Assessing question characteristic influences on ChatGPT's performance and response-explanation consistency: Insights from Taiwan's Nursing Licensing Exam

一致性(知识库) 考试(生物学) 心理学 逻辑回归 护士教育 优势比 等级制度 护理部 可能性 医学 医学教育 计算机科学 病理 人工智能 古生物学 经济 内科学 市场经济 生物
作者
Mei-Chin Su,Li-En Lin,Lihwa Lin,Yu‐Chun Chen
出处
期刊:International Journal of Nursing Studies [Elsevier]
卷期号:153: 104717-104717 被引量:15
标识
DOI:10.1016/j.ijnurstu.2024.104717
摘要

Investigates the integration of artificial intelligence tool, specifically ChatGPT, in nursing education, addressing its effectiveness in exam preparation and self-assessment. This study aims to evaluate the performance of ChatGPT, one of the most promising artificial intelligence-driven linguistic understanding tools in answering question banks for nursing licensing examination preparation. It further analyzes question characteristics that might impact the accuracy of ChatGPT-generated answers and examines its reliability through human expert reviews. Cross-sectional survey comparing ChatGPT-generated answers and their explanations. 400 questions from Taiwan's 2022 Nursing Licensing Exam. The study analyzed 400 questions from five distinct subjects of Taiwan's 2022 Nursing Licensing Exam using the ChatGPT model which provided answers and in-depth explanations for each question. The impact of various question characteristics, such as type and cognitive level, on the accuracy of the ChatGPT-generated responses was assessed using logistic regression analysis. Additionally, human experts evaluated the explanations for each question, comparing them with the ChatGPT-generated answers to determine consistency. ChatGPT exhibited overall accuracy at 80.75 % for Taiwan's National Nursing Exam, which passes the exam. The accuracy of ChatGPT-generated answers diverged significantly across test subjects, demonstrating a hierarchy ranging from General Medicine at 88.75 %, Medical-Surgical Nursing at 80.0 %, Psychology and Community Nursing at 70.0 %, Obstetrics and Gynecology Nursing at 67.5 %, down to Basic Nursing at 63.0 %. ChatGPT had a higher probability of eliciting incorrect responses for questions with certain characteristics, notably those with clinical vignettes [Odds ratio 2.19, 95 % confidence interval 1.24–3.87, P = 0.007] and complex multiple-choice questions [Odds ratio 2.37, 95 % confidence interval 1.00–5.60, P = 0.049]. Furthermore, 14.25 % of ChatGPT-generated answers were inconsistent with their explanations, leading to a reduction in the overall accuracy to 74 %. This study reveals the ChatGPT's capabilities and limitations in nursing exam preparation, underscoring its potential as an auxiliary educational tool. It highlights the model's varied performance across different question types and notable inconsistencies between its answers and explanations. The study contributes significantly to the understanding of artificial intelligence in learning environments, guiding the future development of more effective and reliable artificial intelligence-based educational technologies. New study reveals ChatGPT's potential and challenges in nursing education: Achieves 80.75 % accuracy in exam prep but faces hurdles with complex questions and logical consistency. #AIinNursing #AIinEducation #NursingExams #ChatGPT.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
terryok发布了新的文献求助10
刚刚
1秒前
1秒前
1秒前
韩七安完成签到,获得积分10
1秒前
开朗涔发布了新的文献求助10
1秒前
量子星尘发布了新的文献求助10
2秒前
orixero应助黎明采纳,获得10
2秒前
斯文败类应助zyq采纳,获得10
2秒前
3秒前
简单缘分完成签到,获得积分10
3秒前
4秒前
美好斓应助小丘2024采纳,获得100
5秒前
5秒前
戏子完成签到,获得积分10
6秒前
6秒前
番茄黄瓜芝士片完成签到 ,获得积分10
6秒前
劳伦斯发布了新的文献求助10
7秒前
7秒前
香蕉觅云应助上彐下火采纳,获得10
8秒前
张柏柳发布了新的文献求助10
9秒前
sharyzhou发布了新的文献求助10
10秒前
10秒前
10秒前
10秒前
10秒前
Jasper应助biu采纳,获得10
10秒前
oozawa完成签到 ,获得积分10
11秒前
小马甲应助大胆妖精采纳,获得10
11秒前
12秒前
yeezy123发布了新的文献求助10
12秒前
12秒前
GRATE完成签到 ,获得积分10
12秒前
米糊发布了新的文献求助50
13秒前
传奇3应助nuo采纳,获得10
13秒前
Alpha完成签到,获得积分10
14秒前
bkagyin应助terryok采纳,获得50
14秒前
yqt完成签到,获得积分10
15秒前
15秒前
zyq发布了新的文献求助10
15秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
The Social Work Ethics Casebook: Cases and Commentary (revised 2nd ed.).. Frederic G. Reamer 1070
Alloy Phase Diagrams 1000
Introduction to Early Childhood Education 1000
2025-2031年中国兽用抗生素行业发展深度调研与未来趋势报告 1000
List of 1,091 Public Pension Profiles by Region 891
Historical Dictionary of British Intelligence (2014 / 2nd EDITION!) 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 物理化学 基因 遗传学 催化作用 冶金 量子力学 光电子学
热门帖子
关注 科研通微信公众号,转发送积分 5424419
求助须知:如何正确求助?哪些是违规求助? 4538767
关于积分的说明 14163869
捐赠科研通 4455739
什么是DOI,文献DOI怎么找? 2443880
邀请新用户注册赠送积分活动 1435011
关于科研通互助平台的介绍 1412337