Search across guide titles, topics, keywords, and content throughout the Researcher's Field Guide.
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AI performance claims can become misleading when researchers cite results without identifying the evaluated model, version, task, or testing conditions. Learn how to report these claims accurately while preserving their scientific context and limitations.
Read Guide →Expensive AI models can make it difficult for other researchers to repeat computational procedures or independently test published findings. Learn how financial barriers interact with model access, documentation, and infrastructure, and what researchers can do to make AI-assisted research more reproducible.
Read Guide →Researchers should examine the evidence behind consequential recommendations, regardless of whether they come from AI or human experts. Learn what evidence to request, how to verify supporting claims, and why persuasive explanations and professional credentials cannot substitute for scientific justification.
Read Guide →Generative AI can sometimes flag inconsistencies, coding mistakes, or methodological concerns that experienced researchers overlooked. Understand which errors AI may help identify, why its findings are not automatically reliable, and how to verify suspected problems before changing a study.
Read Guide →Major methodological changes prompted by AI should be evaluated against independent evidence before implementation. Learn how to verify an AI-generated recommendation, compare defensible alternatives, assess the consequences of changing methods, and preserve accountability throughout the research process.
Read Guide →Not every AI-generated criticism deserves a change to a research study. Learn how to distinguish productive challenges that reveal meaningful questions from unreliable suggestions based on incorrect assumptions, unsupported claims, or inappropriate methodological advice.
Read Guide →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 →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 →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 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 →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 →A missing AI disclosure does not establish whether an author misunderstood a changing policy or intentionally concealed AI use. A defensible assessment examines what the policy required, what the researcher knew, what they did, and whether the evidence supports an inference of deliberate misrepresentation.
Read Guide →Researchers need not understand every technical detail of an AI model, but they must understand enough about its contribution to evaluate the methods, verify consequential results, recognize limitations, and defend the scientific work they accept.
Read Guide →AI can suggest research questions, methods, interpretations, and publication strategies, but consequential scientific decisions must remain accountable to human researchers. Learn which decisions require human judgment and how AI can support them without becoming the authority.
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 →Researchers should be able to reproduce or independently substantiate the consequential results they report, rather than relying on AI-generated answers. The required evidence depends on whether AI contributed calculations, classifications, interpretations, or other research outputs.
Read Guide →AI outputs that materially affect research evidence, analysis, or conclusions require verification appropriate to their scientific role. Learn which tasks need independent checks, what counts as independent evidence, and when verification should go beyond asking another AI system.
Read Guide →Researchers should not enter information into AI systems merely because the technology can process it. Learn how to identify prohibited or restricted inputs, assess confidentiality and reidentification risks, and distinguish public AI services from appropriately authorized research environments.
Read Guide →Researchers may have legitimate access to information without permission to share it with an AI provider. Learn how to assess consent, confidentiality, institutional authorization, contractual restrictions, and provider processing arrangements before submitting research data to AI systems.
Read Guide →Researchers may need to explain their AI use to stakeholders with different responsibilities and expectations. Learn what each stakeholder needs to know, how to substantiate AI-assisted procedures, and why being comfortable with disclosure is a useful reflection exercise but not a substitute for compliance or scientific validity.
Read Guide →Researchers remain accountable for the scientific work they submit, even when AI contributes to writing, code, analysis, or interpretation. Learn what full responsibility means, what cannot be delegated to AI, and how researchers should respond when they cannot adequately verify or defend the final output.
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