Evidence map›Paper›PMID 42596885›Full record

ArticleThe journal of nursing research : JNR2026

Is the Application of Artificial Intelligence-Based Competency Assessments for Selecting Clinical Nurses Acceptable? A Qualitative Study.

Hyeongsuk Lee, Hyeongju Ryu, Hye Jin Yoo

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Article in The journal of nursing research : JNR, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Hyeongsuk LeeCollege of Nursing, Research Institute of AI and Nursing Science, Gachon University, Incheon, Republic of Korea.ORCID 0000-0001-7597-3163
Hyeongju RyuDepartment of Nursing, and Research Institute for Biomedical Health Science, Konkuk University, Chungju, Republic of Korea.ORCID 0000-0002-7060-3873
Hye Jin YooCollege of Nursing Science, Kyung Hee University, Seoul, Republic of Korea.ORCID 0000-0001-9337-5167

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial intelligence (AI)-based competency assessments have been incorporated into nurse recruitment processes in South Korea; however, nursing applicants' and educators' experiences with these assessments, as well as their perceptions of the effectiveness of these assessments in nurse selection, have not been examined. PURPOSE: The aim of this study was to comprehensively explore the perceptions of nursing applicants and educators of the effectiveness of AI-based competency assessments in the process of selecting new nurses for patient care roles.

methodsA qualitative descriptive design was used in this study. Data were collected between February and August 2024 in South Korea. Semistructured focus group interviews were conducted with 10 nursing applicants and 10 nurse educators, including nursing professors and nurse managers, regarding their experience participating in AI-based competency assessments. Focus group interviews were conducted in five groups, each consisting of three to five participants. Data were analyzed using conventional content analysis. The COREQ (Consolidated Criteria for Reporting Qualitative Research) guidelines were used to assess study rigor.

resultsThe analysis of the interviews revealed eight subthemes and four themes derived from 32 codes. The following four themes were identified: (a) doubts about the evaluation method and criteria, (b) efficiency of AI-based competency assessments, (c) challenges in preparing for AI-based competency assessments, and (d) improvements and alternative approaches to nurse selection. CONCLUSIONS/IMPLICATIONS FOR PRACTICE: This study highlights the potential of AI-based competency assessments to improve fairness and efficiency in the process of selecting and hiring new nurses. To ensure validity, developing tailored algorithms that reflect core nursing competencies and establishing clear evaluation criteria is necessary. Also, interdisciplinary collaboration is needed to support the ethical and practical integration of AI in the process of selecting and hiring new nurses.

Indexed as

Artificial IntelligenceClinical CompetencePersonnel SelectionAdultFemaleFocus GroupsHumansMaleQualitative ResearchRepublic of Koreaartificial intelligenceclinical competencenursesqualitative research

Identifiers

PMID42596885
PMCPMC13608333

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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.