Evidence map›Paper›PMID 40653560›Full record

ArticleScientific reports2025

Lay beliefs about AI assessment of interpersonal skills in personnel selection.

Ilyung Cheong, Young Eun Huh, Stefano Puntoni

Abstract read
In one paragraph

Article in Scientific reports, 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.

Ilyung CheongSchool of Business and Technology Management, Korea Advanced Institute of Science and Technology, 291 Daehak-ro Yuseong-gu, Daejeon, South Korea.
Young Eun HuhSchool of Business and Technology Management, Korea Advanced Institute of Science and Technology, 291 Daehak-ro Yuseong-gu, Daejeon, South Korea. younghuh@kaist.ac.kr.
Stefano PuntoniWharton School, University of Pennsylvania, 3733 Spruce St, Philadelphia, PA, USA.

Funding

National Research Foundation of Korea (NRF) grant MSIT; No. 2022R1F1A1063178
6 · The paper itself

Abstract

Artificial intelligence (AI) is revolutionizing talent acquisition, with organizations increasingly replacing humans with AI technologies in personnel selection processes. Field studies of nurses provide preliminary evidence that AI can outperform humans in predicting interpersonal skills. However, a series of surveys and experiments document widespread lay beliefs in the opposite direction: individuals perceive AI as being less capable than humans in assessing interpersonal skills. This effect was not observed for analytical skills, suggesting that the results stem from lay beliefs about the suitability of AI in interpersonal contexts and not from a generalized aversion to algorithms. These lay beliefs bias managers' perceptions of employees: managers were less likely to assign tasks requiring interpersonal skills to AI-selected (vs. human-selected) employees. Additionally, applicants reported reducing their emphasis on interpersonal (vs. analytical) skills during AI (vs. human) selection processes. Informing participants about advanced AI selection technologies reduced negative lay beliefs about AI performance in the interpersonal domains.

Indexed as

Artificial IntelligenceInterpersonal RelationsPersonnel SelectionAdultFemaleHumansMaleMiddle AgedSurveys and Questionnaires

Identifiers

PMID40653560
PMCPMC12256587

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.