Core competencies of clinical nursing educators in the AI era: a qualitative study

BACKGROUND: As artificial intelligence (AI) becomes increasingly integrated into healthcare and nursing education, existing competency standards and training systems for clinical nursing educators are no longer sufficient to meet the demands of technology-enabled teaching. This study aimed to explore the lived experiences and perceived competency demands and training needs of clinical nursing educators in the AI era, and to provide empirical evidence to inform competency framework development and training-system optimization. METHODS: A descriptive phenomenological qualitative study was conducted in four tertiary hospitals in Chengdu, China, between July and September 2025. Fourteen clinical nursing educators were recruited using purposive sampling. Data were collected through semi-structured interviews and analyzed using Colaizzi's seven-step method. RESULTS: Five themes and 17 subthemes were identified. The core competency framework comprised four key domains: professional foundation and specialized technical skills, communication and humanistic care, AI tool application, and research translation. Training-system optimization involved four interrelated areas: systematic training design, incentive and support mechanisms, specialty-based and tiered teaching design, and the educational value and practical challenges of integrating AI into clinical teaching. These findings suggest that clinical nursing educators require both foundational teaching competencies and new AI-related capabilities. CONCLUSIONS: Clinical nursing educators in the AI era require a competency-based development pathway that integrates pedagogical, clinical, technological, and research capabilities. Hospitals and nursing education institutions should establish tiered training programs, practice-oriented assessments, and supportive organizational policies to strengthen educator preparedness and facilitate the intelligent transformation of clinical nursing education. TRIAL REGISTRATION: This is a qualitative study, not a health care intervention trial, so no trial registration is applicable.

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Publication Details

Journal
BMC Nursing
Published
2026-04-30
DOI
https://doi.org/10.1186/s12912-026-04686-w
Primary Topic
Simulation-Based Education in Healthcare
Type
article
Field-Weighted Citation Impact
0.00
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article

Core competencies of clinical nursing educators in the AI era: a qualitative study

Deng Shuxia, Chaoya Hu, Xiaoxia Lin, Ting Luo et al.
BMC Nursing
Simulation-Based Education in Healthcare
article

Core competencies of clinical nursing educators in the AI era: a qualitative study

Deng Shuxia, Chaoya Hu, Xiaoxia Lin, Ting Luo, Qian Yang, Xueqin Gan, Yuhang Chen
article en

Abstract

BACKGROUND: As artificial intelligence (AI) becomes increasingly integrated into healthcare and nursing education, existing competency standards and training systems for clinical nursing educators are no longer sufficient to meet the demands of technology-enabled teaching. This study aimed to explore the lived experiences and perceived competency demands and training needs of clinical nursing educators in the AI era, and to provide empirical evidence to inform competency framework development and training-system optimization. METHODS: A descriptive phenomenological qualitative study was conducted in four tertiary hospitals in Chengdu, China, between July and September 2025. Fourteen clinical nursing educators were recruited using purposive sampling. Data were collected through semi-structured interviews and analyzed using Colaizzi's seven-step method. RESULTS: Five themes and 17 subthemes were identified. The core competency framework comprised four key domains: professional foundation and specialized technical skills, communication and humanistic care, AI tool application, and research translation. Training-system optimization involved four interrelated areas: systematic training design, incentive and support mechanisms, specialty-based and tiered teaching design, and the educational value and practical challenges of integrating AI into clinical teaching. These findings suggest that clinical nursing educators require both foundational teaching competencies and new AI-related capabilities. CONCLUSIONS: Clinical nursing educators in the AI era require a competency-based development pathway that integrates pedagogical, clinical, technological, and research capabilities. Hospitals and nursing education institutions should establish tiered training programs, practice-oriented assessments, and supportive organizational policies to strengthen educator preparedness and facilitate the intelligent transformation of clinical nursing education. TRIAL REGISTRATION: This is a qualitative study, not a health care intervention trial, so no trial registration is applicable.

BMC Nursing
Chengdu Medical College (CN), Southwest Medical University (CN), Affiliated Hospital of Southwest Medical University (CN), First Affiliated Hospital of Chengdu Medical College (CN), Chengdu Second People's Hospital (CN)
Quality Education
Openalex Percentile: Top 10%
Simulation-Based Education in Healthcare
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