Artificial Intelligence in Research
Generative AI is most useful as a research assistant for discovering, comparing, and evaluating potential journals, not as the final authority on where to publish. Learn how to use AI efficiently while keeping consequential publication decisions grounded in verified evidence.
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Artificial Intelligence in Research
Generative AI can help draft a concise presubmission inquiry, but not every journal accepts one. Learn when an inquiry is worthwhile, what editors need to know, and how to avoid misleading claims.
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Artificial Intelligence in Research
Generative AI can help turn research findings into concise journal highlights or key points. Learn how to select meaningful findings, follow journal formatting requirements, and prevent AI-generated exaggeration.
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Artificial Intelligence in Research
Generative AI can compare a manuscript with journal author guidelines and flag possible compliance problems. Learn which checks AI can perform, which require verification, and how to avoid outdated instructions.
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Artificial Intelligence in Research
Generative AI can help researchers draft, organize, and revise grant applications, but it cannot establish a project's scientific merit or invent supporting evidence. Learn which tasks AI can assist with, where the risks arise, and how to retain responsibility for the proposal.
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Artificial Intelligence in Research
Generative AI can help researchers identify unclear objectives, methodological gaps, feasibility concerns, and weaknesses in grant proposals. However, its criticisms require verification against the actual funding criteria and scientific evidence.
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Artificial Intelligence in Research
Generative AI can simulate a skeptical grant reviewer and expose questions that applicants may overlook. Learn how to structure a realistic mock review, distinguish valid criticism from AI-generated objections, and improve a proposal without compromising its scientific rationale.
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Artificial Intelligence in Research
Uploading a confidential research grant proposal to an AI tool may expose unpublished ideas, sensitive data, or restricted information. Learn when uploading may be permissible, what protections to verify, and how to use AI without unnecessarily disclosing confidential research.
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Artificial Intelligence in Research
Research funders can restrict generative AI in grant applications and peer review, but the rules may differ considerably between applicants and reviewers. Learn how NIH and NSF policies illustrate these differences and what researchers should verify before using AI.
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Artificial Intelligence in Research
AI disclosure requirements vary among research funders, and not every use of generative AI requires the same treatment. Learn when disclosure may be mandatory, what information to report, and why transparency does not automatically make prohibited AI use acceptable.
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Artificial Intelligence in Research
Grant reviewers generally cannot upload confidential research proposals to AI tools without authorization from the funding organization. Learn why even partial uploads can breach confidentiality, how NIH and NSF policies differ, and what reviewers should do instead.
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Artificial Intelligence in Research
Generative AI can introduce confidentiality risks when reviewers process unpublished grant applications through unauthorized systems. Learn how disclosure, retention, third-party processing, and information leakage affect research grant review and what safeguards are necessary.
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Artificial Intelligence in Research
Generative AI can summarize lengthy grant applications, but reviewers must first establish whether the funder permits its use. Learn how AI summaries can omit consequential details, distort scientific arguments, and affect reviewer judgment.
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Artificial Intelligence in Research
Generative AI may identify methodological weaknesses in research grant applications, including mismatched objectives, sampling concerns, and analytical gaps. However, its criticisms can be inaccurate or inappropriate, and reviewers must consider funder restrictions before using AI.
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Artificial Intelligence in Research
AI-assisted grant scoring can reproduce or introduce biases that affect which research proposals receive favorable evaluations. Understand where these biases arise, how they can influence funding decisions, and what safeguards researchers and funders should consider.
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Artificial Intelligence in Research
Generative AI may favor familiar research approaches over unconventional proposals, potentially disadvantaging scientific novelty. Explore emerging empirical evidence, why this bias may occur, and how research funders can protect innovative ideas during evaluation.
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Artificial Intelligence in Research
AI systems trained on previous research funding decisions may reproduce inequalities embedded in those decisions. Learn how historical funding patterns become algorithmic biases, why predictive accuracy does not establish fairness, and how funders can investigate the risks.
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Artificial Intelligence in Research
Applicants may reasonably expect to know when AI contributes to evaluating their research proposals, especially when it influences scientific judgments or funding decisions. Explore what meaningful disclosure should include, how policies differ, and why transparency must be accompanied by accountability.
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Artificial Intelligence in Research
Research funders can use AI to assist with administrative screening without delegating scientific judgment. Learn which tasks are suitable for automation, where administrative checks can become consequential decisions, and what safeguards are needed.
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Artificial Intelligence in Research
AI can assist with research funding administration and selected evaluation tasks, but consequential scientific judgments require clear accountability. Learn how funders can establish defensible boundaries between AI support and human decision-making.
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Artificial Intelligence in Research
AI-assisted grant review can influence research careers, scientific priorities, and the allocation of scarce funding. Understand why these decisions are particularly consequential, how AI-related errors can cause harm, and what safeguards research funders should consider.
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Artificial Intelligence in Research
AI-generated research code can be reproducible, but generating working code is only the beginning. Researchers must preserve the code, computational environment, dependencies, data-processing decisions, and verification evidence needed for others to reproduce the analysis.
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Artificial Intelligence in Research
Not every AI-generated suggestion needs its own commit, but changes that affect research code should be traceable. Learn how researchers can use version control to preserve analytical decisions, investigate errors, and reproduce published results.
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Artificial Intelligence in Research
AI-generated research code should generally have unit tests when its functions affect data processing, calculations, or analytical results. Learn how to design meaningful tests, establish independent expected outcomes, and recognize the limits of passing test suites.
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Artificial Intelligence in Research
Researchers should define the expected behavior of AI-generated code rather than rely entirely on AI-proposed tests. Learn how independent test cases connect computational verification to research methods and help detect plausible but consequential coding errors.
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