Artificial Intelligence in Research
Accuracy means different things across generative AI research tasks. Learn when conventional accuracy calculations are appropriate, why open-ended responses require different scoring methods, and how to distinguish factual correctness from completeness, consistency, and usefulness.
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Artificial Intelligence in Research
AI-generated qualitative codes and classification labels require more than a simple accuracy calculation. Learn how to evaluate coding validity, agreement, category-specific errors, interpretive consistency, and the limitations of comparing AI judgments with human reference labels.
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Artificial Intelligence in Research
Researchers can evaluate the quality of AI reasoning without observing a model's internal computations, but they must distinguish valid answers and explanations from claims about how the model actually arrived at them. Learn how to design reasoning tasks, evaluate observable justifications, and avoid overinterpreting generated chains of thought.
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Artificial Intelligence in Research
High average AI accuracy can conceal poor performance for particular populations, languages, or task categories. Learn when subgroup analysis is necessary, how to measure performance differences, and how to interpret disparities without confusing statistical variation with evidence of discrimination.
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Artificial Intelligence in Research
A fair comparison between AI and human researchers requires more than asking both to complete the same questions. Learn how to select appropriate participants, control information access, define comparable tasks, reduce evaluator bias, and analyze performance without favoring either condition.
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Artificial Intelligence in Research
The choice of human comparator can fundamentally change the conclusion of an AI performance study. Learn how researchers should select relevant participants, define expertise, determine sample sizes, and establish human performance baselines that support meaningful comparisons rather than exaggerated superiority claims.
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Artificial Intelligence in Research
Knowing whether an answer came from AI or a human can influence how evaluators judge its quality. Learn how researchers can implement blinded scoring, standardize response presentation, conceal source-identifying information, test whether blinding worked, and recognize when complete blinding is impractical.
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Artificial Intelligence in Research
Evaluators may judge identical responses differently depending on whether they believe the author is human or AI. Learn how expectations, source labels, response presentation, and perceived expertise can influence human-AI comparisons, and how researchers can measure and reduce these biases.
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Artificial Intelligence in Research
When generative AI contributes to data collection, analysis, classification, or other research procedures, prompt design can influence the resulting evidence. Learn why prompting may constitute part of the research method, how prompts function as experimental conditions, and what researchers should document to support validity, transparency, and reproducibility.
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Artificial Intelligence in Research
A research prompt is more than a question submitted to an AI system. Learn how prompts function within research protocols, influence data collection and analysis, and require documentation of instructions, examples, context, and execution conditions to support scientific transparency.
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Artificial Intelligence in Research
Prespecifying AI prompts can reduce selective optimization and strengthen confirmatory research, but it does not prohibit pilot testing or exploratory prompt development. Learn when prompts should be finalized, how to document amendments, and why independent evaluation data matter more than simply declaring a prompt fixed.
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Artificial Intelligence in Research
Pilot testing can reveal weaknesses in AI research prompts before they affect formal study results. Learn how to design a prompt pilot, assess instruction clarity, identify inconsistent outputs, separate development from evaluation data, and document revisions without introducing prompt overfitting.
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Artificial Intelligence in Research
Prompt cherry-picking occurs when researchers selectively report prompts or AI outputs that support a preferred conclusion while concealing relevant alternatives. Learn how this practice differs from legitimate prompt optimization, why it threatens evaluation validity, and how to document prompt selection without compromising scientific transparency.
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Artificial Intelligence in Research
Failed AI prompts can reveal methodological limitations, unstable model behavior, and selection decisions that successful outputs alone cannot explain. Learn which unsuccessful prompts researchers should report, how to distinguish development failures from evaluation failures, and how to document negative results without overwhelming the research paper.
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Artificial Intelligence in Research
AI prompt variants can influence research findings even when only one prompt appears in the final methodology. Learn which variants should be archived, how to preserve prompt histories and execution records, and how researchers can balance reproducibility, transparency, confidentiality, and practical documentation requirements.
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Artificial Intelligence in Research
Generative AI responses may depend on earlier conversation turns, instructions, examples, and retained context. Learn how conversation history can influence research outcomes, when fresh conversations are appropriate, and how researchers can document multi-turn AI interactions for valid and reproducible evaluation.
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Artificial Intelligence in Research
Starting each AI research test in a fresh conversation can reduce unwanted context carryover, but it is not always the appropriate experimental design. Learn when conversation isolation is necessary, how persistent memory and system instructions complicate testing, and how researchers can distinguish independent trials from realistic multi-turn interactions.
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Artificial Intelligence in Research
Researchers can legitimately improve AI prompts, but repeatedly modifying instructions until a model produces a preferred finding can undermine scientific validity. Learn how to distinguish methodological optimization from outcome-driven manipulation, separate development from evaluation, and report prompt selection transparently.
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Artificial Intelligence in Research
Repeatedly refining AI prompts against the same evaluation examples can produce impressive benchmark scores without improving generalizable performance. Learn when prompt optimization becomes analytical overfitting, how adaptive testing introduces selection bias, and how researchers can protect the independence of their final evaluations.
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Artificial Intelligence in Research
AI agents can autonomously execute selected research tasks, from retrieving literature to running analytical scripts. However, successful automation does not establish scientific validity or remove researcher accountability. Learn which tasks are suitable for delegation, how to evaluate agent reliability, and when human supervision remains necessary.
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Artificial Intelligence in Research
AI agents can connect literature retrieval, data analysis, and scientific writing into multi-step workflows, but completing every stage does not guarantee valid research. Learn how errors propagate across autonomous research pipelines, why independent verification matters, and when human intervention remains essential.
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Artificial Intelligence in Research
AI agents can clean, standardize, and transform research datasets, but unrestricted modification can compromise the scientific record. Learn which changes may be appropriate, why original data should be preserved, and how permissions, audit trails, validation, and human approval can distinguish legitimate processing from unacceptable manipulation.
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Artificial Intelligence in Research
AI agents can generate and execute statistical code, but a script that runs successfully is not necessarily scientifically correct. Learn when autonomous execution is appropriate, how to validate code and statistical assumptions, and why sandboxing, audit trails, and human approval remain important for research integrity.
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Artificial Intelligence in Research
AI research agents can upload files, submit forms, call APIs, and transmit information to external services, but these actions may disclose confidential data or create irreversible commitments. Learn how researchers should classify external submissions, restrict permissions, verify destinations, and establish human approval before consequential actions.
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Artificial Intelligence in Research
Autonomous AI agents can perform increasingly complex research tasks, but some activities should not be delegated without meaningful human control. Learn when agentic AI is methodologically, ethically, or institutionally inappropriate, how to identify unacceptable risks, and why technical capability does not establish permission or scientific legitimacy.
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