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An AI-generated error can affect research data, analyses, citations, interpretations, or conclusions. Learn how to contain the problem, determine its scope, correct affected work, and decide whether collaborators, institutions, or journals need to be informed.
Read Guide →Discovering an AI-fabricated citation in a research draft requires more than deleting a reference. Learn how to establish whether the source exists, check the claim it was supposed to support, examine other AI-generated references, and correct the manuscript before submission.
Read Guide →AI-generated code can execute successfully while producing scientifically incorrect results. Learn how to contain the error, trace affected analyses, validate corrected code, reassess conclusions, and determine whether collaborators or journals must be informed.
Read Guide →AI misclassification can distort research categories, statistical estimates, qualitative interpretations, and scientific conclusions. Learn how to detect classification errors, assess their extent, validate corrected labels, and establish appropriate human oversight before using AI-classified data.
Read Guide →AI-assisted transcription, translation, paraphrasing, and qualitative analysis can unintentionally change what participants meant. Learn how to identify semantic distortions, restore original meaning, reassess affected interpretations, and protect the integrity of participant accounts.
Read Guide →AI-generated academic writing can introduce inaccurate statements that appear credible and well supported. Learn how to identify the problem, verify the underlying evidence, correct affected passages, audit related claims, and determine whether further research-integrity or editorial action is necessary.
Read Guide →Discovering an AI-generated error after manuscript submission creates responsibilities that depend on the error's seriousness and editorial stage. Learn how to verify the problem, assess its impact, communicate with coauthors and editors, and correct the manuscript before publication.
Read Guide →Discovering an AI-generated error after publication requires careful assessment of its consequences for the scholarly record. Learn how to verify the mistake, determine whether findings remain reliable, communicate with the journal, and distinguish corrections from retractions and other editorial actions.
Read Guide →When AI introduces an error, identifying the system as its source may explain what happened, but it does not automatically reduce a researcher's professional responsibility. Learn how accountability differs from blame, why intent and verification matter, and what researchers must do to correct AI-assisted work.
Read Guide →Not every AI-generated mistake requires a formal correction notice, but errors that materially affect published evidence or conclusions must be addressed. Learn how to assess the seriousness of AI-related inaccuracies, distinguish internal revisions from editorial corrections, and determine when more substantial action may be necessary.
Read Guide →Realizing that AI assistance should have been disclosed can create uncertainty, particularly after manuscript submission or publication. Learn how to establish which disclosure requirements apply, document the omitted contribution, correct the record, and distinguish an honest reporting mistake from more serious research-integrity concerns.
Read Guide →Discovering that a coauthor used AI without informing the research team does not automatically establish misconduct, but it may expose important gaps in verification, transparency, and collaborative oversight. Learn how to establish what AI contributed, assess potential risks, resolve disclosure obligations, and strengthen research team procedures.
Read Guide →Discovering undisclosed AI assistance after manuscript submission requires more than adding a statement. Learn how to establish what AI contributed, verify affected research, assess applicable policies, and communicate with the journal without unnecessarily disrupting peer review.
Read Guide →When undisclosed AI use is identified during peer review, authors may face questions about transparency, methodological validity, and compliance with journal policies. Learn how to respond to editorial inquiries, establish what AI contributed, verify the affected research, and distinguish disclosure problems from more serious integrity concerns.
Read Guide →Discovering undisclosed AI use after publication does not automatically invalidate a study, but it requires a careful assessment of what AI contributed, whether journal policies were breached, and whether the published record needs correction.
Read Guide →Not every undisclosed AI interaction requires notifying a journal. The decision depends on the applicable disclosure policy, what AI contributed, and whether the omission affects the accuracy, transparency, or reliability of the research.
Read Guide →AI is not appropriate for every research task. Researchers should avoid or suspend its use when they cannot verify consequential outputs, protect confidential information, satisfy applicable requirements, or defend the scientific decisions involved.
Read Guide →Researchers should not rely on unverifiable AI outputs for consequential scientific claims or decisions. The appropriate response depends on the purpose of the output, the consequences of error, and whether a credible independent validation method is available.
Read Guide →Researchers should not enter confidential research data into an AI system unless the transfer is authorized and appropriate protections are established. Learn how to evaluate confidentiality risks, consent limitations, provider arrangements, and safer research workflows.
Read Guide →Unclear AI data-use terms can prevent researchers from determining whether a tool is appropriate for confidential or restricted information. Learn which provider commitments matter, how to assess uncertainty, and when to withhold data or select another system.
Read Guide →AI can help researchers learn and implement unfamiliar methods, but it cannot replace the understanding needed to evaluate their suitability and defend the results. Learn when methodological unfamiliarity is manageable and when AI-assisted analysis should be postponed or avoided.
Read Guide →Even infrequent AI errors can be unacceptable when they expose participants to serious harm. Researchers should evaluate the consequences of failure, the reliability of safeguards, and whether a safer validated method can achieve the research objective.
Read Guide →Researchers should reconsider AI use when they cannot document enough about the model and its contribution to evaluate or reconstruct a consequential research procedure. Learn which records matter, when incomplete documentation is manageable, and when it undermines scientific defensibility.
Read Guide →Continuously changing AI models can make research procedures difficult to reproduce, particularly when model outputs determine classifications or analytical findings. Learn when model instability is manageable, how to preserve evidence, and when a fixed or conventional method is preferable.
Read Guide →Generative AI is not necessarily the best choice when a simpler validated tool can perform the same research task. Learn how to compare methodological suitability, reliability, reproducibility, verification costs, and scientific value before selecting a tool.
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