Artificial Intelligence in Research
AI simulation can be a legitimate methodological tool when researchers use it to examine computational behavior, test research procedures, or investigate explicitly defined hypothetical conditions. However, simulation cannot automatically replace empirical evidence about real people or events. Learn how to distinguish scientifically defensible simulations from inappropriate substitutes for data collection.
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Artificial Intelligence in Research
When artificial intelligence itself becomes the object of a research study, researchers must treat model behavior as the phenomenon being investigated. This changes how observations are collected, experimental conditions are controlled, responses are evaluated, and findings are generalized. Learn why model versions, prompts, conversation history, repeated testing, and system updates become important methodological considerations.
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Artificial Intelligence in Research
Commercial AI models may change through updates to their underlying models, system instructions, safety mechanisms, or supporting infrastructure, sometimes without detailed public notice. These changes complicate reproducibility and longitudinal research. Learn how researchers can document testing conditions, detect behavioral changes, and produce scientifically defensible findings even when the AI system cannot be frozen or fully inspected.
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Artificial Intelligence in Research
Recording the exact AI model version helps researchers establish which system produced the observations in a study. However, commercial AI services do not always expose immutable model identifiers, and a model name may refer to a changing deployment. Learn what version information should be preserved, why model identity matters for reproducibility, and how to report uncertainty when the exact model state is unavailable.
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Artificial Intelligence in Research
Recording when an AI model was tested helps researchers establish the temporal context of their findings, especially when commercial systems may change without notice. Learn when testing dates are sufficient, when individual response timestamps are necessary, and how time zones, model updates, and longitudinal designs affect the reproducibility and interpretation of AI research.
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Artificial Intelligence in Research
Prompts are often central methodological materials when researchers study generative AI systems. Preserving only successful prompts may conceal selection bias, while incomplete conversation records can make evaluations difficult to reproduce. Learn which prompts should be archived, how to document exploratory and formal testing, and when confidentiality or practical constraints justify restricted preservation.
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Artificial Intelligence in Research
System instructions can influence an AI model's responses even when researchers submit identical prompts. Learn which instructions to document, how to preserve them, and how to report instructions that are unavailable.
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Artificial Intelligence in Research
Testing an AI prompt once may conceal important variation, but no universal repetition count applies to every study. Learn how to choose repeated trials based on research objectives, response variability, statistical precision, and available resources.
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Artificial Intelligence in Research
Generative AI may produce different responses to identical prompts, complicating reproducibility and performance evaluation. Learn how researchers can measure this variability, control experimental conditions, analyze repeated outputs, and report findings without overstating their stability.
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Artificial Intelligence in Research
A fair AI model comparison requires more than submitting the same questions to different systems. Learn how to establish comparable testing conditions, select appropriate performance measures, account for response variability, and avoid misleading conclusions about which model performs better.
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Artificial Intelligence in Research
Identical prompts can strengthen comparisons between AI models, but they do not automatically guarantee fairness. Learn when exact prompt matching is appropriate, when model-specific adaptations are defensible, and how to preserve methodological comparability.
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Artificial Intelligence in Research
Small changes in prompt wording can influence AI responses and potentially alter a study's conclusions. Learn how researchers can control prompt selection, test reasonable variations, and distinguish genuine model performance from wording-dependent results.
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Artificial Intelligence in Research
Prompt sensitivity describes how an AI model's responses change when the wording or structure of its instructions changes. Learn how researchers can measure these effects, distinguish meaningful variation from superficial differences, and report prompt robustness without overstating model reliability.
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Artificial Intelligence in Research
An AI model may be updated, replaced, or withdrawn before a study evaluating it reaches publication. Learn why this does not automatically invalidate the research and how to preserve the scientific value of findings about rapidly changing AI systems.
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Artificial Intelligence in Research
Commercial AI systems can change during or after a research study, making exact replication difficult. Learn how to preserve experimental conditions, document model behavior, distinguish reproducibility from response stability, and produce scientifically defensible findings even when the original system becomes unavailable.
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Artificial Intelligence in Research
Evaluating AI performance requires more than calculating an accuracy score or selecting impressive examples. Learn how to define the evaluation objective, establish reference standards, select appropriate metrics, account for variability, and interpret results within the conditions actually tested.
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Artificial Intelligence in Research
An AI performance score is only as defensible as the standard used to judge its outputs. Learn how researchers can establish reliable reference answers, handle expert disagreement, assess ambiguous cases, and avoid treating imperfect benchmarks as unquestionable ground truth.
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Artificial Intelligence in Research
Comparing AI with humans or existing software answers different research questions. Learn when each comparator is appropriate, why both may be necessary, and how to design evaluations that establish whether AI offers a meaningful advantage over available alternatives.
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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
Generative AI responses are not always entirely correct or incorrect. Learn how researchers can score partially correct answers, distinguish minor omissions from consequential errors, develop defensible rubrics, and report performance without allowing partial credit to conceal serious inaccuracies.
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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
Hallucination rates can reveal factual reliability problems that overall accuracy scores may conceal. Learn how to define hallucinations, select an appropriate denominator, verify generated claims, and distinguish unsupported content from other AI errors.
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Artificial Intelligence in Research
An AI model may achieve high accuracy while producing inconsistent answers to identical questions. Learn how researchers can evaluate response stability, distinguish it from scientific reproducibility, measure variation across repeated trials, and determine when consistency matters for practical AI use.
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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
An AI system may achieve high accuracy but require substantial time or expense to produce usable results. Learn how researchers can measure latency, throughput, token consumption, total workflow cost, and cost-effectiveness without confusing technical speed with practical efficiency.
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