Research Concepts, Terminology & Commonly Confused Ideas
Methods are the procedures you use to conduct research; methodology concerns the reasoning, principles, and assumptions that inform how those procedures are selected and used. The distinction matters because rigorous research requires more than reporting what you did.
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Research Concepts, Terminology & Commonly Confused Ideas
A research gap describes what existing knowledge does not adequately answer, while a research problem defines the specific issue your study will investigate. Learn how the two concepts connect without treating them as interchangeable.
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Artificial Intelligence in Research
AI in research can mean using artificial intelligence at many points in the research process, from finding patterns in data to generating or transforming content. The important question is not simply whether AI was used, but what it did and how its outputs influenced the research.
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Artificial Intelligence in Research
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.
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Artificial Intelligence in Research
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.
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Artificial Intelligence in Research
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.
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Artificial Intelligence in Research
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.
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Artificial Intelligence in Research
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.
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Artificial Intelligence in Research
Generative AI requires additional safeguards because it can generate plausible falsehoods, obscure information provenance, handle sensitive inputs, produce variable outputs, and influence scholarly judgment. The right safeguards should follow the particular task and risk rather than treating every AI use as equally dangerous.
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Artificial Intelligence in Research
Researchers do not need to become AI engineers before using generative AI, but they should understand enough to judge its outputs, protect research information, document consequential use, and remain responsible for the resulting scholarship.
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Artificial Intelligence in Research
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.
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Artificial Intelligence in Research
Generative AI can identify many features associated with stronger or weaker evidence, but scientific strength cannot be determined from study design labels alone. Reliable appraisal requires attention to risk of bias, precision, directness, consistency, and the question being asked.
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Artificial Intelligence in Research
A plausible AI answer is something the model can generate convincingly. A verified research answer is one whose important claims have been checked against appropriate evidence, sources, and context.
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Artificial Intelligence in Research
Generative AI can invent methodological details or incorrectly describe methods used in real studies. Researchers should verify designs, samples, instruments, procedures, and analyses directly against the original study or authoritative methodological source.
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Artificial Intelligence in Research
A research paper can be completely real while an AI-generated description of it is wrong. Generative AI may alter methods, results, scope, limitations, or conclusions, so researchers should verify substantive claims against the original paper.
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Artificial Intelligence in Research
AI hallucinations can be reduced, detected, and avoided in many situations, but that is different from guaranteeing that a general-purpose generative system will never produce an unsupported claim. Reliable systems increasingly combine generation with grounding, uncertainty, abstention, and verification.
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Artificial Intelligence in Research
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.
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Artificial Intelligence in Research
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.
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Artificial Intelligence in Research
A plausible scientific claim from AI is not evidence. Verify it by defining exactly what the claim asserts, locating appropriate scientific sources, and checking whether the evidence actually supports that specific conclusion.
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Artificial Intelligence in Research
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.
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Artificial Intelligence in Research
An AI explanation of a statistical method can sound technically convincing while misstating an assumption, interpretation, or condition for use. Verify each important methodological claim against authoritative statistical sources and the requirements of your actual analysis.
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Artificial Intelligence in Research
AI can produce correct calculations and convincing wrong ones. Researchers should verify consequential numerical results independently by checking the inputs, formula, operations, units, assumptions, and final interpretation.
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Artificial Intelligence in Research
An AI system can report research results accurately while interpreting them incorrectly. Verify its interpretation against the underlying data, statistical analysis, study design, uncertainty, and limits of inference before using it in a manuscript or conclusion.
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Artificial Intelligence in Research
A second AI model can catch errors the first model missed, but model agreement is not independent evidence that an answer is correct. Cross-model review is best used as an additional diagnostic layer before verification against authoritative sources, data, calculations, tests, or other external evidence.
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Artificial Intelligence in Research
Independent verification means checking AI-assisted research against evidence or a validation process that does not simply depend on the generated answer itself. The appropriate method depends on whether you are verifying a claim, citation, calculation, code, result, or interpretation.
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