Evidence map›Paper›PMID 41036128›Full record

ArticleFrontiers in public health2025

The current status, knowledge, attitudes, and challenges of generative artificial intelligence use among undergraduate nursing students: a single-center cross-sectional survey of western China.

Yuanyuan Zhao, You Yuan, Zhuosi Wen, Lanlan Leng, Lei Shi, Xinyang Hu, Xiaoman Wei, Meng Zuo, Jianghong Mou, Qian Luo and 3 more

Abstract read
In one paragraph

Article in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 pooled it
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Yuanyuan Zhao *Department of Critical Care Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, China.
You Yuan *Department of Critical Care Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, China.
Zhuosi WenSchool of Nursing, Zunyi Medical University, Zunyi, Guizhou, China.
Lanlan LengSchool of Nursing, Zunyi Medical University, Zunyi, Guizhou, China.
Lei ShiSchool of Nursing, Zunyi Medical University, Zunyi, Guizhou, China.
Xinyang HuDepartment of Nursing, Zunyi Medical and Pharmaceutical College, Zunyi, Guizhou, China.
Xiaoman WeiSchool of Nursing, Zunyi Medical University, Zunyi, Guizhou, China.
Meng ZuoSchool of Nursing, Zunyi Medical University, Zunyi, Guizhou, China.
Jianghong MouSchool of Nursing, Zunyi Medical University, Zunyi, Guizhou, China.
Qian LuoDepartment of Critical Care Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, China.
Mei ChenDepartment of Critical Care Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, China.
Rujun HuDepartment of Critical Care Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, China.
Huiming GaoDepartment of Critical Care Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Generative artificial intelligence (Gen AI) is rapidly permeating the fields of education and healthcare, with increasing impact on nursing education. Understanding nursing students' acceptance of Gen AI and the challenges they face is essential for optimizing future curriculum design. Objective: This study aimed to assess the current usage, knowledge level, attitudes, and perceived challenges of Gen AI among undergraduate nursing students in western China, to inform the effective integration of AI into nursing education. Methods: A single-center, cross-sectional study was conducted using a structured, validated questionnaire that covered five domains: demographics, AI tool usage, knowledge, attitude, and challenges. Participants were undergraduate nursing students from Zunyi Medical University. Data were collected via an online platform from May to June 2025 and analyzed using SPSS 29.0 for descriptive and inferential statistics based on demographic subgroups. Results: A total of 534 valid responses were analyzed. Females accounted for 80.15%, with a mean age of 20.88 years. Grade distribution: sophomore (30.71%), freshman (22.47%), senior (24.53%), and junior (22.28%); 64.79% of students were from urban backgrounds. About 57.86% reported frequent or consistent use of Gen AI tools, mainly via smartphones (94.76%). Most students used 2-3 tools (70.41%), with DeepSeek (72.10%) and Doubao (69.85%) being the most popular. Primary uses included problem-solving (84.46%), course support (66.29%), and academic writing (51.87%). Daily multiple usage was reported by 25.47, and 87.45% used AI for less than 30 min per session. Primary information sources were social media (78.09%) and peer recommendations (71.35%). Median scores: knowledge 3.43 (IQR 2.86-3.86), attitude 3.58 (IQR 3.33-3.83), challenges 3.50 (IQR 3.17-3.92). Only 38.01% received AI-related training; 83.33% found it challenging to ask probing or insightful questions when using Gen AI. Students demonstrated moderate knowledge and positive attitudes, but faced notable concerns, particularly regarding data privacy, tool reliability, and the impact on critical thinking skills. Conclusion: Undergraduate nursing students in western China exhibit a generally positive yet cautious attitude toward Gen AI. Targeted educational interventions are recommended to address their concerns and enhance the benefits of AI in nursing education. Future research should focus on the development of AI literacy and the long-term implications of integrating AI into clinical nursing practice.

Indexed as

Artificial IntelligenceHealth Knowledge, Attitudes, PracticeStudents, NursingAdultChinaCross-Sectional StudiesEducation, Nursing, BaccalaureateFemaleGenerative Artificial IntelligenceHumansMaleSurveys and QuestionnairesYoung Adultattitudeschallengesgenerative artificial intelligenceknowledgenursing educationundergraduate nursing students

Identifiers

PMID41036128
PMCPMC12479433

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.