Evidence map›Paper›PMID 40608186›Full record

Trial reportCognitive research: principles and implications2025

Specific media literacy tips improve AI-generated visual misinformation discernment.

Sean Guo, Briony Swire-Thompson, Xiaoqing Hu

Abstract readRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in Cognitive research: principles and implications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

3 authors.

Sean GuoDepartment of Psychology, The University of Hong Kong, Pokfulam, Hong Kong SAR, China.
Briony Swire-ThompsonDepartment of Political Science, Northeastern University, Boston, MA, USA.
Xiaoqing HuDepartment of Psychology, The University of Hong Kong, Pokfulam, Hong Kong SAR, China. xiaoqinghu@hku.hk.

Funding

National Natural Science Foundation of China 32171056
6 · The paper itself

Abstract

Images generated using artificial intelligence (AI) have become increasingly realistic, sparking discussions and fears about an impending "infodemic" where we can no longer trust what we see on the internet. In this preregistered study, we examine whether providing specific media literacy tips about how to spot AI-generated images can reduce susceptibility to AI-generated visual misinformation (AIVM). Participants were randomly assigned to one of three conditions, reading specific media literacy tips, general media literacy tips, or no media literacy tips (control). The general tips provided tips on how to spot misinformation, while the specific tips provided more detailed tips for how to detect AIVM. Results showed that specific tips increased headline discernment between true and false information more than general tips. Both media literacy interventions reduced belief in AIVM compared to control, but specific tips reduced belief in AIVM more than general tips. Finally, both specific and general tips also reduced belief in real headlines compared to control, with no difference between them. In an information environment that sees increasing prevalence of AIVM, it may be worth being specific about how to detect misinformation online rather than only providing general information.

Indexed as

Artificial IntelligenceCommunicationLiteracyAdultFemaleHumansMaleYoung AdultAI-generated contentMedia literacyMisinformation discernmentVisual misinformation

Identifiers

PMID40608186
PMCPMC12229391

What OpenQuestion holds

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LicenceCC BY
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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.