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Use AI as a bounded assistant for published literature, not as a default destination for your unpublished thinking. Ask it to suggest search terms, Boolean strings, databases, screening criteria, or an initial list of public papers. Keep hypotheses, draft text, proposal material, private annotations, confidential manuscripts, partner details, and identifiable data out of public or personal accounts unless your institution has approved the exact service, account configuration, contract, and data types.
Then verify every useful-looking citation and claim in the original source, keep a record of substantive AI assistance, and follow the rules of the journal, funder, institution, and any data-sharing agreement involved. No label such as “enterprise,” “private,” or “paid” proves that a service preserves confidentiality.
Why unpublished ideas need a higher level of care
A literature review can contain much more than published articles. Your notes may reveal a novel hypothesis, an unannounced method, a grant concept, a patentable implementation, a partner’s trade secret, or a participant’s identity. Uploading that material to a cloud model can create obligations or risks that do not arise when you ask for help with papers already available to the public.
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Several institutions make the boundary explicit. Columbia’s researcher guidance says, “Researchers must avoid uploading, or using as input, any unpublished research data or other Confidential Information into a Generative AI tool.” Oxford advises that sensitive commercial, intellectual-property, contractual, export-controlled, or personal information should be uploaded only when confidentiality can be guaranteed. The U.S. National Science Foundation (NSF) warns, “The agency cannot protect non-public information disclosed to third-party GAI from being recorded and shared.” These are policy statements for those organizations and processes; they are not a universal rulebook, but they illustrate why a personal account should not be your default.
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Classify material before you open an AI tool
Make a quick classification for every file, paragraph, table, and prompt. A single document can contain both public and restricted material, so classify the specific passage you plan to provide.
- Public and published: article metadata, abstracts, government reports, and text that the copyright holder permits you to process.
- Internal: working notes or project plans that are not public but do not contain regulated, contractual, or identifiable information.
- Confidential: non-public methods, draft results, reviewer comments, collaborator information, trade secrets, or material covered by a nondisclosure agreement.
- Personal or identifiable: names, contact details, coded data that can be re-identified, interview excerpts, health information, or other data covered by privacy or ethics requirements.
- Contractual or controlled: content subject to a sponsor agreement, export-control rule, data-use agreement, patent strategy, or a journal’s confidential peer-review process.
- Unpublished and potentially protectable: an original hypothesis, invention, draft proposal, or synthesis whose early disclosure could affect priority, funding, publication, or commercial rights.
If any item falls into a sensitive category, pause. Ask your institutional research-data, information-security, privacy, or technology-transfer office what is permitted under the relevant agreement. Do not paste a “sanitized” excerpt until someone has confirmed that the remaining details cannot identify people, partners, projects, or the idea itself.
What is safe enough for a public-literature workflow?
Use AI for bounded assistance that does not require your private interpretation. Appropriate starting tasks include:
- Generating synonyms and related terms for a topic you can describe publicly.
- Turning known concepts into Boolean search strings.
- Suggesting databases, subject headings, or inclusion and exclusion criteria to investigate.
- Organizing citations that you already have permission to process.
- Producing a preliminary resource list from a public question.
- Helping with readability after you remove unpublished findings, confidential context, and identifying details.
Run the resulting searches yourself in scholarly databases. Treat an AI-generated list as a set of leads, not as evidence that the papers exist, are relevant, or support a particular conclusion.
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Example prompts that keep the boundary clear
Suggest synonyms and Boolean search strings for published work on [public topic]. Do not invent citations.Using only the titles and abstracts I provide from public papers, group them by method and identify terms I should search next. Flag uncertainty.Propose screening criteria for a systematic search about [public question]. Explain what each criterion would include or exclude.Do not add your unpublished hypothesis to make the prompt “more precise.” Describe the public domain question instead, then perform the intellectual synthesis yourself.
How to use AI step by step without disclosing your ideas
- Inventory the material. Separate public bibliographic records from private notes, draft prose, proposals, reviewer files, partner information, and data. Remove sensitive attachments from the workspace you use for public-literature prompting.
- Check the destination. Confirm whether the account is personal, institutionally managed, or locally deployed. Read the current terms for retention, model training or improvement, sharing, access, deletion, processing location where relevant, and rights in inputs and outputs. A paid tier or a setting that says “do not train” does not replace a contract or institutional approval.
