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AI can help researchers search and review literature, explore hypotheses, analyze data and images, build models, design molecules or materials, and draft or edit research documents. It cannot make those outputs trustworthy simply by making them sound convincing. Researchers still have to verify evidence, judge whether a method fits the question, protect sensitive information, document relevant AI use, and take responsibility for the scientific work.
What can AI actually do in scientific research?
AI is useful in research as a set of task-specific aids, not as a single all-purpose scientific method. A 2025 ethics review describes uses across literature work, analysis, modeling, design, synthetic data, writing, and review. Whether a particular system is suitable depends on the discipline, the task, the data, and the researcher’s ability to evaluate its output.
| Research task | Potential assistance | What still needs human judgment |
|---|---|---|
| Literature search and review | Help locate, organize, or summarize material relevant to a question. | Confirm that sources exist, are relevant, and support the claims attributed to them; assess what the search may have missed. |
| Hypothesis generation | Suggest candidate explanations, alternatives, or questions to investigate. | Decide whether a hypothesis is meaningful, testable, and consistent with established evidence. |
| Data and image analysis | Assist with analysis or interpretation of research data and images. | Check that the method fits the data and research design; inspect errors, bias, and the limits of interpretation. |
| Modeling and design | Support modeling and the design of molecules or materials. | Determine whether model assumptions are appropriate and whether predictions hold up to independent testing. |
| Synthetic data | Generate data that may be used to validate a hypothesis or model. | Keep synthetic data distinct from observations and explain how and why it was generated and used. |
| Research writing and review | Assist with drafting or editing papers, grant proposals, consent forms, IRB applications, and reviews of research outputs. | Verify every factual claim, meet applicable disclosure rules, and ensure the final work reflects the researchers’ evidence and decisions. |
NIH’s ninth-edition Guidelines for the Conduct of Research, published in May 2026, also describes AI uses in experiment planning, data analysis and presentation, writing, and logistical tasks. It uses the term “AI co-scientists” for systems that may synthesize literature, generate hypotheses and plans, run experiments or models, suggest interpretations and alternatives, and draft text. These are descriptions of possible system capabilities—not evidence that a system independently performs reliable science.
Can AI make scientific discoveries?
AI can contribute to discovery by helping researchers explore possibilities: generating several hypotheses, testing or revising assumptions, proposing alternatives, and pointing to potential gaps. A 2024 National Academies workshop chapter records discussion of both this kind of assistance and the hurdles to AI conducting independent scientific discovery.
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The distinction is between generating a promising possibility and establishing a scientific result. A suggested explanation is not validated because a model produced it, and a predicted result is not an observation. Researchers must still decide how to test a proposal, conduct or evaluate the relevant work, and determine what the evidence supports. AI may widen or speed up exploration; empirical validation remains a separate step.
Can you trust AI-generated research, explanations, or citations?
Not without checking them. Fluent wording and plausible reasoning are not evidence that a claim is true. A generated reference can be fabricated or inaccurately described, and a correct-looking summary can omit an important limitation. NIH warns that AI use in grant preparation may lead to plagiarism, fabricated citations, or other research misconduct. The practical standard is to verify important claims and references against the original sources, not to treat the generated answer as its own evidence.
AI-related risks also include bias and random errors, challenges to attribution and reproducibility, and confusion between synthetic and observed data. The authors of a 2025 ethics review state that “Researchers are responsible for identifying, describing, reducing, and controlling AI-related biases and random errors.” That responsibility includes considering how a system’s use could affect communities represented in the data and consulting affected stakeholders where appropriate.
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Keep synthetic data visibly separate
Do not present generated or synthetic material as data collected from the world. When synthetic data are used, clearly identify them, describe how they were generated, and explain why and how they entered the analysis. Keep enough provenance to let others understand the data’s origin and role.
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Do not assign AI human accountability
An AI system should not be named as an author, inventor, or copyright holder. Describe and disclose its contribution instead, following the applicable rules. A tool cannot take responsibility for the validity, integrity, or consequences of the work.
No single accuracy or error-rate figure applies across disciplines and AI systems. Performance depends on the task and conditions, so a general reliability percentage would not tell a researcher whether a particular output is dependable.
How should researchers use AI responsibly?
Use AI where it can help with a defined task and where you have enough expertise to evaluate its output. Before using a tool, decide what it is allowed to do in the workflow, what evidence will be checked independently, and what information must not be shared. A practical process is:
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- Assess fit and expertise. Consider whether the tool is appropriate for the subject, data, and method, and whether someone on the team can recognize a bad answer or an unsuitable analysis.
- Check rules and permissions before entering material. Review institutional, funder, journal, privacy, cybersecurity, and data-use requirements. Confirm that the tool and its terms are approved for any unpublished, participant, proprietary, or controlled-access information involved.
- Verify output against evidence. Trace factual statements and references back to original sources. Inspect analytical choices and results rather than relying on a generated explanation of them.
- Preserve a record. Record relevant tool use, what it contributed, how outputs were checked, and any limitations that affect interpretation. For synthetic data, retain provenance about generation and use.
