# AI-Based Competency Assessment

Using a qualitative descriptive design with focus group interviews conducted in South Korea between February and August 2024, this study interviewed 10 nursing applicants with prior AI-based competency assessment (AICA) experience and 10 nurse educators (professors and nurse managers), and through conventional content analysis derived eight subthemes and four themes from 32 codes, finding that participants recognized AICA's efficiency and objectivity for large-scale recruitment while questioning opaque evaluation criteria, misalignment with person-centered nursing values, and fairness risks from network, lighting, device, and information-support gaps, and recommending nurse-specific algorithms, clear criteria, and positioning AI as a support to face-to-face interviews.

Using a qualitative descriptive design with focus group interviews conducted in South Korea between February and August 2024, this study interviewed 10 nursing applicants with prior AI-based competency assessment (AICA) experience and 10 nurse educators (professors and nurse managers), and through conventional content analysis derived eight subthemes and four themes from 32 codes, finding that participants recognized AICA's efficiency and objectivity for large-scale recruitment while questioning opaque evaluation criteria, misalignment with person-centered nursing values, and fairness risks from network, lighting, device, and information-support gaps, and recommending nurse-specific algorithms, clear criteria, and positioning AI as a support to face-to-face interviews.