Transforming Healthcare AI Education Through Micro-Learning: A Novel Partnership Model for Nursing Workforce Development
Bibliographic record
Abstract
Abstract Healthcare 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 (4-5 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 < 0.001) 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 (4.61-4.71), indicating integration of academic rigor with practical applicability. This micro-learning approach addresses critical gaps in healthcare 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 healthcare delivery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".