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Article(id=1322929616389812490, tenantId=1146029695717560320, journalId=1220038315760373764, issueId=1322929547464815391, articleNumber=null, orderNo=null, doi=10.12102/j.issn.1009-6493.2026.14.005, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1758211200000, receivedDateStr=2025-09-19, revisedDate=1781107200000, revisedDateStr=2026-06-11, acceptedDate=null, acceptedDateStr=null, onlineDate=1793340599390, onlineDateStr=2026-10-30, pubDate=1784908800000, pubDateStr=2026-07-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793340599390, onlineIssueDateStr=2026-10-30, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793340599390, creator=13701087609, updateTime=1793340599390, updator=13701087609, issue=Issue{id=1322929547464815391, tenantId=1146029695717560320, journalId=1220038315760373764, year='2026', volume='40', issue='14', pageStart='2385', pageEnd='2576', issueExtLink='null', onlineDate='null', pubDate='1784908800000', pubDateStr='2026-07-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793340582958, creator='13701087609', updateTime=1793343111003, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322940150912790807, tenantId=1146029695717560320, journalId=1220038315760373764, issueId=1322929547464815391, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322940150912790808, tenantId=1146029695717560320, journalId=1220038315760373764, issueId=1322929547464815391, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=2423, endPage=2429, ext={EN=ArticleExt(id=1322929616649859339, articleId=1322929616389812490, tenantId=1146029695717560320, journalId=1220038315760373764, language=EN, title=The influence of nursing undergraduates' artificial intelligence readiness on their use intention and path of action, columnId=1220689484526572183, journalTitle=Chinese Nursing Research, columnName=Original Articles, runingTitle=null, highlight=null, articleAbstract=
Objective

To investigate nursing undergraduates' readiness and intention to use artificial intelligence,and to elucidate the path of action through which artificial intelligence readiness influences their intention to use artificial intelligence based on the technology acceptance model.

Methods

Using convenience sampling,460 nursing undergraduates from five universities in Shandong,Henan,and Sichuan provinces were selected from March to July 2025.Participants were surveyed using a general information questionnaire,the Artificial Intelligence Technology Acceptance Scale,and the Medical Artificial Intelligence Readiness Scale for Medical Students.

Results

Among the 460 nursing undergraduates,the mean item score for artificial intelligence use intention was 5.53±1.13.Mean item scores for dimensions of the Medical Artificial Intelligence Readiness Scale for Medical Students,from lowest to highest,were cognition 2.85±0.79,ability 3.32±0.75,vision 3.54±0.72,and ethics 3.85±0.75.Path analysis results showed that cognition,ability,and ethics indirectly influenced artificial intelligence use intention through the mediating effect of perceived usefulness.Furthermore,cognition,ability,and vision indirectly influenced artificial intelligence use intention through the chain-mediating effects of perceived ease of use and perceived usefulness.

Conclusions

Nursing undergraduates' intention to use artificial intelligence is moderately high,but considerable room for improvement remained.Nursing educators should strengthen training in artificial intelligence knowledge and application skills for nursing undergraduates,while simultaneously fostering their forward-looking vision and awareness of ethics.These measures may enhance nursing undergraduates' artificial intelligence readiness,improve their perceived ease of use and perceived usefulness of artifical intelligence,and consequently increase their intention to use artificial intelligence.

, authors=Chaoqun LIANG, Huaxue ZHANG, Xiuzhen FAN, authorsList=Chaoqun LIANG, Huaxue ZHANG, Xiuzhen FAN, authorCompany=null, correspAuthors=Xiuzhen FAN, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1322929618763788558, articleId=1322929616389812490, tenantId=1146029695717560320, journalId=1220038315760373764, language=CN, title=护理本科生人工智能准备度对其使用意愿的影响及作用路径, columnId=1220689484681761433, journalTitle=护理研究, columnName=科研论著, runingTitle=null, highlight=null, articleAbstract=
目的

调查护理本科生对人工智能的准备度和使用意愿，并基于技术接受模型阐明护理本科生人工智能准备度对其使用意愿的作用路径。

方法

采用方便抽样法，于2025年3月—7月选取山东省、河南省、四川省5所高校的460名护理本科生为研究对象。采用一般资料问卷、人工智能技术接受量表、医学生人工智能准备度量表对护理本科生进行调查。