- Ask for discovery help. Request synonyms, database suggestions, search syntax, or a preliminary public resource list. Keep the question general enough that it does not reveal a novel combination of variables, an unannounced result, or a partner’s identity.
- Search authoritative databases yourself. Record the actual query, date, database, filters, and results. AI can miss terminology, overrepresent familiar topics, or suggest a nonexistent source.
- Screen against your protocol. Apply your inclusion and exclusion criteria to the papers, not to an AI summary. Keep reasons for exclusion in your review log.
- Verify every citation and claim. Open the original article or authoritative record. Confirm that the authors, title, journal, year, DOI, quotation, page number, sample, and conclusion match. Mark anything you cannot verify as unusable.
- Write the synthesis yourself. Compare methods, populations, limitations, and conflicting findings from the original sources. An AI summary can help you navigate a paper; it cannot supply your evidentiary judgment.
- Record substantive assistance. Save the tool name and version, date, purpose, important prompts, outputs, and corrections when your policy, publisher, funder, or good research practice calls for it.
When sensitive work might be allowed
An institutionally contracted enterprise service or a locally deployed model may support a sensitive workflow, but neither category is automatically safe. Approval must cover your specific account, configuration, data classification, and use case.
| Option | Data decision | Controls to verify | What approval means |
|---|---|---|---|
| Public or personal cloud account | Use public material only unless a written policy and contract expressly permit more. | Provider retention, training or improvement use, human or vendor access, deletion behavior, and account sharing. | A personal subscription does not establish institutional confidentiality. |
| Institutionally contracted enterprise service | Only the classifications named in your institution’s agreement and guidance. | Contract language, retention and deletion, access permissions, processing location where relevant, input/output rights, audit documentation, and the exact approved tenant or work mode. | Approval is organization-specific; another university’s enterprise approval does not transfer to yours. |
| Locally deployed model | Potentially suitable for categories your institution allows, subject to local controls. | Where data is stored and processed, administrator access, logging, backups, model-update process, deletion, security testing, and permitted data types. | Local hosting reduces some external-transfer questions but does not remove privacy, IP, ethics, or records obligations. |
Oxford identifies an institution-governed enterprise license and a locally deployed model as options to consider. UWE Bristol gives a local example: its designated service is Microsoft Copilot in the Enterprise version, and users are told to confirm the Work tab and green shield. That instruction applies to UWE users and does not prove that the same product or indicator is approved elsewhere.
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Ask your institution or provider for written answers, not marketing labels:
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- Which account, tenant, model, and configuration are covered by approval?
- Are prompts, uploaded files, and outputs retained? For how long, and in backups?
- Are inputs used to train or improve a model beyond delivering your service?
- Who can access content: your team, administrators, contractors, or other customers?
- Where is data processed and stored, and are cross-border transfers relevant?
- Can you delete content and obtain confirmation that deletion reaches logs and backups?
- What rights do you retain in inputs and outputs, and does the contract address third-party material?
- Are audit logs, incident reporting, and security documentation available?
- Does the approval cover identifiable data, confidential sponsor material, export-controlled information, or unpublished inventions?
If an answer is unclear, do not upload the material. Consult the local data-protection, research-security, records, or technology-transfer office.
How to verify AI-generated literature results
Language models can produce fluent but false references, merge findings from different papers, omit inconvenient evidence, or describe a conclusion more strongly than the authors did. Verification is part of the review method, not a final editing step.
- Search the title, author, journal, and DOI in the publisher’s site or a trusted scholarly index.
- Read the abstract and the relevant full-text section. Check the population, intervention or exposure, comparison, outcome, sample size, and stated limitations.
- Match every quotation and page detail to the original PDF or HTML. Never cite a quotation you have not seen.
- Check whether a claimed “consensus” leaves out contradictory studies, preprints, negative results, or non-English evidence relevant to your protocol.
- Use citation chaining and database searches to find work the model did not mention.
- Keep a correction log so that an attractive but unsupported sentence does not survive into the manuscript.
