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The best AI tool for research depends on what you need it to do and what could go wrong if it fails. Choose by task fit, verifiability, privacy, transparency, and research risk rather than popularity or headline capabilities.
Read Guide →AI use in research is rarely governed by a single policy. Researchers may need to comply simultaneously with laws, university rules, ethics approvals, funder conditions, journal policies, and research-team agreements.
Read Guide →Your university may permit an AI tool while a journal restricts how it can be used in submitted work. Institutional permission and publication requirements govern different parts of the research process.
Read Guide →University and journal AI policies can impose different requirements, but not every difference is a true conflict. Learn how to identify which rules apply and what to do when both cannot be satisfied as written.
Read Guide →Unpublished research is not automatically confidential, but entering it into a public AI tool can create disclosure, privacy, ownership, and policy concerns. Check what the material contains and whether you are authorized to submit it before doing so.
Read Guide →Do not assume that a confidential research proposal can be uploaded to an AI tool simply because you wrote it. Proposal content may be governed by confidentiality, institutional, funder, collaborator, intellectual-property, and data-handling requirements.
Read Guide →An unpublished manuscript is not automatically prohibited from AI use, but uploading the complete document can expose far more information than a writing task requires. Check confidentiality, co-author rights, research data, publisher rules, and the AI service before submitting it.
Read Guide →Whether reviewer comments can be entered into an AI tool depends partly on who is using them and what they contain. Authors working on their own revision face a different confidentiality situation from reviewers handling material entrusted to them during peer review.
Read Guide →A confidential grant application entrusted to you for review is not ordinary research material. Some major funders expressly prohibit reviewers from uploading applications, proposal content, critiques, or related review information to unapproved generative AI systems.
Read Guide →Having access to a collaborator's unpublished work does not automatically give you authority to submit it to an AI provider. Permission, confidentiality, ownership, research agreements, and the nature of the information all matter.
Read Guide →The safest default is simple: do not put confidential, sensitive, restricted, or third-party unpublished research into an unapproved AI tool. Use it only after you can establish authorization, appropriate safeguards, and a genuine need to disclose the information.
Read Guide →De-identification can reduce privacy risk, but ethical research involves more than preventing someone from attaching a name to a record. AI use may still raise questions about consent, secondary use, group harms, fairness, confidentiality, and whether participants could reasonably have expected their data to be used this way.
Read Guide →Generative AI can help researchers interrogate why a research question might matter, compare possible contributions, and expose weak rationales. It cannot independently determine scientific importance without reliable evidence and disciplinary context.
Read Guide →Generative AI can help researchers identify feasibility constraints and stress-test a proposed study. Its assessment is only as reliable as the project-specific information it receives about methods, access, resources, ethics, expertise, and time.
Read Guide →Generative AI can be used as an adversarial research-planning partner that deliberately searches for weaknesses in a proposed study. Its criticisms can sharpen reasoning, but they must themselves be checked rather than treated as expert verdicts.
Read Guide →Asking generative AI to recommend research papers may help you discover useful studies, but it does not necessarily constitute a literature search. The difference lies in how publications are retrieved, selected, verified, and documented.
Read Guide →Generative AI can help researchers convert research questions into Boolean search strings by organizing concepts, synonyms, and search operators. However, AI-generated queries must be checked for conceptual accuracy, database compatibility, and retrieval performance before they are used in a literature review.
Read Guide →Generative AI can help adapt literature search strategies between scholarly databases by converting field codes, operators, and query structures. However, accurate translation requires more than changing syntax because databases differ in indexing, controlled vocabulary, search behavior, and coverage.
Read Guide →Generative AI can help identify recently published scientific research, particularly when connected to current scholarly retrieval sources. However, finding the most recent studies requires distinguishing publication dates from indexing dates, verifying source coverage, and accounting for delays in database updates.
Read Guide →Researchers should document AI assistance according to how it influenced literature discovery, search strategy development, retrieval, and screening. A useful record distinguishes AI-generated suggestions from searches actually executed and preserves enough information to evaluate important decisions.
Read Guide →An AI-generated citation may look authentic even when the publication does not exist. Learn a practical verification process for checking article titles, authors, DOIs, publisher records, and bibliographic inconsistencies before citing a source.
Read Guide →Finding a real research paper is only the beginning. Before citing an AI-suggested publication, researchers must verify its bibliographic identity, examine what the study actually found, assess its relevance, and check whether the evidence supports the manuscript's claim.
Read Guide →A DOI can identify a genuine publication without confirming that the paper supports the claim attached to it. Learn the difference between DOI verification and evidence verification, and how to check whether a citation genuinely supports your argument.
Read Guide →When AI inserts a fabricated reference into a manuscript, responsibility does not transfer to the software. Learn how accountability is distributed among authors, coauthors, editors, and publishers, and what researchers should do when fabricated citations are discovered.
Read Guide →Using AI with academic references does not have to mean risking fabricated citations or unsupported claims. Follow a practical workflow that separates literature discovery, source verification, evidence evaluation, reference management, and final manuscript checks.
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