Search across guide titles, topics, keywords, and content throughout the Researcher's Field Guide.
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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 research ethics committee may restrict or require changes to AI use when it affects participants, consent, privacy, confidentiality, risk, or an approved research protocol.
Read Guide →Research teams can benefit from project-level AI rules that clarify what tools and uses are acceptable, how sensitive information is handled, and who remains responsible for AI-assisted work.
Read Guide →Agreeing on AI use early can prevent inconsistent practices once research is underway. A useful plan defines permitted tools and tasks, data restrictions, verification, documentation, responsibility, and how later changes will be handled.
Read Guide →Unpublished research is not automatically confidential, but entering it into a public AI tool can create disclosure, privacy, ownership, and policy concerns. Check what the material contains and whether you are authorized to submit it before doing so.
Read Guide →An unpublished manuscript is not automatically prohibited from AI use, but uploading the complete document can expose far more information than a writing task requires. Check confidentiality, co-author rights, research data, publisher rules, and the AI service before submitting it.
Read Guide →A confidential grant application entrusted to you for review is not ordinary research material. Some major funders expressly prohibit reviewers from uploading applications, proposal content, critiques, or related review information to unapproved generative AI systems.
Read Guide →De-identification can reduce privacy risk, but ethical research involves more than preventing someone from attaching a name to a record. AI use may still raise questions about consent, secondary use, group harms, fairness, confidentiality, and whether participants could reasonably have expected their data to be used this way.
Read Guide →Participant recordings can reveal identity through voices, faces, speech, surroundings, and metadata. Before using AI for transcription or analysis, researchers should determine whether the recording itself may be disclosed to the service, not merely whether the resulting transcript will be de-identified.
Read Guide →Informed consent does not necessarily need to name every software tool used in research, but AI processing may warrant disclosure when it materially changes how participant data are handled, who receives them, what is inferred, or the risks participants face.
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 →Do not search only the exact wording of an AI-generated research question. Break it into concepts, relationships, populations, and contexts, then search the literature broadly enough to discover substantively equivalent questions.
Read Guide →Generative AI can help researchers identify feasibility constraints and stress-test a proposed study. Its assessment is only as reliable as the project-specific information it receives about methods, access, resources, ethics, expertise, and time.
Read Guide →Generative AI can help researchers generate competing explanations for the same observation, potentially exposing mechanisms and interpretations they had overlooked. Those explanations remain hypotheses until they are evaluated against theory and evidence.
Read Guide →Generative AI can help you draft, compare, and revise possible research questions, but a polished question is not necessarily a defensible one. Learn how to use AI without outsourcing the substantive decisions that define your study.
Read Guide →An overly broad research question can make a study difficult to design and complete. Learn how generative AI can help identify unnecessary breadth, compare narrower formulations, and preserve the original research purpose.
Read Guide →A research topic identifies what interests you, but a researchable question establishes what you intend to investigate. Learn how generative AI can help bridge that gap without inventing a research problem or assuming an unverified knowledge gap.
Read Guide →Generative AI can help translate a research question into clear general and specific objectives, but it may also introduce unsupported variables, methods, or outcomes. Learn how to evaluate AI-generated objectives without allowing them to redefine your study.
Read Guide →Research objectives can appear aligned with a research question while introducing different variables, populations, or claims. Learn how generative AI can help identify conceptual mismatches, missing coverage, and objectives that exceed what the study intends to investigate.
Read Guide →Generative AI can propose research hypotheses from questions, theories, and variables, but plausibility is not scientific justification. Learn how to assess AI-generated hypotheses for theoretical support, testability, and methodological consistency.
Read Guide →A research hypothesis expresses a substantive expectation, while a statistical hypothesis specifies a claim about a population or probability model. Learn how generative AI can help distinguish and connect them without confusing scientific meaning with statistical testing.
Read Guide →A research hypothesis can sound plausible while remaining impossible to evaluate with evidence. Learn how generative AI can help identify unclear constructs, missing measurements, unsupported assumptions, and methodological barriers to testability.
Read Guide →Generative AI can suggest competing hypotheses and alternative explanations that researchers may overlook. Learn how to distinguish genuine theoretical alternatives from statistical hypotheses, evaluate their assumptions, and avoid selecting predictions after seeing the results.
Read Guide →Generative AI can help identify variables and constructs within research questions, but assigning their roles requires more than recognizing familiar terms. Learn how to distinguish predictors, outcomes, and contextual factors without accepting unsupported causal assumptions.
Read Guide →Generative AI can suggest variables that may confound an exposure-outcome relationship, but correlation alone does not establish confounding. Learn how to evaluate AI-generated suggestions using causal reasoning, temporal ordering, and defensible adjustment strategies.
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