Doing Research Without Compromising Ethics
Eliminating every possible research risk may seem ethically responsible, but excessive avoidance can prevent studies that address serious social problems. Learn how researchers can distinguish unacceptable risks from manageable ones and justify ethically proportionate research.
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Doing Research Without Compromising Ethics
The method offering the strongest evidence may involve procedures that cannot be ethically justified. Learn how to distinguish methodological advantages from ethical acceptability, examine alternative designs, and decide when a research question must change or a project should not proceed.
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Research Concepts, Terminology & Commonly Confused Ideas
Research, inquiry, and investigation all involve seeking answers, but the terms emphasize different aspects of that process. Understanding their overlap helps clarify when asking questions or investigating a problem becomes formal research.
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Research Concepts, Terminology & Commonly Confused Ideas
Basic research seeks understanding without a specific practical application as its primary aim, while applied research is directed toward a specific practical objective. The distinction is useful, but many studies sit closer to a continuum than a clean either-or divide.
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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 Software, Digital Tools & Computational Workflows
You can conduct academic research without a reference manager, but manual reference management becomes increasingly difficult as your sources, projects, and citations grow. Here is how to decide when using one is worth the effort.
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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 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.
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Artificial Intelligence in Research
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.
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Artificial Intelligence in Research
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.
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Artificial Intelligence in Research
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.
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Artificial Intelligence in Research
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.
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Artificial Intelligence in Research
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.
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Artificial Intelligence in Research
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.
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Artificial Intelligence in Research
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.
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Artificial Intelligence in Research
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.
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Artificial Intelligence in Research
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.
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Artificial Intelligence in Research
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.
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Artificial Intelligence in Research
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.
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Artificial Intelligence in Research
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.
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Artificial Intelligence in Research
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.
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