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Use AI to speed up brainstorming and searching, but treat every important factual statement it produces as unverified until you check it against the original evidence. Break the answer into individual claims, locate the underlying paper, dataset, law or institutional report, and confirm that the source exists and actually supports the wording. Check dates, definitions, limitations and competing evidence, then record what you verified. The House of Commons Library’s concise rule is useful: “AI should be treated as an assistant, not an authority.” This workflow helps you catch invented citations, distorted statistics, outdated rules and confident but unsupported conclusions before they reach an essay, dissertation or policy document.
Why should I double-check every important AI claim?
Generative AI produces plausible language by predicting patterns; it does not guarantee that a statement is true, current or supported by a source. Carnegie Mellon University Qatar Library describes generative AI as an algorithm that generates text from patterns in training data, not as an information source. Fluency and confidence therefore provide no evidence of accuracy.
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A response can contain a real article paired with the wrong finding, a genuine statistic stripped of its population or date, or a completely invented reference that looks scholarly. A small error may be harmless in a brainstorming session but serious when it affects a grade, clinical recommendation, public policy, legal interpretation or someone’s reputation. University of Nevada, Reno guidance recommends more verification as the impact of an error rises.
Use AI for tasks such as generating search terms, outlining possibilities or explaining unfamiliar vocabulary. Make your evidence decisions from documents you can inspect, and remain responsible for the final claims.
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Which parts of an AI answer need checking first?
Start by marking claims that could be tested or disputed. Prioritise details whose failure would materially change your conclusion.
- Dates and current rules: publication years, software versions, regulations, election results and other information that can change.
- Names and identities: authors, organisations, journals, datasets, court cases and places.
- Numbers: percentages, sample sizes, effect sizes, rankings, costs and forecasts.
- Quotations: any wording presented as someone’s exact words.
- Causal statements: claims that one factor produced an outcome rather than merely occurring alongside it.
- Contested interpretations: assertions about disputed history, politics, ethics or social effects.
- Scope statements: words such as “all,” “ proves,” “never,” or “the first,” which may exceed the evidence.
Give low-stakes background facts a quick source check, but reserve full-text reading and independent corroboration for claims that drive your thesis or could cause harm.
How do I turn an AI response into checkable claims?
Rewrite each important sentence as a proposition that could be confirmed or rejected. This prevents a paragraph’s persuasive tone from hiding several separate assertions.
- Underline the factual units. Separate the date, population, measurement, comparison and conclusion in a single sentence.
- Label the claim type. Note whether it is a quotation, statistic, definition, observation, correlation, causal explanation or prediction.
- Record the required evidence. A quotation needs the original passage; a statistic needs its table or dataset; a legal claim needs the current text of the law or official guidance.
- Set a checking level. Decide whether an identifier check is enough or whether you need the complete document, a second source and expert review.
For example, turn “A 2022 study proved remote learning reduced attainment by 15 percent” into separate checks: Does the study exist? Was it published in 2022? What population and outcome did it measure? Is 15 percent the reported estimate, and does the design support a causal conclusion?
Where can I find the original evidence?
Trace the claim to the source closest to the underlying evidence instead of relying on an AI summary or a page that repeats it.
- Search the complete citation. Copy the title, author names, journal, year and distinctive phrase into a library catalogue or scholarly database.
- Resolve the identifier. Check a DOI for an article, PMID for a biomedical paper, ISBN for a book, or the issuing body and document number for a report or law.
- Use official repositories. Government agencies, regulators, courts, universities, statistical offices and dataset hosts are preferable for rules, measurements and administrative data.
- Open the primary document. Read the abstract and the relevant methods, results, table, footnote or legal section instead of stopping at a search snippet.
- Save the version you checked. Keep the access date, publication or revision date and a stable copy or record, especially when the source can change.
QUT’s guidance advises tracing claims, quotes, statistics and references back to their origin. If you cannot identify an origin, treat the statement as unverified rather than filling the gap with a second AI response.
How can I tell whether an AI citation is real?
Verify both the existence of the work and the identity details. A citation that looks academic can still be fabricated or subtly altered.