APA Journals advises authors to verify AI search results by reading the original sources. Its example for an AI-generated literature resource list recommends preserving the prompts and output and describing the tool and keywords in the introduction. That is APA guidance, not a universal formatting requirement.
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Publisher, funder, and institutional rules are different
Check the destination-specific rule before submission or review. Policies can differ on permitted tasks, disclosure wording, and confidentiality.
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Elsevier journals
Elsevier allows supportive uses such as synthesis, identifying research gaps, organization, and readability when authors retain human oversight and responsibility. Its policy asks authors to review accuracy, comprehensiveness, impartiality, privacy, intellectual property, and tool terms, including whether inputs are used for training beyond service delivery. It says relevant manuscript-preparation use should be disclosed. The policy also treats submitted manuscripts as confidential and says reviewers should not upload manuscripts or portions of them to AI tools. Elsevier states: “Authors must check the terms and conditions of any AI tool that they use to ensure that the privacy and confidentiality of their data and input, including their unpublished manuscripts, is maintained.”
APA Journals
APA guidance says not to enter confidential material into open tools, to verify search results against original sources, and to disclose substantive use in an appropriate part of the work. Its resource-list example calls for retaining prompts and outputs and reporting the tool and keywords. Follow the current instructions for the specific APA journal rather than treating the example as a rule for every publisher.
NSF proposals and merit review
NSF says third-party generative AI cannot protect non-public information disclosed to it. Reviewers may not upload proposal or review content to non-approved tools. NSF also encourages proposers to indicate the extent and manner of AI use in project descriptions and holds proposers responsible for accuracy and authenticity. These requirements apply to NSF’s process and roles; other funders may set different conditions.
Oxford and Columbia
Oxford tells users with personal ChatGPT accounts not to transmit internal or confidential information, advises checking training terms, and says substantive use should be declared. Columbia requires validated contract language and security controls approved through its IT and procurement processes before confidential or personal information is entered; its guidance also tells researchers to avoid uploading unpublished research data and to check output accuracy and disclose generative-AI-derived work. Both are institution-specific policies.
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What to disclose and what not to claim
Do not list an AI system as an author. Describe substantive assistance according to the destination’s current rule: for example, assistance with search-term generation, resource-list creation, language editing, or manuscript preparation. Keep enough detail for a reader or editor to understand what the system did and what you verified. If a funder asks for the extent and manner of use, answer that question directly.
Do not claim that a service was confidential merely because you deleted the chat, changed a privacy setting, used a paid plan, or saw an “enterprise” badge. State only the control your contract and institution actually establish.
Common failure modes and recovery steps
You pasted a draft hypothesis into a personal account
Stop using the account for that project. Preserve the date, prompt, files, account identity, and any available deletion or export record. Notify your supervisor and the institutional privacy, research-security, or technology-transfer contact; they can assess contractual, patent, ethics, or incident-reporting duties.
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Remove it from your working bibliography, search independently for the claimed work, and check every neighboring citation generated in the same session. Add a note to your correction log and do not rely on an unverified reference in a manuscript or proposal.
A summary concealed disagreement or bias
Return to your protocol and original papers. Search for contradictory findings, different populations, null results, and studies excluded by the model’s suggested terms. Rebuild the synthesis from the evidence you can document.
You uploaded a confidential peer-review manuscript
Do not circulate the output or use it to inform another review. Follow the journal’s incident and confidentiality guidance and contact the editor or institutional officer if required. Elsevier’s policy, for example, tells reviewers not to upload submitted manuscripts or parts of them.
A compact pre-prompt checklist
- Is every planned input public, or do I have written approval for this exact sensitive category?
- Am I using the institution-approved account, tenant, model, and configuration?
- Have I read retention, training, access, deletion, processing-location, and input/output-rights terms?
- Could the wording reveal a novel idea even after names and numbers are removed?
- Will I run the search and verify sources independently?
- Have I planned how to record prompts, outputs, corrections, and required disclosure?
- Have I checked the current rules of the target journal, funder, institution, ethics approval, and contract?
If any answer is no or uncertain, keep the material out of the tool and seek a documented local decision. The defensible use of AI in a literature review is not “upload everything and hope the settings work”; it is a staged process that limits exposure, preserves human judgment, and makes compliance visible.
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