Evidence map›Paper›PMID 42838736›Full record

ArticleApplied psychology. Health and well-being2026

Perceptions of AI-generated informational texts and effects of the source: An online experiment.

Laura M König, Maren C Podszun, Wolfgang Gaissmaier, Helge Giese

Abstract read
In one paragraph

Article in Applied psychology. Health and well-being, 2026. 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

4 authors.

Laura M KönigFaculty of Psychology, University of Vienna, Vienna, Austria.ORCID https://orcid.org/0000-0003-3655-8842
Maren C PodszunInstitute of Nutritional Sciences, University of Hohenheim, Stuttgart, Germany.ORCID https://orcid.org/0000-0001-8222-6131
Wolfgang GaissmaierDepartment of Psychology, University of Konstanz, Konstanz, Germany.ORCID https://orcid.org/0000-0001-6273-178X
Helge GieseDepartment of Psychology, University of Konstanz, Konstanz, Germany.ORCID https://orcid.org/0000-0001-7609-0215

Funding

German Research Foundation (DFG) 441541975
6 · The paper itself

Abstract

Artificial intelligence (AI) is becoming increasingly common for health information seeking, yet many people are still skeptical of its performance. This between-subjects online experiment tested whether informing readers that a text was produced by AI influenced their ratings in 774 participants. They were randomly assigned to read one of six brief texts and rated trustworthiness, interest, and perceived understanding, as well as intention to act upon the received information and willingness to share the information with others. Source information did not influence ratings, but only 41.5% of participants in the AI source condition noticed the source. Source awareness was associated with higher education and greater nutrition knowledge. Nutrition information produced by AI chatbots is generally seen as trustworthy, although source information is often ignored. Our research highlights critical implications for public health messaging in the digital age as consumers may not differentiate between information generated by AI versus other sources, although future research is required to assess source perceptions in AI-generated texts in more detail.

Indexed as

Artificial IntelligenceHealth Knowledge, Attitudes, PracticeAdolescentAdultFemaleGenerative Artificial IntelligenceHumansInternetMaleMiddle AgedTrustYoung AdultChatGPTgenerative AIlarge language modelsnutrition communicationpublic health nutrition

Identifiers

PMID42838736
PMCPMC13641973

What OpenQuestion holds

Textmetadata
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.