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- Search the exact title in a library catalogue, the journal or publisher’s archive, and a recognised scholarly index.
- Check that the author list, journal, volume, issue, pages and year match across records.
- Open the DOI or other identifier and confirm that it resolves to the same work, not a similarly titled item.
- For books, verify the ISBN and edition. For reports, confirm the issuing institution, report number and publication page.
- Inspect the cited passage, table or figure. Confirm that it contains the proposition attributed to it.
The University of British Columbia Library warns that you need to search to confirm articles actually exist, while the University of Washington recommends direct searching and DOI checks. If no credible record appears after searching the complete citation, reject it; do not cite an untraceable item or ask AI to invent a replacement.
How do I check that a real source supports the AI’s wording?
Read beyond the title and abstract. A source may be genuine while the AI has changed its meaning, population or certainty.
Compare the study design and population
Match the AI’s description with the methods: observational studies show association, whereas randomised experiments can provide stronger evidence for causation. Check who participated, how many people were included, where and when the work took place, and whether the result applies to your population.
Match the number and its definition
Find the exact table, figure or dataset row. Determine whether a percentage is a proportion, percentage-point change, relative risk or modelled estimate. Check denominators, missing data, confidence intervals and whether the number is preliminary or revised.
Read limitations and qualifying language
Look for terms such as “may,” “associated with,” “in this sample” and “under these conditions.” If AI changes a cautious conclusion into “proves,” “causes” or “works for everyone,” restore the source’s level of certainty.
Verify quotations word for word
Use the document’s search function or page numbers to locate the exact sentence. Check whether ellipses, translation or surrounding paragraphs change the meaning. If you cannot locate the words, do not present them as a quotation.
Should I compare independent sources?
Yes, especially for consequential, contested or fast-changing claims. Independent corroboration tests whether the result survives a different method or institution; it is not a reason to average contradictory findings.
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- Choose a second reputable source that is not merely copying the first.
- Prefer different evidence streams, such as an official dataset alongside a peer-reviewed analysis.
- Compare definitions, dates, populations and measurement methods before comparing numbers.
- Investigate disagreements. Differences may reflect revisions, sampling, a different outcome or a genuine unresolved debate.
For stable background facts, one authoritative primary source may be sufficient. For a disputed conclusion or a claim with safety, policy or reputational consequences, seek multiple sources and explain meaningful uncertainty.
How do I assess source quality and context?
Use the CRAAP framework—currency, relevance, authority, accuracy and purpose—then apply it to the exact claim rather than assigning a permanent quality label to a website.
| Check | Questions to ask |
|---|---|
| Currency | When was it published or revised? Is the information still valid for the date and version in your assignment? |
| Relevance | Does it answer your precise question, population and geography, or only a nearby one? |
| Authority | Who produced it, what expertise or mandate do they have, and can you verify their identity? |
| Accuracy | Are methods, references, data and limitations visible? Can another source corroborate the result? |
| Purpose | Is the aim research, regulation, education, advocacy, marketing or entertainment, and how might that aim shape the presentation? |
Also ask what the AI omitted. A summary may leave out a control group, a conflicting result, a narrow eligibility criterion or a study limitation. The University of Alberta’s Check, Analyze, Reflect approach adds three useful tests: inspect the logic and missing perspectives, challenge assumptions and distinguish fact from speculation, then consider the consequences if you rely on an error.
What should I do when AI frames a topic confidently or selectively?
Rephrase the question and request evidence from more than one perspective, but treat the new output as another set of leads to verify. Separate the source’s observations from your interpretation of them.
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- Search for reputable sources that disagree, including methodological critiques or updated analyses.
- Check whether the selected examples represent the whole field or only an unusual case.
- Identify assumptions about values, definitions or causation that are not stated.
- State unresolved uncertainty in your writing instead of forcing a single conclusion.
This process is particularly important for political, historical, medical and social claims, where selection and framing can mislead even when individual sentences are technically true.
How do I handle common AI research failures?
Fabricated references
Search the full citation, verify the DOI, PMID or ISBN, and inspect the journal or publisher record. Reject an item that cannot be found in a credible database; a plausible title is not evidence that the work exists.
