ArticleComputers, informatics, nursing : CIN2026
Transforming Health Care AI Education Through Micro-learning: A Novel Partnership Model for Nursing Workforce Development.
Article in Computers, informatics, nursing : CIN, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Core competencies of clinical nursing educators in the AI era: a qualitative study.BMC nursing · 2026Article
- Beyond the Bedside in the Age of Artificial Intelligence (AI): Preserving the Humanistic Core of Nursing.Investigacion y educacion en enfermeria · 2026Article
- Change Fatigue and Attitudes Toward AI in County-Level Nurses: The Mediating Role of AI Literacy.Journal of nursing management · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Health care professionals face an urgent need for AI literacy as artificial intelligence technologies rapidly transform clinical practice, yet nursing-specific educational resources remain scarce. The objective of this study was to evaluate the effectiveness of an innovative micro-learning AI education program developed through an academic-industry partnership. We implemented 11 micro-courses (4 to 5 hours each) across foundational, application, and advanced competency levels, with nursing-specific content addressing professional scope and leadership opportunities. The program was delivered through Chamberlain University Center for Faculty Excellence and Walden University School of Lifelong Learning. We analyzed enrollment data, learning outcomes, and satisfaction scores from 478 students and faculty with 612 course completions. Among 612 course completions, registered nurses comprised 49% of participants. Students demonstrated significant knowledge gains (Cohen's d=0.65, P <.001) with high satisfaction scores (mean=4.58/5.0). Faculty participants showed exceptional outcomes (satisfaction mean=4.67/5.0) with 99% expressing commitment to applying learning. Content relevance scored highest across all measures (4.61 to 4.71), indicating integration of academic rigor with practical applicability. This micro-learning approach addresses critical gaps in health care AI education through scalable, nursing-specific curriculum. The partnership model bridges academic expertise with industry relevance, providing a replicable framework for systematic workforce preparation in AI-enhanced health care delivery.
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What OpenQuestion holds
Registered trials
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.