- Disclose clearly. Follow applicable journal, funder, and institutional disclosure requirements. Describe the contribution in language readers can understand and do not imply that AI-generated material was observed or independently verified when it was not.
Good documentation is more than naming a tool: it helps readers and collaborators understand where AI entered the workflow and how its contribution was evaluated. The required form of disclosure varies, so researchers should check the policy governing their particular project or submission.
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What U.S. federal policies should researchers know?
Federal examples illustrate why role and context matter. The policies below concern specific NIH or NSF activities; they are not a universal rulebook for every institution, journal, funder, or country. Check the current terms for the specific submission or data access involved.
NSF merit review: restrictions on reviewers
An NSF notice dated December 14, 2023, prohibits reviewers from uploading proposal content, review information, or related records to non-approved generative AI tools. The notice encourages proposers to indicate whether and how generative AI was used in developing a proposal. NSF states: “Proposers are responsible for the accuracy and authenticity of their proposal submission in consideration for merit review, including content developed with the assistance of generative AI tools.” The restriction on reviewers should not be misread as a general ban on AI use in research.
NIH applications: originality and AI-developed material
NIH Notice NOT-OD-25-132, released July 17, 2025, applies to applications submitted for receipt dates beginning September 25, 2025. It says that applications substantially developed by AI, or containing sections substantially developed by AI, will not be considered original ideas of the applicants. The notice also raises concerns about plagiarism, fabricated citations, and research misconduct. Because the rule concerns applications and uses a substantial-development standard, researchers should check current NIH terms and any applicable exceptions before deciding how it applies to a particular submission.
The notice also reported that NIH had evidence of principal investigators using AI tools to submit more than 40 distinct applications in a single submission round. This is an agency-reported observation in the context of the policy notice, not a measure of how common AI use is among researchers generally.
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NIH conduct guidance: responsibility stays with the scientist
The ninth edition of NIH’s Guidelines for the Conduct of Research appeared in May 2026. It encourages responsible AI use while directing scientists to follow relevant NIH restrictions, applicable cybersecurity guidance, and changing journal policies. Its guidance emphasizes integrity, quality, reproducibility, privacy, intellectual property, and transparency. It places responsibility for publication accuracy and integrity with the individual scientist who uses AI assistance.
Controlled-access genomic information: check data-use terms
In a March 28, 2025 notice, NIH reminded researchers that the Genomic Data Sharing Policy and Data Use Certification prohibit distributing controlled-access genomic or associated data, and derivatives, to unauthorized people or organizations without appropriate written approvals. Do not enter protected data into an AI tool unless the data-use terms and required institutional approvals permit it. A data derivative is not automatically safe to share simply because it has been transformed.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should you evaluate an AI tool for research?
There is no single best tool established for scientific research across tasks. Evaluate the tool against the work it will actually perform, rather than relying on a general claim that it is “good at research.” Useful questions include:
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- Evidence traceability: Can researchers inspect the sources, inputs, assumptions, and transformations behind its output?
- Error and bias controls: What checks can reveal mistakes, systematic distortions, or impacts on represented groups?
- Reproducibility: Can the steps and relevant settings be recorded well enough for collaborators to understand or repeat the work?
- Privacy and confidentiality: Are the tool’s terms and approved use compatible with the data’s restrictions and the project’s obligations?
- Policy compatibility: Does use fit the requirements of the institution, funder, journal, and any data-sharing agreement that applies?
- Researcher competence: Does the team have the subject and methodological expertise needed to judge the output?
If a tool produces useful suggestions but its evidence cannot be traced, its limitations cannot be assessed, or its data handling conflicts with project rules, those are material drawbacks—not details to defer until after the analysis.
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Frequently Asked Questions
What can AI actually do in scientific research?
It can assist with literature searching and review, hypothesis generation, data and image analysis, modeling, molecule or material design, synthetic data, and drafting or editing research documents. Suitability is task-specific, and researchers must judge and verify the output.
Can AI make scientific discoveries on its own?
AI systems may help synthesize literature, generate hypotheses and plans, suggest alternatives, or run models and experiments. Those capabilities do not establish independent, reliable discovery: researchers still need to validate proposals with appropriate evidence, including empirical work where relevant.
Can I trust AI-generated research or citations?
Not without checking them. AI output can include fabricated citations, unsupported claims, bias, or random errors. Verify references and important factual claims against original sources, and do not treat synthetic data as observations.
How should researchers use AI responsibly?
Define the task, confirm you have the expertise to assess results, check data-use and confidentiality rules before sharing material, verify outputs, document relevant tool use and limitations, and follow applicable journal, funder, and institutional disclosure requirements. Researchers remain accountable for the work.
The Bottom Line
AI can broaden research assistance, from literature work and analysis to modeling and writing, but it does not replace scientific judgment or accountability. Treat outputs as proposals to evaluate: verify claims and sources, keep synthetic data distinct from observations, protect restricted information, document relevant use, and follow the rules that apply to your role and project.
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