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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 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 →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 →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 →The seriousness of undisclosed AI use depends on more than whether a researcher used an AI tool. Its scientific significance, potential consequences, applicable disclosure requirements, and circumstances of omission all influence how the problem should be assessed.
Read Guide →A missing AI disclosure can often be addressed after publication through the journal's editorial procedures. Learn how to prepare an accurate disclosure, request a correction, and distinguish transparency amendments from problems affecting the research itself.
Read Guide →Undisclosed AI use becomes a research-integrity concern when it compromises the honesty, transparency, accountability, or reliability of research. A missing declaration alone does not necessarily establish misconduct, but certain omissions warrant investigation beyond a simple disclosure correction.
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 remain responsible for verifying consequential AI contributions before incorporating them into scientific work. Learn which claims, calculations, methodological decisions, and interpretations require checking, and how verification should reflect the risks of error.
Read Guide →Researchers should be able to explain what AI contributed, why it was appropriate, how consequential outputs were verified, and how human judgment shaped the final work. Technical detail should be sufficient for scientific scrutiny without implying that every internal model mechanism must be known.
Read Guide →Reviewers may reasonably question how AI influenced a study's methods, evidence, and conclusions. Researchers should be prepared to justify AI's role, present independent validation, explain limitations, and respond with verifiable documentation rather than relying on the tool's reputation.
Read Guide →A human approval step does not necessarily mean an AI-assisted research process has meaningful oversight. Rubber-stamping occurs when researchers approve outputs without sufficient evidence, competence, independence, or opportunity to challenge them.
Read Guide →AI verification should reflect the task and the consequences of error rather than follow one universal checking rule. Learn how to distinguish editorial review, source verification, methodological validation, and high-consequence oversight.
Read Guide →AI can help researchers generate possibilities, examine assumptions, and implement research procedures without becoming the authority behind scientific decisions. Learn how to distinguish useful assistance from unsupported AI influence and maintain evidence-based control over the research process.
Read Guide →Responsible AI-assisted research requires more than selecting a tool and checking its final output. Learn how to design a workflow that establishes appropriate AI tasks, protects research information, verifies consequential results, preserves methodological decisions, and supports scientific accountability.
Read Guide →A prepublication AI audit examines whether AI-assisted research procedures are sufficiently documented, verified, and scientifically defensible. Learn how to trace AI contributions, assess methodological errors, review confidentiality and disclosure obligations, and decide whether unresolved issues must be corrected before submission.
Read Guide →AI-generated answers can be persuasive without being scientifically correct. Learn what independent verification means, how to select suitable checks for different research tasks, and when insufficient evidence should prevent researchers from relying on an AI output.
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