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Machine learning is a broad set of methods through which systems learn patterns from data, while generative AI focuses on producing new synthetic content. The categories overlap: modern generative AI is itself built using machine-learning techniques.
Read Guide →AI-assisted research uses artificial intelligence to support one or more research tasks while researchers retain responsibility for the study and its scholarly decisions. The important question is not merely whether AI was used, but what it assisted with and how its contribution was evaluated.
Read Guide →AI-assisted research uses AI to support a human-led research process, while AI-generated refers to material or outputs produced by the AI itself. A project can involve both, so researchers should describe what AI actually contributed rather than relying on either label alone.
Read Guide →Generative AI can assist with tasks across much of the research process, including exploration, literature work, coding, data preparation, analysis support, writing, and communication. Its usefulness depends on the task, the system, the evidence available for verification, and the consequences of getting the output wrong.
Read Guide →AI can assist with many demanding research tasks, but some responsibilities should remain under meaningful human control. Researchers should not completely delegate accountable scholarly judgment, ethical responsibility, authoritative verification, or final interpretation to AI.
Read Guide →Generative AI can explain, summarize, compare, and analyze research remarkably well, but successful performance does not establish that it understands research in the same way a human researcher does. The distinction matters when judgment, context, evidence, and scientific meaning become important.
Read Guide →Generative AI can perform useful forms of scientific reasoning, but its reliability varies by model, task, evidence, and context. Researchers should evaluate the reasoning chain and underlying evidence rather than assume that a persuasive conclusion is scientifically sound.
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 →Generative AI can express an incorrect answer with the same polished language it uses for a correct one. Verbal confidence, internal model confidence, and factual correctness are related but distinct, which is why researchers should never use confident wording as a proxy for verification.
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 →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 →Web search, retrieval, and retrieval-augmented generation can reduce hallucinations by giving AI access to external evidence, but they do not eliminate them. Errors can arise during retrieval, source selection, interpretation, and generation.
Read Guide →AI models can sometimes detect and correct their own mistakes, particularly under structured verification procedures, but self-verification remains unreliable as a general guarantee of correctness. For consequential research work, use self-review to identify possible problems and external evidence to resolve them.
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 →The safest default is simple: do not put confidential, sensitive, restricted, or third-party unpublished research into an unapproved AI tool. Use it only after you can establish authorization, appropriate safeguards, and a genuine need to disclose the information.
Read Guide →Survey responses may look less sensitive than interviews, but structured variables, free-text answers, metadata, and combinations of demographics can still identify participants. AI use should depend on the actual data and the conditions governing their processing.
Read Guide →Generative AI can be useful for producing, expanding, and recombining possible research ideas. Its strongest role is as an ideation partner, not as an authority on whether an idea is novel, important, feasible, or supported by the literature.
Read Guide →Generative AI can make research-topic brainstorming faster and more varied, especially when you give it meaningful constraints. The strongest approach is not to ask for a long list of titles, but to use AI to explore different directions before selecting and validating a topic yourself.
Read Guide →Generative AI can formulate research questions that appear novel and may help researchers consider directions they had not thought of. Whether those questions are genuinely novel can only be determined by comparing them with existing scholarship.
Read Guide →Generative AI can help researchers expose assumptions hidden in a research idea, from presumed causal relationships to definitions, populations, methods, and claims of significance. The researcher must still determine which critiques are substantively valid.
Read Guide →Generative AI can be used as an adversarial research-planning partner that deliberately searches for weaknesses in a proposed study. Its criticisms can sharpen reasoning, but they must themselves be checked rather than treated as expert verdicts.
Read Guide →Generative AI can make a familiar research idea sound unusually innovative by changing its framing, terminology, and claims of contribution. Rhetorical novelty should never be confused with novelty demonstrated against the literature.
Read Guide →Generative AI can make fashionable research directions unusually easy to generate and justify. Researchers should distinguish genuine scientific opportunity from questions that are merely timely, familiar, and easy for AI to formulate.
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