ArticleBMC nursing2026
Core competencies of clinical nursing educators in the AI era: a qualitative study.
Article in BMC nursing, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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.
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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.
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Who cites it
1 citing paper in PubMed.
- Integrating case-based learning into the ADDIE model for higher vocational rehabilitation nursing courses: a quasi-experimental study.Frontiers in medicine · 2026Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
backgroundAs 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.
methodsA 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.
resultsFive 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.
conclusionsClinical 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 registrationThis is a qualitative study, not a health care intervention trial, so no trial registration is applicable.
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