职业院校护理专业教师对学生使用人工智能工具回答主观题的辨识能力调查研究

doi:10.3761/j.issn.1672-9234.2026.10.005

中华护理教育 ›› 2026, Vol. 23 ›› Issue (10): 1180-1185.doi: 10.3761/j.issn.1672-9234.2026.10.005

• 专题策划——教师发展 • 上一篇下一篇

职业院校护理专业教师对学生使用人工智能工具回答主观题的辨识能力调查研究

陈冬¹(), 潘彦彦¹, 张玉环², 信博³, 王磊⁴, 于海珍⁵, 杨丽妲¹^,^*()

¹ 黑龙江护理高等专科学校护理系哈尔滨市 150086
² 哈尔滨医科大学附属第二医院学生科哈尔滨市 150086
³ 西安交通大学护理学院西安市 710049
⁴ 大连医科大学护理学院大连市 116044
⁵ 哈尔滨医科大学附属第一医院创伤骨科哈尔滨市 150086

收稿日期:2026-02-01 出版日期:2026-10-15 发布日期:2026-10-15
通讯作者: *杨丽妲，E-mail：yang_lida@126.com
作者简介:陈冬：女，硕士，讲师，E-mail：18245119625@163.com
作者贡献声明
构思设计、论文撰写：陈冬；问卷发放与数据录入：潘彦彦、杨丽妲；数据处理：陈冬、信博；英文撰写与修订：张玉环；论文修订与审校：于海珍、王磊、张玉环。
基金资助:
大连市社科联2025年第三批立项课题(2025dlskyb396)

A survey on the identification ability of vocational college nursing teachers for students’ AI tool misuse in subjective questions

CHEN Dong¹(), PAN Yanyan¹, ZHANG Yuhuan², XIN Bo³, WANG Lei⁴, YU Haizhen⁵, YANG Lida¹^,^*()

¹ Department of Nursing， Heilongjiang Nursing College， Harbin 150086， China
² Department of Student Affairs， the Second Affiliated Hospital of Harbin Medical University， Harbin 150086， China
³ School of nursing， Xi’an Jiaotong University， Xi’an 710049， China
⁴ School of Nursing， Dalian Medical University， Dalian 116044， China
⁵ Department of Traumatic Orthopedics， the First Hospital of Harbin Medical University， Harbin 150086， China

Received:2026-02-01 Online:2026-10-15 Published:2026-10-15
Contact: *YANG Lida，E-mail：yang_lida@126.com
Supported by:
The Third Batch of Approved Projects of Dalian Federation of Social Sciences in 2025(2025dlskyb396)

摘要/Abstract

摘要：

目的了解职业教育护理专业教师对学生主观题使用人工智能（artificial intelligence，AI）工具抄袭的辨识能力现状及影响因素，为实施针对性干预提供参考。方法采用一般资料调查表、AI素养量表和教师对学生主观题AI工具抄袭辨识能力测试对276名职业教育护理专业教师进行调查。结果职业教育护理专业教师对学生主观题AI工具抄袭辨识能力测试总分为16.00（12.25，18.75）分。广义线性模型分析结果显示，教师职称、是否参加过AI相关培训或讲座、日常应用AI工具频次及教师AI素养是职业教育护理专业教师对学生主观题AI工具抄袭辨识能力的影响因素（P<0.05）。结论学校或相关主体应关注职业教育护理专业教师对学生主观题AI工具抄袭辨识能力。可根据教师职称情况，通过为教师提供AI相关培训或讲座、鼓励教师加大日常应用AI工具频次及提高AI素养，提升职业教育护理专业教师对学生主观题AI工具抄袭辨识能力。

关键词: 职业教育, 护理专业, 教师, 人工智能, 主观题, 抄袭, 学术诚信, 教学改革

Abstract:

Objective To investigate the current situation and influencing factors of the ability of nursing teachers in vocational education to identify AI-assisted plagiarism in subjective questions of nursing students，and to provide reference for the implementation of targeted intervention. Methods A survey was conducted on 276 vocational education nursing teachers using a general information questionnaire，the AI Literacy Scale，and an AI tools plagiarism identification ability test for subjective questions of nursing students. Results The total score of AI tools plagiarism identification ability of vocational nursing teachers on subjective questions of nursing students was 16.00（12.25，18.75）. Generalized linear model analysis revealed that teachers’ professional title，whether they had participated in AI-related training or lectures，frequency of daily application of AI tools，and teachers’ AI literacy were identified as influencing factors of the ability（P<0.05）. Conclusion Schools or relevant entities should pay attention to vocational nursing teachers’ ability to identify AI tools plagiarism in students’ subjective questions. Targeted strategies to enhance this ability may include providing AI-related training or lectures based on teachers’ professional titles，encouraging more frequent use of AI tools in daily practice，and improving teachers’ AI literacy.

Key words: Vocational Education, Nursing Major, Teacher, Artificial Intelligence, Subjective Question, Plagiarism, Academic Integrity, Teaching Reform

陈冬, 潘彦彦, 张玉环, 信博, 王磊, 于海珍, 杨丽妲. 职业院校护理专业教师对学生使用人工智能工具回答主观题的辨识能力调查研究[J]. 中华护理教育, 2026, 23(10): 1180-1185.

