ArticleScientific reports2024
Human intelligence can safeguard against artificial intelligence: individual differences in the discernment of human from AI texts.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Children's susceptibility to content generated by artificial intelligence.Technology in society · 2026Article
- Can artificial intelligence help with the development of generic clinical skills when breaking bad news? A quantitative evaluation.Clinical medicine (London, England) · 2026Article
- Detecting "large language models fingerprint" for Japanese texts generated by six LLMs.Frontiers in artificial intelligence · 2026Article
- Learning to detect AI texts and learning the limits.PloS one · 2025Article
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Abstract
Artificial intelligence (AI) models can produce output that closely mimics human-generated content. We examined individual differences in the human ability to differentiate human- from AI-generated texts, exploring relationships with fluid intelligence, executive functioning, empathy, and digital habits. Overall, participants exhibited better than chance text discrimination, with substantial variation across individuals. Fluid intelligence strongly predicted differences in the ability to distinguish human from AI, but executive functioning and empathy did not. Meanwhile, heavier smartphone and social media use predicted misattribution of AI content (mistaking it for human). Determinations about the origin of encountered content also affected sharing preferences, with those who were better able to distinguish human from AI indicating a lower likelihood of sharing AI content online. Word-level differences in linguistic composition of the texts did not meaningfully influence participants' judgements. These findings inform our understanding of how individual difference factors may shape the course of human interactions with AI-generated information.
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