Evidence map›Paper›PMID 38785844›Full record

ArticleBehavioral sciences (Basel, Switzerland)2024

Understanding and Perception of Automated Text Generation among the Public: Two Surveys with Representative Samples in Germany.

Angelica Lermann Henestrosa, Joachim Kimmerle

Abstract read
In one paragraph

Article in Behavioral sciences (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

2 authors.

Angelica Lermann HenestrosaKnowledge Construction Lab, Leibniz-Institut für Wissensmedien, 72076 Tübingen, Germany.ORCID 0000-0002-0733-9045
Joachim KimmerleKnowledge Construction Lab, Leibniz-Institut für Wissensmedien, 72076 Tübingen, Germany.ORCID 0000-0002-6345-9498

Funding

Leibniz-Institut für Wissensmedien xxxxx
6 · The paper itself

Abstract

Automated text generation (ATG) technology has evolved rapidly in the last several years, enabling the spread of content produced by artificial intelligence (AI). In addition, with the release of ChatGPT, virtually everyone can now create naturally sounding text on any topic. To optimize future use and understand how humans interact with these technologies, it is essential to capture people's attitudes and beliefs. However, research on ATG perception is lacking. Based on two representative surveys (March 2022:

Indexed as

automated journalismautomated text generationChatGPT impactpublic attitudes toward AI

Identifiers

PMID38785844
PMCPMC11118015

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.