ArticleBehavior research methods2024
The impact of ChatGPT on human data collection: A case study involving typicality norming data.
Article in Behavior research methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- AI-generated familiarity estimates are a useful new source of information about word knowledge in Simplified Chinese.Behavior research methods · 2026Article
- A large-scale evaluation of commonsense knowledge in humans and large language models.PNAS nexus · 2026Article
- Influence of solution efficiency and valence of instruction on additive and subtractive solution strategies in humans, GPT-4, and GPT-4o.Communications psychology · 2026Article
- Updating the German Psycholinguistic Word Toolbox with AI-Generated Estimates of Concreteness, Valence, Arousal, Age of Acquisition, and Familiarity.Journal of cognition · 2026Article
- Reporting guideline for the use of Generative Artificial intelligence tools in MEdical Research: the GAMER Statement.BMJ evidence-based medicine · 2025Article
- Article
- Accuracy and Repeatability of ChatGPT Based on a Set of Multiple-Choice Questions on Objective Tests of Hearing.Cureus · 2024Article
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2 authors.
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Abstract
Tools like ChatGPT, which allow people to unlock the potential of large language models (LLMs), have taken the world by storm. ChatGPT's ability to produce written output of remarkable quality has inspired, or forced, academics to consider its consequences for both research and education. In particular, the question of what constitutes authorship, and how to evaluate (scientific) contributions has received a lot of attention. However, its impact on (online) human data collection has mostly flown under the radar. The current paper examines how ChatGPT can be (mis)used in the context of generating norming data. We found that ChatGPT is able to produce sensible output, resembling that of human participants, for a typicality rating task. Moreover, the test-retest reliability of ChatGPT's ratings was similar to that of human participants tested 1 day apart. We discuss the relevance of these findings in the context of (online) human data collection, focusing both on opportunities (e.g., (risk-)free pilot data) and challenges (e.g., data fabrication).
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