CHEN Dong, PAN Yanyan, ZHANG Yuhuan, XIN Bo, WANG Lei, YU Haizhen, YANG Lida. A survey on the identification ability of vocational college nursing teachers for students’ AI tool misuse in subjective questions[J]. Chinese Journal of Nursing Education, 2026, 23(10): 1180-1185.

图/表 3

参考文献 19

[1]	Liang WX, Yuksekgonul M, Mao YN, et al. GPT detectors are biased against non-native English writers[J]. Patterns, 2023, 4(7):100779. doi: 10.1016/j.patter.2023.100779
[2]	吴嫩萍, 陈少华, 余标君, 等. 高职护理专业学生应用生成式人工智能辅助学习的现状与思考[J]. 中华护理教育, 2025, 22(4):403-409.
	Wu NP, Chen SH, Yu BJ, et al. The current situation and reflection on the application of generative artificial intelligence assisted learning for vocational nursing students[J]. Chin J Nurs Educ, 2025, 22(4):403-409.
[3]	彭玉勃, 孙丹, 陈冬, 等. 线上学习耐心量表的汉化及在护理专业学生中的信效度检验[J]. 中华护理教育, 2026, 23(2):182-188.
	Peng YB, Sun D, Chen D, et al. Reliability and validity of the Chinese version of the Student Online Learning Patience Scale in nursing students[J]. Chin J Nurs Educ, 2026, 23(2):182-188.
[4]	Sağlam RK, Kalanlar B. Living with and without AI:a mixed-methods study on AI usage,addiction,and ‘AIlessphobia’ in nurs-ing students[J]. Nurse Educ Pract, 2025, 88:104530. doi: 10.1016/j.nepr.2025.104530
[5]	Chiu TKF, Moorhouse BL, Chai CS, et al. Teacher support and student motivation to learn with Artificial Intelligence(AI) based chatbot[J]. Interact Learn Environ, 2024, 32(7):3240-3256. doi: 10.1080/10494820.2023.2172044
[6]	Alsharefeen R. Faculty as street-level bureaucrats:discretionary decision-making in the era of generative AI[J]. Front Educ, 2025, 10:1662657. doi: 10.3389/feduc.2025.1662657
[7]	Alsadaan N, Ramadan OME, Alqahtani M, et al. Examining the mediating role of intrinsic motivation in Laissez-Faire leadership and nurses’ task performance:a cross-sectional study[J]. BMC Nurs, 2025, 24(1):1195. doi: 10.1186/s12912-025-03869-1 pmid: 41013525
[8]	Wang BC, Rau PP, Yuan TY. Measuring user competence in using artificial intelligence:validity and reliability of artificial intelligence literacy scale[J]. Behav Inf Technol, 2023, 42(9):1324-1337. doi: 10.1080/0144929X.2022.2072768
[9]	刘建国, 王胜龙. 人工智能通识与应用[M]. 哈尔滨: 黑龙江人民出版社, 2025:32-35.
	Liu JG, Wang SL. General knowledge and application of artificial intelligence[M]. Harbin: Heilongjiang People’s Publishing House, 2025:32-35.
[10]	柳国杰, 陈军. 教育测量中难度与区分度的计算方法[J]. 信阳师范学院学报(自然科学版), 2003, 16(3):351-352.
	Liu GJ, Chen J. Research on calculating methods of difficulty and differentiation in education survey[J]. J Xinyang Norm Univ (Nat Sci Ed), 2003, 16(3):351-352.
[11]	Carruba MC, Caiazzo A, Scuotto C, et al. A grade for artificial intelligence:a study on school teachers’ ability to identify assignments written by generative artificial intelligence[J]. Cyberpsychol Behav Soc Netw, 2025, 28(7):489-496. doi: 10.1089/cyber.2024.0524
[12]	Morari V, Grimes D, Hawe D. Academic integrity and generative artificial intelligence-views and perceptions of students in an Irish University[J]. J Acad Ethics, 2025, 24(1):17. doi: 10.1007/s10805-025-09689-x
[13]	郑思佳. 教育数字化转型背景下提升高职院校教师数字胜任力的路径探索[J]. 创新创业理论研究与实践, 2026, 9(12):133-135.
	Zheng SJ. Exploring the paths to enhance the digital competence of higher vocational teachers under the background of educational digital transformation[J]. Theory Pract Innov Enterpreneurship, 2026, 9(12):133-135.
[14]	Arighi W. How generative AI policies and training can empower faculty and students[J]. Dean Provost, 2025, 26(9):3-4.
[15]	Møgelvang A, Cipriani E, Grassini S. Generative AI in action: acceptance and use among higher education staff pre- and post-training[J/OL]. Technol Knowl Learn,2025-10-30. https://link.springer.com/article/10.1007/s10758-025-09915-w.
[16]	匡恺, 刘力铭. 生成式人工智能的风险特征及语义图像:基于网络社区讨论的计算文本分析[J]. 河北大学学报(哲学社会科学版), 2024, 49(6):147-160. doi: 10.3969/j.issn.1005-6378.2024.06.011
	Kuang K, Liu LM. Risk characteristics and semantic imagery of generative AI:a computational text analysis based on online community discussions[J]. J Hebei Univ Philos Soc Sci, 2024, 49(6):147-160.
[17]	陈莺, 蒋艳双, 马啸天, 等. 人机协同教学中智能体应用:场景适配、价值意蕴与困境超越[J]. 中国电化教育, 2025(11):26-33.
	Chen Y, Jiang YS, Ma XT, et al. Intelligent agent for human-machine collaborative teaching:scenario adaptation,value implications and overcoming dilemma[J]. China Educ Technol, 2025(11):26-33.
[18]	Meegan J, Young K. It’s a tool not a crutch:a PilotGenerative AI intervention to enhance pre-service teachers’ self-efficacy and AI literacy[J]. Technol Knowl Learn, 2025, 30(4):1895-1916. doi: 10.1007/s10758-025-09875-1
[19]	Ehmke SD, Bridges J, Patel SE. Self-perceived knowledge,skills,and attitude of nursing faculty on generative artificial intelligence in nursing education:a descriptive,cross-sectional study[J]. Teach Learn Nurs, 2025, 20(3):222-227. doi: 10.1016/j.teln.2025.01.029