Real source, wrong claim
Read the cited section and compare its definitions, sample, date and limitations with the AI’s summary. Rewrite the claim to fit the source or find evidence that genuinely supports the stronger wording.
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Outdated information
Check publication and revision dates, then confirm current rules, software versions, statistics or events with the responsible authority. Cite the version or date you actually used.
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Invented quotations or numbers
Locate the exact sentence, table or dataset entry. If you cannot locate it, remove it; never repeat an attractive quote or statistic merely because another generated answer supplies the same words.
Confident but biased framing
Ask what evidence is missing, compare opposing interpretations and distinguish description from opinion. Record why you selected the sources that remain.
No authoritative, directly comparable figure establishes a general rate of false academic citations produced by AI. Avoid inserting a percentage unless a credible study defines the population, test method and date clearly enough to support it.
How much verification is enough for different claims?
Match effort to stakes, source type, independence, depth and currency. The following is a practical minimum, not a substitute for disciplinary rules.
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|---|---|---|
| Low-stakes, stable background fact | Confirm with one authoritative source and record the date. | The fact is disputed, unusually specific or central to your argument. |
| Important statistic or quotation | Locate the original table or passage and verify definitions and context. | The number drives your conclusion or differs across sources. |
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| Contested or high-impact claim | Use multiple reputable sources and document your reasoning. | Sources conflict or you lack subject expertise; obtain expert or librarian review. |
How should I document AI use and cite sources?
Follow your course, journal or publisher’s policy on disclosure. Save the prompt, relevant output, date and any edits when documentation is required, but cite the original books, articles, datasets, laws and websites that support your prose.
Keep an audit trail showing the claim you checked, the source consulted, the passage or data point that supports it, and any correction you made. This makes later updates possible and demonstrates that your references are evidence rather than decoration. Carnegie Mellon University Qatar Library advises documenting AI use, verifying information, confirming source support and citing original sources instead of AI output.
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Do not cite a chatbot’s wording as though it conducted the study. If your institution requires an AI-use statement, describe the tool and purpose accurately without treating the generated text as an authority.
When should a person review my research?
Ask a subject expert, librarian or supervisor to examine unresolved or high-stakes claims. Human review is especially valuable when sources conflict, terminology is specialised, the evidence is unpublished, or an error could affect safety, policy, grades or reputation.
Provide the reviewer with your claim list, primary documents, comparison notes and specific questions. The parliamentary workflow for evaluating AI-assisted material emphasises subject-expert review and makes clear that the author remains responsible for the final text; review improves judgment but does not transfer accountability.
What is the final pre-submission checklist?
- Every material AI-generated claim has been split into a checkable proposition.
- Each citation exists in a credible catalogue, index, publisher archive or official record.
- Identifiers resolve to the cited work and match its authors, date and edition.
- The original passage, table or dataset supports the exact wording you use.
- Numbers include their population, denominator, date, unit and uncertainty where relevant.
- Quotes are exact and traceable to a page, section or timestamp.
- Current rules and statistics come from the latest authoritative version.
- At least one independent source checks consequential or contested claims.
- Limitations, opposing evidence and missing perspectives are represented fairly.
- AI use is disclosed and original sources—not generated prose—are cited.
- Unresolved high-impact issues have been reviewed by a qualified human.
Frequently Asked Questions
Can AI search results replace my university library database?
No. Use generated suggestions to build keywords, synonyms and likely authors, then run those searches in your library catalogue and subject databases. Library records expose editions, indexing details and holdings that a generated list may omit.
What if two authoritative sources report different statistics?
Check whether they use different years, populations, definitions or revision dates before deciding that one is wrong. If the methods are both sound, report the disagreement and explain which measure best fits your question.
Should I keep a copy of an online source that may change?
Yes, when your assignment or policy allows it. Record the access date and revision date, save a permitted PDF or archival reference, and note the exact section you relied on so a reader can reconstruct your check.
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No. Peer review does not make a study suitable for every population, date or interpretation. Examine its design, sample, measures, funding or conflicts where disclosed, and limitations before extending its conclusion.
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