结果

460名护理本科生人工智能使用意愿条目均分为（5.53±1.13）分，医学生人工智能准备度量表各维度条目均分由低到高依次为认知理解［（2.85±0.79）分］、应用能力［（3.32±0.75）分］、技术洞察［（3.54±0.72）分］、伦理规范［（3.85±0.75）分］。路径分析结果显示，护理本科生的认知理解、应用能力、伦理规范可通过感知有用性的中介效应间接影响人工智能使用意愿，认知理解、应用能力和技术洞察可通过感知易用性和感知有用性的链式中介效应间接影响人工智能使用意愿。

结论

护理本科生的人工智能使用意愿处于中等偏高水平，但仍有较大提升空间。护理教育者应加强对护理本科生进行人工智能知识与应用能力培训，同时培养其前瞻性的技术洞察能力和伦理规范意识，以提升护理本科生的人工智能准备度，增强护理本科生对人工智能的感知易用性和感知有用性，进而提升其人工智能使用意愿。

, authors=梁超群, 张华雪, 范秀珍*, *, authorsList=梁超群, 张华雪, 范秀珍, authorCompany=null, correspAuthors=范秀珍, authorNote=

梁超群，硕士研究生在读

, correspAuthorsNote=
范秀珍，E-mail:
FAN Xiuzhen, E-mail:
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tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322929616389812490, doi=null, pmid=null, pmcid=null, year=2024, volume=141, issue=null, pageStart=106321, pageEnd=null, url=null, language=null, rfNumber=31, rfOrder=30, authorNames=Luo C, Mao B, Wu Y, journalName=Nurse Educ Today, refType=null, unstructuredReference=Luo C,Mao B,Wu Y,et al.The research hotspots and theme trends of artificial intelligence in nurse education:a bibliometric analysis from 1994 to 2023[J].Nurse Educ Today,2024,141:106321., articleTitle=The research hotspots and theme trends of artificial intelligence in nurse education:a bibliometric analysis from 1994 to 2023, refAbstract=null)], funds=null, companyList=[AuthorCompany(id=1322929619065778447, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322929616389812490, xref=1, ext=[AuthorCompanyExt(id=1322929619078361360, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322929616389812490, companyId=1322929619065778447, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1School of Nursing and Rehabilitation, Shandong University, Shandong 250012 China), AuthorCompanyExt(id=1322929619103527185, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322929616389812490, companyId=1322929619065778447, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1山东大学护理与康复学院，山东 250012)])], figs=[ArticleFig(id=1322929621267788080, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322929616389812490, language=EN, label=Figure 1, caption=Structural equation model of the path of action from nursing undergraduates' AI readiness to AI use intention, figureFileSmall=TfJr+XXKKFwuioCE03w+FA==, figureFileBig=q+wDNU9GPXhXmO39CtT82w==, tableContent=null), ArticleFig(id=1322929621360062769, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322929616389812490, language=CN, label=图1, caption=护理本科生AI准备度对使用意愿作用路径的结构方程模型, figureFileSmall=TfJr+XXKKFwuioCE03w+FA==, figureFileBig=q+wDNU9GPXhXmO39CtT82w==, tableContent=null), ArticleFig(id=1322929623163613490, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322929616389812490, language=EN, label=Table 1, caption=

General characteristics of nursing undergraduates（n=460）

, figureFileSmall=null, figureFileBig=null, tableContent=
项目分类人数项目分类人数
性别男83使用过AI应用程序或技术是407
女377否53
年级一年级132接受过AI相关培训是106
二年级71否354
三年级119就读学校省属高校302
四年级138部属高校158
家庭居住地城镇311
农村149
), ArticleFig(id=1322929623289442611, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322929616389812490, language=CN, label=表1, caption=

护理本科生一般资料（n=460）

, figureFileSmall=null, figureFileBig=null, tableContent=
项目分类人数项目分类人数
性别男83使用过AI应用程序或技术是407
女377否53
年级一年级132接受过AI相关培训是106
二年级71否354
三年级119就读学校省属高校302
四年级138部属高校158
家庭居住地城镇311
农村149
), ArticleFig(id=1322929623381717300, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322929616389812490, language=EN, label=Table 2, caption=

The scores of the Medical Artificial Intelligence Readiness Scale for Medical Students and the Artificial Intelligence Technology Acceptance Scale among nursing undergraduates(x±s)

