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ORIGINAL RESEARCH article

Front. Public Health

Sec. Public Health Education and Promotion

Volume 13 - 2025 | doi: 10.3389/fpubh.2025.1648416

This article is part of the Research TopicAI in Public Health Teaching and Education: Current Trends and Future OutlookView all 4 articles

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

Provisionally accepted
Yuanyuan  ZhaoYuanyuan Zhao1You  YuanYou Yuan1*Zhuosi  WenZhuosi Wen2Lanlan  LengLanlan Leng2Lei  ShiLei Shi2Xinyang  HuXinyang Hu3Xiaoman  WeiXiaoman Wei2Meng  ZuoMeng Zuo2Jianghong  MouJianghong Mou2Qian  LuoQian Luo2Mei  ChenMei Chen1,2*Rujun  HuRujun Hu1,2*Huiming  GaoHuiming Gao1,2*
  • 1Affiliated Hospital of Zunyi Medical University, Zunyi, China
  • 2Zunyi Medical University, Zunyi, China
  • 3Zunyi Medical and Pharmaceutical College, Zunyi, China

The final, formatted version of the article will be published soon.

Background: Generative artificial intelligence 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 minutes per session. Primary information sources were social media (78.09%) and peer recommendations (71.35%).Median scores: knowledge 3.43 (2.86,3.86), attitude 3.58 (3.33,3.83), challenges 3.50 (3.17,3.92). Only 38.01% received AI-related training; 83.33% found it challenging to ask probing or insightful questions 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.

Keywords: Generative artificial intelligence, Nursing education, Undergraduate nursing students, attitudes, knowledge, Challenges

Received: 17 Jun 2025; Accepted: 03 Sep 2025.

Copyright: © 2025 Zhao, Yuan, Wen, Leng, Shi, Hu, Wei, Zuo, Mou, Luo, Chen, Hu and Gao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
You Yuan, Affiliated Hospital of Zunyi Medical University, Zunyi, China
Mei Chen, Zunyi Medical University, Zunyi, China
Rujun Hu, Affiliated Hospital of Zunyi Medical University, Zunyi, China
Huiming Gao, Affiliated Hospital of Zunyi Medical University, Zunyi, China

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