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TRUVACE RECORD VERSION
record: TRV-2026-1582
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-16T10:42:59.458923Z
status: published
lens: trace
sector: health
headline: Is the Application of Artificial Intelligence-Based Competency Assessments for Selecting Clinical Nurses Acceptable? A Qualitative Study
dek: Background Artificial 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 proce…
gain_title: Interviewees and the authors see AI-based competency assessments as having potential to make selecting and hiring new nurses fairer and more efficient.
problem_title: Participants doubted the evaluation method and criteria, and found preparing for the assessments challenging, so validity depends on tailored algorithms and clear criteria.
trace_subject: AI-based competency assessments in South Korean nurse recruitment
gain_reading: Interviewees and the authors see AI-based competency assessments as having potential to make selecting and hiring new nurses fairer and more efficient.
gain_evidence: potential of AI-based competency assessments to improve fairness and efficiency in the process of selecting and hiring new nurses
problem_reading: Participants doubted the evaluation method and criteria, and found preparing for the assessments challenging, so validity depends on tailored algorithms and clear criteria.
problem_evidence: doubts about the evaluation method and criteria | challenges in preparing for AI-based competency assessments
quick_read: Researchers interviewed nursing applicants and nurse educators in South Korea about AI-based competency assessments already built into nurse recruitment. The focus groups ran from February to August 2024, and the analysis grouped the responses into four themes covering doubts, efficiency, preparation challenges and alternatives.

The findings matter because these tools influence who gets hired for patient care roles, and the participants' doubts about the criteria bear on whether the tools can be trusted. The study is small and qualitative, so it shows perceptions rather than measured hiring outcomes, and it does not show that fairness actually improved.
limitation: 
tag: Dual reading
key_points: The study used a qualitative descriptive design with five focus groups. | Participants were 10 nursing applicants and 10 nurse educators, including professors and nurse managers. | Content analysis of the interviews produced 32 codes, eight subthemes and four themes. | The authors call for tailored algorithms that reflect core nursing competencies and for clear evaluation criteria. | The authors also call for interdisciplinary collaboration on ethical and practical integration of AI in nurse hiring.
rundown: The work addresses a gap: AI-based assessments are already part of nurse recruitment in South Korea, but applicants' and educators' experiences with them had not been examined. Data were collected in 2024 through semistructured focus group interviews, with each of the five groups holding three to five participants.

Four themes emerged: doubts about the evaluation method and criteria, efficiency of the assessments, challenges in preparing for them, and improvements and alternative approaches to nurse selection. The COREQ guidelines were used to assess study rigor.

The authors conclude that the assessments could improve fairness and efficiency, but validity requires algorithms tailored to core nursing competencies and clear criteria. They add that interdisciplinary collaboration is needed to support ethical and practical integration.
sources:
- peer_reviewed | Journal of Nursing Research | https://doi.org/10.1097/jnr.0000000000000768 | 2026-10-01
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