, figureFileSmall=null, figureFileBig=null, tableContent=
维度条目数得分条目均分
医学生AI准备度量表 认知理解822.82±6.292.85±0.79
应用能力826.52±5.983.32±0.75
技术洞察310.63±2.173.54±0.72
伦理规范311.54±2.243.85±0.75
AI技术接受量表 感知易用性630.73±5.725.12±0.95
感知有用性631.96±5.685.33±0.95
使用意愿211.06±2.255.53±1.13
), ArticleFig(id=1322929623503352117, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322929616389812490, language=CN, label=表2, caption=

护理本科生的医学生AI准备度量表与AI技术接受量表得分（x±s）

, figureFileSmall=null, figureFileBig=null, tableContent=
维度条目数得分条目均分
医学生AI准备度量表 认知理解822.82±6.292.85±0.79
应用能力826.52±5.983.32±0.75
技术洞察310.63±2.173.54±0.72
伦理规范311.54±2.243.85±0.75
AI技术接受量表 感知易用性630.73±5.725.12±0.95
感知有用性631.96±5.685.33±0.95
使用意愿211.06±2.255.53±1.13
), ArticleFig(id=1322929623591432502, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322929616389812490, language=EN, label=Table 3, caption=

Pathways and effect analysis of AI readiness on AI use intention among nursing undergraduates（n=460）

, figureFileSmall=null, figureFileBig=null, tableContent=
效应路径标准化路径系数标准误95%CI
直接效应认知理解→感知易用性-0.2640.100[-0.460，-0.064]
认知理解→感知有用性-0.3020.078[-0.458，-0.155]
应用能力→感知易用性0.3720.128[0.079，0.611]
应用能力→感知有用性0.2860.097[0.107，0.484]
技术洞察→感知易用性0.2440.101[0.041，0.438]
技术洞察→感知有用性-0.0660.079[-0.223，0.090]
伦理规范→感知易用性0.0900.100[-0.117，0.282]
伦理规范→感知有用性0.1990.066[0.069，0.333]
感知易用性→感知有用性0.6900.046[0.592，0.776]
感知易用性→使用意愿-0.1010.096[-0.306，0.073]
感知有用性→使用意愿0.9450.089[0.776，1.128]
间接效应认知理解→感知易用性→使用意愿0.0270.030[-0.013，0.118]
应用能力→感知易用性→使用意愿-0.0370.041[-0.154，0.019]
技术洞察→感知易用性→使用意愿-0.0250.028[-0.107，0.012]
伦理规范→感知易用性→使用意愿-0.0090.016[-0.061，0.009]
认知理解→感知有用性→使用意愿-0.2850.076[-0.436，-0.142]
应用能力→感知有用性→使用意愿0.2700.090[0.098，0.454]
技术洞察→感知有用性→使用意愿-0.0620.075[-0.210，0.085]
伦理规范→感知有用性→使用意愿0.1880.067[0.064，0.331]
感知易用性→感知有用性→使用意愿0.6520.088[0.510，0.850]
认知理解→感知易用性→感知有用性→使用意愿-0.1720.072[-0.330，-0.045]
应用能力→感知易用性→感知有用性→使用意愿0.2420.094[0.065，0.444]
技术洞察→感知易用性→感知有用性→使用意愿0.1590.073[0.032，0.314]
伦理规范→感知易用性→感知有用性→使用意愿0.0590.066[-0.081，0.184]
), ArticleFig(id=1322929623692095799, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322929616389812490, language=CN, label=表3, caption=