职业院校护理专业教师对学生使用人工智能工具回答主观题的辨识能力调查研究

A survey on the identification ability of vocational college nursing teachers for students’ AI tool misuse in subjective questions

HTML阅读

PDF下载

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 3

参考文献 19

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	夏立平. “新双高”背景下高职护理教育的现实困境与“五金”建设路径探析[J]. 中华护理教育, 2026, 23(9): 1029-1035.
[2]	张先庚, 邓小利, 梁小利, 郑爽, 张文, 王红艳, 徐飞. 人工智能赋能高职护理产教融合高质量发展的创新路径思考与实践[J]. 中华护理教育, 2026, 23(9): 1036-1040.
[3]	熊薇, 李习平. 健康中国背景下“新双高”护理专业群建设逻辑与实践[J]. 中华护理教育, 2026, 23(9): 1041-1045.
[4]	刘鑫鑫, 赵学荣, 覃芹丹, 范晶晶, 王梦瑶, 李芳, 胡阳, 颜萍. 本科外科护理学智慧课程建设及教学成效研究[J]. 中华护理教育, 2026, 23(9): 1065-1072.
[5]	张立雪, 杨战利, 金龙. 知识图谱联合人工智能助教在护理专业生理学课程教学中的应用研究[J]. 中华护理教育, 2026, 23(9): 1073-1079.
[6]	葛宾倩, 盛楠, 吕涵, 虞晓漪, 张慧, 沈娟. 基于建构主义的叙事教学在高职护理专业学生医学人文素养培养中的实践与成效[J]. 中华护理教育, 2026, 23(9): 1080-1086.
[7]	于万慧, 孙海娅, 刘娅, 张雪辉, 张莉莉, 陈玉荷, 吕淑棋, 刘佳敏, 刘冰. 护理专业本科生生成式人工智能素养现状及影响因素研究[J]. 中华护理教育, 2026, 23(9): 1086-1092.
[8]	于子莹, 王晓梅, 刘盼, 孟庆慧. 人工智能驱动的硕士研究生护理理论课程教学设计与实践研究[J]. 中华护理教育, 2026, 23(8): 938-943.
[9]	武圣君, 张银玲, 画妍, 柳琴, 刘旭峰. 人工智能辅助案例教学在本科护理心理学课程中的应用研究[J]. 中华护理教育, 2026, 23(8): 944-950.
[10]	王文颖, 袁佩佩, 潘圆圆, 赵雪旸. 人工智能焦虑量表在护理专业学生中应用的信效度检验[J]. 中华护理教育, 2026, 23(8): 964-970.
[11]	张聪, 林汉, 沈月娥, 刘晶晶, 孙若晗, 陈慧楠, 崔莹. “纵横整合·循阶环升”医学教学创新实践[J]. 中华护理教育, 2026, 23(7): 773-777.
[12]	高云, 苏茜, 杜志敏, 周英, 唐秋碧, 廖晓艳. 人工智能赋能护理心理学课程思政教学改革与实践[J]. 中华护理教育, 2026, 23(7): 778-783.
[13]	周莉, 孙圣博, 张松灵, 朴美花, 伦汪洋, 周越洋. 新医科背景下妇产科学课程教学创新实践及启示[J]. 中华护理教育, 2026, 23(7): 784-789.
[14]	谌绍林, 曾芬莲, 陈硕贞, 邵星, 缪小菊, 李金, 杨莉, 宋凌霞, 邓仁丽. “认知链-决策链-价值链”三链协同的外科护理学教学改革与实践[J]. 中华护理教育, 2026, 23(7): 790-796.
[15]	王妙丽, 黄重梅, 肖霖, 陈琦蓉, 张浩杰, 谭茗慧, 陶宏亮, 刘伟, 丁金锋. 多智能体协作的护理问诊教学小程序构建与评价研究[J]. 中华护理教育, 2026, 23(7): 814-821.