Maintenance Notice

Due to necessary scheduled maintenance, the JMIR Publications website will be unavailable from Wednesday, July 01, 2020 at 8:00 PM to 10:00 PM EST. We apologize in advance for any inconvenience this may cause you.

Who will be affected?

Accepted for/Published in: JMIR Medical Education

Date Submitted: Sep 14, 2023
Date Accepted: Jun 15, 2024

The final, peer-reviewed published version of this preprint can be found here:

Performance of ChatGPT on Nursing Licensure Examinations in the United States and China: Cross-Sectional Study

Wu Z, Xue Z, Zhang Y, Zheng X, Gan W

Performance of ChatGPT on Nursing Licensure Examinations in the United States and China: Cross-Sectional Study

JMIR Med Educ 2024;10:e52746

DOI: 10.2196/52746

PMID: 39363539

PMCID: 11466054

Performance of ChatGPT on nursing licensure examinations in the United States and China: a cross-sectional study

  • Zelin Wu; 
  • Zhaowen Xue; 
  • Yiming Zhang; 
  • Xiaofei Zheng; 
  • Wenyi Gan

ABSTRACT

Background:

The creation of large language models (LLMs) is a significant step in the evolution of artificial intelligence (AI). The United States and China standardise the theoretical and practical knowledge of nurses through the National Council Licensure Examination for Registered Nurses (NCLEX-RN) and the National Nursing Licensure Examination (NNLE), respectively. However, ineffective nursing education instruments are lacking.

Objective:

Examine how well LLMs respond to U.S. and Chinese multiple-choice questions (MCQs) in the nursing licensure exams. To determine whether LLMs can be utilised as multilingual learning assistance for nursing.

Methods:

First, we compiled 150 NCLEX-RN practise questions, 240 NNLE theoretical MCQs, and 240 NNLE practical MCQs. Then, the translation function of ChatGPT 3.5 is utilised to translate NCLEX-RN questions from English to Chinese and NNLE questions from Chinese to English. Finally, the original version and the translated version of the MCQs are inputted into ChatGPT 4.0, ChatGPT 3.5, and Google Bard, respectively.

Results:

The accuracy rates of ChatGPT4.0 for NCLEX-RN practise questions and Chinese-translated NCLEX-RN practise questions were 88.7% and 79.3%, respectively. Despite the statistical significance of the difference(P=.03), the correct rate was generally satisfactory. 71.9% of NNLE Theoretical MCQs and 69.1% of NNLE Practical MCQs were correctly answered by ChatGPT4.0. The accuracy of ChatGPT4.0 in processing NNLE Theoretical MCQs and NNLE Practical MCQs translated into English was 71.5%(P=.92) and 67.8%(P=.77), respectively, and there was no statistically significant difference between the results of text input in different languages. ChatGPT3.5(NCLEX-RN’s P=.003,NNLE Theoretical P<.001, NNLE Practical P=.12) and Google Bard(NCLEX-RN’s P<.000,NNLE Theoretical P<.001, NNLE Practical P<.001) have lower accuracy rates for nursing-related MCQs than ChatGPT4.0 in English input. English accuracy was higher when compared to ChatGPT3.5's Chinese input, and the difference was statistically significant(NCLEX-RN’s P=.02, NNLE Practical P=.02).

Conclusions:

ChatGPT 4.0 has a high level of accuracy on both the US and Chinese nursing exams' MCQs and provides a thorough explanation of the questions' knowledge points. ChatGPT can be used as an effective auxiliary tool for nursing knowledge learning. Clinical Trial: Not applicable.


 Citation

Please cite as:

Wu Z, Xue Z, Zhang Y, Zheng X, Gan W

Performance of ChatGPT on Nursing Licensure Examinations in the United States and China: Cross-Sectional Study

JMIR Med Educ 2024;10:e52746

DOI: 10.2196/52746

PMID: 39363539

PMCID: 11466054

The author of this paper has made a PDF available, but requires the user to login, or create an account.

© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.