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Generative AI can improve the appearance of weak research without improving the research itself. Polished prose, methodological vocabulary, detailed explanations, and professional presentation should not be mistaken for valid methods or stronger evidence.
Read Guide →Generative AI can produce remarkably accurate answers, but producing a correct answer is not the same as knowing it in the human sense. Researchers need to distinguish what a model can generate from what has actually been verified.
Read Guide →Generative AI can often recognize obvious differences between established findings and speculation, but it does not reliably preserve the evidential status of every scientific claim. Researchers should verify not only whether a claim appears in the literature, but how strongly the literature actually supports it.
Read Guide →A model cannot have learned a paper published after the data used for that training run, but that does not mean an AI system can never use newer research. Search, retrieval, uploaded papers, tools, and later model updates can provide post-cutoff information at the time you ask.
Read Guide →No general-purpose generative AI should be assumed to have access to every published research paper. Scholarly literature is distributed across publishers, databases, repositories, and access systems, while model training and retrieval tools provide only particular forms of coverage.
Read Guide →Generative AI may produce an accurate-looking description of a paper it has not accessed, but that description cannot safely be assumed to represent the paper itself. The model may be combining metadata, abstracts, related literature, learned associations, and plausible inference.
Read Guide →Generative AI can often distinguish obvious primary studies from review articles, but ambiguous designs, incomplete abstracts, and misleading terminology can cause misclassification. Researchers should verify publication type from what the paper actually did.
Read Guide →Generative AI can express uncertainty and abstain from answering, but it does not reliably recognize every situation in which it lacks sufficient knowledge. Researchers should treat “I know” and “I don't know” as outputs to evaluate, not infallible reports of the model's internal knowledge.
Read Guide →Academic-sounding AI writing can be clear, sophisticated, and completely wrong. Fluency tells you that the model can produce the conventions of scholarly prose; accuracy still depends on whether its factual claims, citations, and interpretations are supported by evidence.
Read Guide →An AI hallucination occurs when a generative AI system produces information that is false, unsupported, inconsistent, or unfaithful to the available evidence while presenting it as a plausible response. For researchers, the practical consequence is simple: plausible AI-generated content should not be treated as verified evidence.
Read Guide →AI hallucinations can be difficult to detect because language models are capable of producing fluent, coherent, contextually appropriate language even when individual claims are wrong. For researchers, confidence, detail, and academic tone should never substitute for evidence.
Read Guide →Generative AI can produce scientific statements that are false, unsupported, distorted, or more certain than the available evidence permits. Researchers should verify consequential claims against the underlying scientific literature rather than relying on the plausibility of AI-generated explanations.
Read Guide →Generative AI can invent numerical results, extract the wrong values, or misinterpret statistics from real studies. Researchers should trace every consequential AI-generated statistic to the original analysis, dataset, table, figure, or publication.
Read Guide →Generative AI can produce references to publications that do not exist and incorrect bibliographic details for publications that do. Researchers should verify every AI-generated reference against authoritative scholarly records before citing it.
Read Guide →Generative AI can invent journal names, article titles, author combinations, and other bibliographic details. More deceptively, it can combine real scholarly entities into a publication that never existed.
Read Guide →Generative AI can combine genuine authors, titles, findings, journals, or other details from different sources into a convincing publication that never existed. Researchers should verify whether all bibliographic and substantive details actually converge on one real paper.
Read Guide →Generative AI can produce quotation marks around wording that never appeared in the cited research paper, alter genuine wording, or attach a quotation to the wrong source. Researchers should verify every direct quotation against the original document.
Read Guide →Giving generative AI the actual research paper can improve grounding, but it does not guarantee that every generated statement will faithfully represent the document. Researchers should still verify consequential summaries, quotations, methods, results, and interpretations against the source.
Read Guide →Researchers should distinguish useful AI-generated information from verified research evidence. Consequential factual claims should be checked against appropriate primary or authoritative sources before they influence a manuscript, analysis, or research decision.
Read Guide →Not every sentence in an AI-generated response carries the same verification burden. Researchers should prioritize claims, citations, calculations, methods, interpretations, and other content whose accuracy affects the research.
Read Guide →Researchers remain responsible for factual claims they use, but that does not mean every AI-generated sentence requires identical verification effort. The sensible approach is risk-based: verify consequential claims rigorously and do not publish unsupported claims merely because AI supplied them.
Read Guide →An AI-generated research summary can be fluent and still distort the original paper. Verify it against the source, paying particular attention to study purpose, methods, participants, findings, limitations, and the strength of the authors' conclusions.
Read Guide →General-purpose and research-specific AI tools solve different problems. The better choice depends on the research task, the evidence you need, and how easily you can verify the output.
Read Guide →Paying for an AI tool can change access to models, features, usage limits, and data controls, but price does not establish research reliability. Compare what the specific plan changes for the task you need to perform.
Read Guide →A more capable AI model may perform better on difficult research tasks, but capability and reliability are not the same thing. Researchers still need task-specific testing, source verification, and appropriate safeguards.
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