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Performance of large language models on nursing licensure examinations: A systematic review and meta-analysis
- Truvace record
- TRV-2026-2792raw data (JSON)
- Version
- v1Published
- First published
- Evidence type
- Systematic review or meta-analysis
- Sources
- 1 sourcePeer-reviewed
- sha256
- befca1084a12…check it
OBJECTIVES: This systematic review and meta-analysis assessed the performance of large language models (LLMs) in nursing licensure examinations. Despite the increasing use of LLMs in healthcare education, their capabilities in nursing licensure examinations remain uncertain. This study provides evidence on the accuracy and limitations of LLMs to help guide their integration into nursing education and licensure. DESIGN: The systematic review and meta-analysis adhered to PRISMA 2020 guidelines. DATA SOURCES: PubMe…
57Gain side
How the two sides scoreClose call
61Problem side
The gain
In a meta-analysis of 12 studies, GPT-4 answered 77.2% of nursing licensure questions correctly versus 60.4% for GPT-3.5, and a domain-customised model reached 93.6%.
The problem
A systematic review of 12 studies covering 13,870 nursing licensure questions found pooled chatbot accuracy of 69.6% with wide variation (I² = 98%), weak ethics and psychosocial results, and a recommendation against unmoderated high-stakes use.
A fixed formula sets both scores from the evidence and its sources. Reader votes don’t move them.
What the scores mean
G is the gain side, out of 100. It goes up when the gain is specific, when it measurably helped people and when the sources behind it are strong.
P is the problem side, out of 100. It goes up when the problem is specific, when it measurably hurt people and when the sources behind it are strong.
What this doesn’t fix
The review included only English-language studies, relied on author-reported outcomes, and found that key settings such as temperature, prompting strategy and scoring were often unreported, which limits what the pooled figures can show.
Sources
1 on file- Nurse Education Todaydoi:10.1016/j.nedt.2026.107154
What the review found when it combined the studies
These are the review’s own pooled results, copied from its abstract. Truvace did not recalculate them.
Accuracy of large language models on multiple-choice nursing licensure exam questions
Accuracy 69.6% (95% CI 65.6% to 73.6%)
search to April to June 2025
The review’s own words
Pooled accuracy was 69.6% (95% CI: 65.6-73.6%) with substantial heterogeneity (I2 = 98%).
Code checks every number above against the review’s text before it is shown. The short labels are an AI model’s reading of that text, so the review’s own sentence sits under each one. A pooled figure is only as strong as the studies behind it. How Truvace uses pooled results
Cite this record
v1Truvace Impact Record TRV-2026-2792, v1: “Performance of large language models on nursing licensure examinations: A systematic review and meta-analysis.” Truvace, 2026-11-12. /record/TRV-2026-2792 (accessed on the day you copy it). sha256 befca1084a12d15b…
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