Can human intelligence safeguard against artificial intelligence? Exploring individual differences in the discernment of human from AI texts
Open accessPreprint — published as 10.1038/s41598-024-76218-y · Paper page
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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- Authors
- Chein, J., Martinez, S., & Barone, A.
- Affiliations
- Temple University (US)
- Corresponding author
- Chein, Jason
- ORCID
- Chein, Jason
- Venue
- Scientific Reports
- Published
- 2024-04-29
- Volume
- 14
- Issue
- 1
- Pages
- 25989-25989
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC11522284
- OpenAlex
- W4403870620
- Cited by
- 38
- References
- 59
- Citations by year
- 2026: 24 · 2025: 12 · 2024: 2
- FWCI
- 5.60
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Scientific Reports (publishedVersion, cc-by-nc-nd)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Temple University
- National Institute of Child Health and Human Development (R01 HD098097-S)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD098097)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Authorship Attribution and Profiling
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- AI-generated text detection, individual differences, fluid intelligence, executive function, empathy, digital habits, smartphone use, social media use, linguistic composition, human-AI interaction
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