Evidence map›Paper›PMID 42004821›Full record

ArticleTechnology in society2026

Children's susceptibility to content generated by artificial intelligence.

Allison Langer, Steven Martinez, Peter J Marshall, Jason Chein

Abstract read
In one paragraph

Article in Technology in society, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Allison LangerDepartment of Psychology and Neuroscience, Temple University, PA, USA.ORCID 0000-0002-0003-5700
Steven MartinezDepartment of Psychology and Neuroscience, Temple University, PA, USA.ORCID 0000-0003-2769-2804
Peter J MarshallDepartment of Psychology and Neuroscience, Temple University, PA, USA.
Jason CheinDepartment of Psychology and Neuroscience, Temple University, PA, USA.ORCID 0000-0002-8430-7899

Funding

Origins And Outcomes Of Smartphone And Social Media Habits Across Development SupplementR01HD098097 · NICHD · TEMPLE UNIV OF THE COMMONWEALTH · PI CHEIN, JASON M · 2021 to 2025
$2.1M
NICHD NIH HHS R01 HD098097
6 · The paper itself

Abstract

Advancements in artificial intelligence (AI) tools can make it difficult to discern what is "real". Though children are interacting with AI-generated materials through entertainment and learning applications, no published work directly considers children's ability to distinguish human-created from AI-generated information. In the current study, 37 children (6-10 years old) heard a narrative about a "SmartBot" (representing AI) that could write text, invent photos, and create art. They were then asked to discern which in a series of stimuli including short texts and images of objects, art, and faces, had come from the SmartBot vs. a human teacher and answer questions regarding their beliefs about AI. Children also completed measures of abstract reasoning and receptive vocabulary, and parents completed a home technology use survey. A separate sample of 49 adults (18-34 years) served as a comparison group for accuracy scores on the set of stimuli. On average, children performed at or below chance in discriminating human-created from AI-generated content across modalities, and their overall accuracy was significantly below that of adults. Children's individual discernment varied and was significantly and negatively associated with parent reports of children's at-home technology use. Children had varied beliefs about AI agency and experience, but broadly understood that an AI agent (the SmartBot) is not alive (95%). Our findings suggest that spending more time on digital devices is associated with susceptibility to artificial content and risk for misattributing AI content as human, highlighting the importance of early AI literacy to support children's engagement with AI technologies.

Indexed as

ChildrenDevelopmental psychologyGenerative AIHuman-computer interaction

Identifiers

PMID42004821
PMCPMC13089802

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.