional fact checkers judged websites better than historians and Stanford undergraduates. They did it by reading laterally. They left the site quickly and opened "a series of additional browser tabs, seeking context and perspective from other sites." The same habit works on AI. Kalamazoo's version is "Open a new tab and look for supporting pieces of information."

Make the two places independent. Two articles that both copy the same press release are one source. Mike Caulfield's SIFT method calls this tracing claims to their original context, following the claim back to where it first appeared.

5. Test with an example you can work out

Some explanations can be checked without any source at all. Say a tutor explains that a 20 percent discount followed by a 20 percent price increase brings you back to the original price. Try it with 100. A 20 percent discount gives 80. A 20 percent increase on 80 gives 96. The explanation is wrong, and you found out in ten seconds.

Ask for a worked example whenever an explanation involves a rule, a formula or a process. Then do the example yourself before reading the AI's answer.

Move the percentage slider and calculate one case yourself. Check which price each percentage is applied to.

6. Ask for the strongest objection

What is the strongest argument against what you just told me? Who makes it?

This works well for history, economics, nutrition, and anything else where experts disagree. If the tutor cannot name any disagreement on a contested topic, the explanation was probably flattened. If it names a serious one, you now know where to look.

7. Ask again, fresh

Anthropic's guidance for developers suggests running the same prompt more than once and comparing the outputs, because "inconsistencies across outputs could indicate hallucinations." You can do a reader's version. Open a new chat or page, ask the same question in different words, and compare the specifics. Stable answers are not proof. Answers that change dates or numbers between runs are a warning sign that the model is guessing.

This is also why "Are you sure?" is a weak check. Whatever the model says next, you still have to verify it somewhere else.

8. Notice confident vagueness

Some sentences sound like facts and contain none. Watch for "studies show" with no study named, "experts agree" with no expert, and "it is widely believed" with no indication of who believes it. Ask the follow-up.

Which study? Give me the authors, year and title so I can look it up.

If the answer gets vaguer, treat the claim as unsupported.

9. Distrust neat reasoning that ends somewhere wrong

Because NIST flags confabulated logic, check the conclusion separately from the steps. A chain of individually plausible steps can still arrive at a false conclusion. If the conclusion fails steps 2 to 5, it does not matter how good the steps looked.

How much checking an answer deserves

Checking every sentence would leave no time for learning anything. A rough triage helps.

NIST notes that the risk arises when people believe false content "often due to the confident nature of the response," and it singles out consequential decisions. It also describes automation bias, the tendency to defer too much to automated systems.

Where Learnfold's own warning fits

Learnfold writes its lessons with AI, and it says so on its site. AI makes mistakes. Treat it as a tutor, not a textbook, and check important facts against a reliable source.

The app makes some of the checks quicker. You can select the exact sentence you doubt and ask "Why?", "How?", or for an example. Typing your own question, such as "Which study is this from?", works on the whole page and through "Where next?". Discussions are saved, so you can come back to a question after you have checked it. If you find an error, you can fix the sentence yourself or ask the AI to rewrite that section, since every lesson is an editable page. And the optional checks at the end of a page, written answers especially, are a fair test of whether you understood the explanation or just enjoyed reading it. That matters, because rereading can make you feel more confident than your memory justifies, which is what Roediger and Karpicke saw in their 2006 study.

When this goes wrong

The source exists but does not say that. This one slips past careful people. Read the relevant paragraph of the source instead of stopping at its title and abstract.

Your two sources are really one. Trace both to where they got the claim.

You checked the easy claims and skipped the important one. Go back to the triage table. Check what you will act on first.

The AI agreed with your wrong correction. You pushed back, it apologized, and now both of you are wrong. Settle disagreements with a source, not with the model's mood.

You are exhausted by checking. Ask better questions up front, with the level and source you want. How to ask an AI tutor better questions covers that. If you are learning from an article, learning from a link without skimming it applies the same habits to the page itself, and an AI tutor you can question shows what this looks like inside a course.