Patterns, advances, and gaps in using ChatGPT and similar technologies in nursing education: A PAGER scoping review
Bibliographic record
Abstract
BACKGROUND AND AIM: Generative AI (GenAI) can transform nursing education and modernise content delivery. However, the rapid integration of these tools has raised concerns about academic integrity and teaching quality. Previous reviews have either looked broadly at artificial intelligence or focused narrowly on single tools like ChatGPT. This scoping review uses a structured framework to identify patterns, advances, gaps, evidence, and recommendations for implementing GenAI in nursing education. METHODS: This scoping review followed the JBI methodology and PRISMA-ScR guidelines. We searched PubMed, CINAHL, SCOPUS, ERIC, and grey literature (October to November 2024). Data synthesis utilised the PAGER framework as a mapping tool to organise and describe patterns, advances, gaps, evidence for practice, and recommendations. RESULTS: Analysis of 107 studies revealed GenAI implementation across four key domains: assessment and evaluation, clinical simulation, educational content development, and faculty/student support. Three distinct implementation patterns emerged: restrictive, integrative, and hybrid approaches, with hybrid models demonstrating superior adoption outcomes. Technical advances showed significant improvement from GPT-3.5 (75.3 % accuracy) to GPT-4 (88.67 % accuracy) in NCLEX-style assessments, with enhanced capabilities in multilingual assessment, clinical scenario generation, and adaptive content creation. Major gaps included limited methodological rigour (29.0 % of empirical studies), inconsistent quality control, verification challenges, equity concerns, and inadequate faculty training. Geographic distribution showed North American (42.1 %) and Asian (29.9 %) dominance, with ChatGPT representing 83.2 % of tool implementations. Key recommendations include developing institutional policies, establishing quality verification protocols, enhancing faculty training programs, and addressing digital equity concerns to optimise GenAI integration in nursing education. CONCLUSIONS: GenAI has transformative potential in nursing education. To realise its full potential and ensure responsible use, research should focus on developing standardised governance frameworks, empirically validating outcomes, developing faculty in AI literacy, and improving technical infrastructure for low-income contexts. Such efforts should involve international collaboration, highlighting the importance of the audience's role in the global healthcare community.
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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 | 0.059 | 0.222 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.037 | 0.037 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".