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TRUVACE RECORD VERSION
record: TRV-2026-2792
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T17:24:43.576899Z
status: published
lens: trace
sector: education
headline: Performance of large language models on nursing licensure examinations: A systematic review and meta-analysis
dek: 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…
gain_title: 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%.
problem_title: 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.
trace_subject: AI chatbot accuracy on nursing licensure exam questions
gain_reading: 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%.
gain_evidence: GPT-4 outperformed GPT-3.5 (77.2% vs. 60.4% | domain-customised and newer models reached 93.6%
problem_reading: 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.
problem_evidence: Pooled accuracy was 69.6% (95% CI: 65.6–73.6%) | underperformed in ethics and psychosocial integrity | unsuitable for unmoderated, high-stakes licensure decisions
quick_read: A systematic review and meta-analysis in Nurse Education Today pooled 12 studies in which AI chatbots answered multiple-choice questions from nursing licensure exams. The studies covered 13,870 questions, five exam systems and eight models. Overall accuracy was 69.6% (95% CI 65.6–73.6%), with a prediction interval of 43.8% to 95.3%.

Newer and customised models did much better than early ones. GPT-4 reached 77.2% against 60.4% for GPT-3.5, Qwen 2.5 reached 88.9%, and a task-specific Custom GPT reached 93.6%. Google Bard scored lowest at 53.3%. The authors conclude the tools suit low-stakes uses such as formative assessment but not unmoderated licensure decisions.

The pooled figure hides large gaps between studies (I² = 98%). Chatbots did well in general medicine and pharmacology but poorly in ethics and psychosocial integrity, and accuracy fell when NCLEX-RN items were translated. Nine of the 12 studies came from East Asia, so results for Western exams rest on less evidence.
limitation: 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.
tag: Dual reading
key_points: The review searched PubMed, CINAHL, PsycINFO, EMCARE and ERIC from April to June 2025 and included 12 studies, which assessed 13,870 multiple-choice questions. | Pooled accuracy was 69.6% (95% CI 65.6–73.6%) with a wide prediction interval of 43.8–95.3% and very high heterogeneity (I² = 98%). | GPT-4 scored 77.2% against 60.4% for GPT-3.5; GPT-4o scored 80.7%, Qwen 2.5 88.9%, a Custom GPT 93.6%, and Google Bard 53.3%. | Chatbots were strongest in general medicine (88.75%) and weakest in psychosocial integrity, where one included study reported 13.3%. | Translating NCLEX-RN items into Chinese lowered accuracy significantly (p = 0.03), while translated NNLE items showed no such drop (p = 0.92). | Image-based questions were a weak spot: one included study reported 56.3% accuracy on visual items. | Nine of the 12 studies came from East Asia (China, Japan, Taiwan), and temperature settings were reported in only two studies.
rundown: The authors screened 117 records and included 12 studies after excluding 27 at eligibility assessment. The studies tested models on exams from the United States (NCLEX-RN/PN), China (NNLE, CNNLE), Japan (JNNE), Taiwan (RNLE), Norway and Spain. GPT-3.5 appeared in eight studies and GPT-4 in four. Chinese models such as Qwen-2.5, ERNIE and SPARK appeared only in a single seven-model comparison.

Against national pass marks, results were mixed. GPT-4 scored 92.4% on NCLEX practice questions in Spain against a 77% benchmark, 80.75% on the Taiwan exam and 79.7% on the Japanese exam, while GPT-3.5 scored 59.9% there and missed the Japanese standard. In China, ChatGPT failed to reach the 60% threshold across five years of exams.

Complex formats lowered scores. One study found significant effects for clinical vignettes (p = 0.007) and complex multiple-choice items (p = 0.049), and another reported scenario-based accuracy from 51.7% to 80.0%. Even so, the pooled analysis found no significant differences by question difficulty level (p = 0.90) or by question format (p = 0.96).

The authors recommend regulators separate educational from high-stakes uses, avoid unmoderated use in licensure, and develop nursing-specific and culturally adapted models. They also call for standardised reporting, error analysis, comparisons with human test-takers and more research on Western exams.
sources:
- peer_reviewed | Nurse Education Today | https://doi.org/10.1016/j.nedt.2026.107154 | 2026-05-12
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