Search across guide titles, topics, keywords, and content throughout the Researcher's Field Guide.
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AI can identify limitations acknowledged by researchers and suggest additional concerns, but it may confuse the two. Learn how to verify the source of each limitation and avoid presenting AI-generated interpretations as author statements.
Read Guide →Generative AI can assist with critical appraisal by examining research methods, identifying possible bias, and questioning conclusions. Learn how to use established appraisal frameworks without treating AI-generated judgments as verified assessments.
Read Guide →Generative AI may identify methodological problems that research authors did not acknowledge, but it can also invent weaknesses or misapply methodological standards. Learn how to distinguish evidence-supported concerns from speculative criticism.
Read Guide →Generative AI can support systematic review tasks ranging from search preparation and screening to data extraction and reporting. Learn which applications are promising, which require closer scrutiny, and how to decide where AI belongs in your review workflow.
Read Guide →AI-recommended study designs may appear complete while containing unsupported assumptions, validity threats, or impractical procedures. Learn why methodological review matters, what reviewers should examine, and how to use AI recommendations without surrendering scientific responsibility.
Read Guide →Using generative AI to interpret statistical results does not transfer responsibility for the findings to the technology. Learn what researchers must verify, how accountability differs from AI disclosure, and what to do when an AI-assisted interpretation contains an error.
Read Guide →AI-generated statistical code can run successfully while producing incorrect results. Learn how researchers can verify data transformations, statistical methods, model assumptions, and numerical output using systematic testing and independent validation.
Read Guide →AI-generated statistical code may appear correct while concealing methodological or computational errors. Learn when independent review is warranted, what a qualified reviewer should examine, and how code review can strengthen research reliability without replacing author accountability.
Read Guide →Original scientific images provide the evidential record needed to verify processing, measurements, and published findings. Learn what researchers should preserve, why exported images may not be sufficient, how to manage AI-generated modifications, and what determines appropriate retention periods.
Read Guide →Does editing one sentence with AI require disclosure while rewriting an entire manuscript does not? The answer is not determined by word count alone. Learn how publishers distinguish routine corrections from substantive changes, why percentages can be misleading, and how researchers should evaluate AI-assisted editing for accurate disclosure.
Read Guide →When generative AI introduces errors during academic language editing, who is responsible: the researcher, the AI provider, or the journal? Learn why human authors remain accountable for submitted manuscripts, how editorial responsibilities differ, and what researchers should do when AI-generated revisions introduce scientific inaccuracies.
Read Guide →AI can make difficult research passages easier to read, but a fluent paraphrase does not prove that either the researcher or the AI has understood the source correctly. Learn why comprehension should precede paraphrasing and how to use AI to clarify unfamiliar academic material without misrepresenting it.
Read Guide →Submitting AI-generated academic text as your own is not automatically plagiarism under every academic-integrity framework. However, it may involve misrepresentation, prohibited assistance, nondisclosure, or unattributed borrowing. Learn how to distinguish these concerns and evaluate what journals actually require.
Read Guide →Undisclosed generative AI use can raise authorship, research-integrity, and publication-policy concerns, but these are not interchangeable classifications. Learn how to distinguish them, when nondisclosure becomes consequential, and how to assess AI assistance against the requirements of a journal or institution.
Read Guide →Generative AI may reproduce language from a publication the researcher has never encountered. Although human authors remain accountable for submitted material, an unexpected textual match does not automatically establish deliberate plagiarism or formal misconduct. Learn how responsibility, intent, verification, and correction should be distinguished.
Read Guide →AI-text detectors may appear useful for journal screening, but their classifications cannot independently establish undisclosed AI use or misconduct. Learn what publisher guidance says and how editors can evaluate AI-related concerns without compromising confidentiality or editorial fairness.
Read Guide →AI detectors may provide limited information during research-integrity investigations, but fairness requires much more than human review of a score. Learn how investigators should assess reliability, establish policy violations, protect procedural rights, and avoid unsupported misconduct findings.
Read Guide →AI systems can generate scholarly text and assist with research, but they cannot assume the professional and ethical responsibilities attached to academic authorship. Understand what accountability requires and why human researchers remain answerable for AI-assisted work.
Read Guide →Extensive AI assistance does not reduce a researcher's responsibility for the accuracy and integrity of a manuscript. Learn how authorship accountability applies when AI contributes to writing, analysis, interpretation, and other substantive research activities.
Read Guide →AI use may need to be reported in the acknowledgments, methods section, a dedicated declaration, or multiple submission locations. Learn how to select the correct placement according to the tool's role, journal requirements, and the information readers need to evaluate the research.
Read Guide →Ambiguous journal AI disclosure policies can leave researchers uncertain about what to report and where. Learn how to distinguish policy silence from permission, reconcile publisher and journal instructions, request editorial clarification, and document AI assistance without inventing requirements.
Read Guide →Researchers should consider keeping records of consequential AI assistance, particularly when it influences research methods, evidence, analysis, or findings. Learn what documentation can establish, which interactions warrant preservation, and how AI-use records support transparency without requiring researchers to archive every conversation.
Read Guide →Researchers should document how consequential AI outputs were verified, particularly when they influence research methods, evidence, analysis, or findings. Learn what constitutes meaningful verification, how to preserve supporting evidence, and why claiming that AI-generated content was reviewed is not equivalent to demonstrating that it was checked.
Read Guide →Using AI during peer review can violate confidentiality when unpublished manuscript information is disclosed to an unauthorized system. Learn why confidentiality extends beyond uploading PDFs, how publisher policies differ, and what safeguards reviewers must observe.
Read Guide →Generative AI may help reviewers make their comments clearer, more constructive, and professionally worded. However, even language editing can introduce confidentiality risks or change the meaning of a criticism. Learn when such assistance may be permitted and how reviewers should verify the final report.
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