How to Check AI-Generated Research Notes Against the Papers

AI notes are most useful when you treat them as a list of claims to verify, not as finished prose. The fastest reliable method is to sample the highest-risk claims first, trace each one to a specific location in a specific paper, confirm the number and the population it describes, and log anything you cannot confirm into a correction queue. This article gives you that sequence, a worked example under a deadline, a reusable checklist, and clear rules for when to stop checking and when to escalate.

You rarely have time to re-read every source behind every sentence. Sampling works because risk is unevenly distributed: some claims are easy to get right and cheap to fix, while others carry most of the damage if wrong. Cochrane's guidance on searching and selecting studies is built on the same logic — plan from explicit criteria, document the process, and screen in stages rather than treating everything as equally urgent. Apply that staged thinking to verification: screen your notes in passes, from highest risk to lowest.

Break your notes into atomic claims — one fact, number, or finding per row. A sentence with two numbers is two claims. A sentence that combines a result with an interpretation is two claims of different types.

Label each claim with a type:

• Number — a statistic, sample size, effect, or percentage.
• Population — who or what the finding describes.
• Attribution — which paper or author the claim is credited to.
• Inference — a conclusion you or the AI drew that is not stated in the source.

This inventory is your work queue. Everything downstream operates on rows, not paragraphs.

You cannot verify everything under a deadline, so rank. Prioritize a claim when it is:

1. Quantitative — any number is a high-risk claim because digits are easy to transpose or detach from context.
2. Load-bearing — the argument in your paragraph collapses if the claim is wrong.
3. Attribution-specific — it names a paper, author, or year that a reader could look up.
4. Population-shifting — it generalizes a finding beyond the group actually studied.
5. Unfamiliar — it cites a paper you have not read yourself.

A practical sample: check every number and every named attribution, then a spread of the remaining claims across your sections. If your notes are short, check all of them; if long, this ranking still tells you which rows to check first when time runs out.

"It's in the paper somewhere" is not verification. For each sampled claim, record the exact location: section, paragraph, table or figure number, and page if available. Cochrane's emphasis on documenting the search and selection process applies here too — a trace you cannot reproduce is not a trace.

If a claim has no locatable source, that is a finding, not a dead end. Move it straight to the correction queue with the status "unsourced."

When you need to find the underlying paper in the first place, use a scholarly search interface rather than a general web search. WisPaper search lets you look for academic papers directly; once you have candidates, save the ones you will actually cite so your trace targets stay in one place using My Library.

A number is only correct relative to the population it describes. Check them as a pair:

• Does the number match the source exactly — same units, same denominator, same rounding?
• Does the population in your note match the population in the study?
• Is the number from the study's own results, or from its introduction citing another study?

That last point catches a common error: a figure quoted in a paper's background section may belong to a different study entirely. Trace it to the original if your sentence attributes it to the paper you are reading.

Separate what the source says from what you concluded. Inference is legitimate in a paper — it just needs to be visible. Rewrite inferred sentences with markers such as "this suggests," "taken together," or "we interpret this as," and keep them out of any sentence that reads as a reported finding.

A useful test: could a reader tell, from your sentence alone, whether the authors said it or you did? If not, mark it.

Every failed check becomes a row with a status and an action. Keep the queue in the same document as your claim inventory so nothing gets lost between sessions.

Statuses to use consistently: Verified, Fix, Unsourced, Mark as inference, Escalate. Close rows only when the action is done, not when you have decided what to do.

Suppose you have a 2,500-word course paper due in four days, a rubric that weights "use of evidence" heavily, and library access that covers some but not all of your sources. Your AI-assisted notes contain about 40 claims.

Day 1 — inventory and sample. You split the notes into 40 rows and tag types. You flag 9 numbers, 6 named attributions, and 5 sentences that read as findings but may be your own inference. That is your high-risk set of 20.

Day 2 — trace. You trace the 20 rows to locations. Twelve verify cleanly. Three numbers turn out to be from a different study cited inside your source's introduction — you retrace them to the original or cut them. Two attributions have no locatable source and go to the queue as unsourced. Three population statements are broader than the studied group and get rewritten.

Day 3 — inference and gaps. You mark the five inference sentences. For the two unsourced rows, you search for the underlying paper; if you find it, you add it to your library and re-trace. If you cannot locate it, you cut the claim rather than leave it unsupported. Where you need to interrogate a paper you have already saved, Library QA lets you ask questions based on papers uploaded to My Library or added from search results, which helps you locate the passage behind a claim before you record its location.

Day 4 — close the queue and decide. You re-read only the rows marked Fix or Mark, confirm each action is done, and check that no inference sentence still reads as a reported finding. Then you make the submission decision below.

Run these checks at fixed points so problems surface before they compound:

• After inventory: Does every paragraph have at least one row? Rows with no source are already visible.
• After tracing: What share of sampled claims have a location? Any claim without one is queued, not excused.
• After number/population checks: Do any numbers appear in more than one place in your notes? Duplicated numbers drift — check each occurrence.
• Before submission: Is every queue row closed? Are inference markers present wherever the sentence is yours?

Set these before you start so you are not negotiating with yourself at midnight.

Stop checking a claim when: it has a located source, the number and population match, and the attribution is correct. Record it as Verified and move on.

Escalate to your supervisor or instructor when:

• A load-bearing claim cannot be sourced after a genuine search.
• Two sources appear to conflict on a number your argument depends on.
• A claim's population is so different from your paper's scope that rewriting it changes your argument.
• You are unsure whether a paraphrase crosses into misattribution.

Stop the session when: the queue has grown faster than you can close it. That is a signal to cut scope — remove the weakest claims rather than verify everything shallowly.

• Notes broken into atomic claims with types assigned
• High-risk sample selected (all numbers, all named attributions, spread of the rest)
• Every sampled claim has a source and a location (section, table/figure, page)
• Numbers match exactly, including units and denominators
• Populations in notes match populations studied
• Figures quoted from a paper's background traced to the original study
• Inference sentences marked as inference
• Correction queue has no open rows
• Escalations sent and responses incorporated

Submit when every high-risk row is Verified or closed, every inference is marked, and no unsourced claim remains in a load-bearing sentence. If unsourced rows remain but sit in low-stakes supporting sentences, cut those sentences and submit. If an unsourced or conflicting claim is load-bearing and you have not heard back from your supervisor, do not submit that section as written — remove the claim, adjust the argument, and note the change. The decision rule is about the state of your queue, not about how confident the notes felt when you first read them.