护理本科生AI准备度对使用意愿的作用路径及效应分析（n=460）

, figureFileSmall=null, figureFileBig=null, tableContent=
效应路径标准化路径系数标准误95%CI
直接效应认知理解→感知易用性-0.2640.100[-0.460，-0.064]
认知理解→感知有用性-0.3020.078[-0.458，-0.155]
应用能力→感知易用性0.3720.128[0.079，0.611]
应用能力→感知有用性0.2860.097[0.107，0.484]
技术洞察→感知易用性0.2440.101[0.041，0.438]
技术洞察→感知有用性-0.0660.079[-0.223，0.090]
伦理规范→感知易用性0.0900.100[-0.117，0.282]
伦理规范→感知有用性0.1990.066[0.069，0.333]
感知易用性→感知有用性0.6900.046[0.592，0.776]
感知易用性→使用意愿-0.1010.096[-0.306，0.073]
感知有用性→使用意愿0.9450.089[0.776，1.128]
间接效应认知理解→感知易用性→使用意愿0.0270.030[-0.013，0.118]
应用能力→感知易用性→使用意愿-0.0370.041[-0.154，0.019]
技术洞察→感知易用性→使用意愿-0.0250.028[-0.107，0.012]
伦理规范→感知易用性→使用意愿-0.0090.016[-0.061，0.009]
认知理解→感知有用性→使用意愿-0.2850.076[-0.436，-0.142]
应用能力→感知有用性→使用意愿0.2700.090[0.098，0.454]
技术洞察→感知有用性→使用意愿-0.0620.075[-0.210，0.085]
伦理规范→感知有用性→使用意愿0.1880.067[0.064，0.331]
感知易用性→感知有用性→使用意愿0.6520.088[0.510，0.850]
认知理解→感知易用性→感知有用性→使用意愿-0.1720.072[-0.330，-0.045]
应用能力→感知易用性→感知有用性→使用意愿0.2420.094[0.065，0.444]
技术洞察→感知易用性→感知有用性→使用意愿0.1590.073[0.032，0.314]
伦理规范→感知易用性→感知有用性→使用意愿0.0590.066[-0.081，0.184]
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护理本科生人工智能准备度对其使用意愿的影响及作用路径
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梁超群 , 张华雪 , 范秀珍 *, *
护理研究 | 科研论著 2026,40(14): 2423-2429
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护理研究 |科研论著 2026 , 40 (14) : 2423 -2429
护理本科生人工智能准备度对其使用意愿的影响及作用路径
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梁超群, 张华雪, 范秀珍*, *
作者信息
  • 1山东大学护理与康复学院，山东 250012
通讯作者:
范秀珍，E-mail:
FAN Xiuzhen, E-mail:
作者简介:

梁超群，硕士研究生在读

The influence of nursing undergraduates' artificial intelligence readiness on their use intention and path of action
Chaoqun LIANG, Huaxue ZHANG, Xiuzhen FAN
Affiliations
  • 1School of Nursing and Rehabilitation, Shandong University, Shandong 250012 China
出版时间: 2026-07-25 doi: 10.12102/j.issn.1009-6493.2026.14.005
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摘要
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目的

调查护理本科生对人工智能的准备度和使用意愿，并基于技术接受模型阐明护理本科生人工智能准备度对其使用意愿的作用路径。

方法

采用方便抽样法，于2025年3月—7月选取山东省、河南省、四川省5所高校的460名护理本科生为研究对象。采用一般资料问卷、人工智能技术接受量表、医学生人工智能准备度量表对护理本科生进行调查。

结果

460名护理本科生人工智能使用意愿条目均分为（5.53±1.13）分，医学生人工智能准备度量表各维度条目均分由低到高依次为认知理解［（2.85±0.79）分］、应用能力［（3.32±0.75）分］、技术洞察［（3.54±0.72）分］、伦理规范［（3.85±0.75）分］。路径分析结果显示，护理本科生的认知理解、应用能力、伦理规范可通过感知有用性的中介效应间接影响人工智能使用意愿，认知理解、应用能力和技术洞察可通过感知易用性和感知有用性的链式中介效应间接影响人工智能使用意愿。

结论

护理本科生的人工智能使用意愿处于中等偏高水平，但仍有较大提升空间。护理教育者应加强对护理本科生进行人工智能知识与应用能力培训，同时培养其前瞻性的技术洞察能力和伦理规范意识，以提升护理本科生的人工智能准备度，增强护理本科生对人工智能的感知易用性和感知有用性，进而提升其人工智能使用意愿。

护理本科生  /  人工智能  /  准备度  /  使用意愿  /  结构方程模型  /  影响因素  /  路径分析
Objective

To investigate nursing undergraduates' readiness and intention to use artificial intelligence,and to elucidate the path of action through which artificial intelligence readiness influences their intention to use artificial intelligence based on the technology acceptance model.

Methods

Using convenience sampling,460 nursing undergraduates from five universities in Shandong,Henan,and Sichuan provinces were selected from March to July 2025.Participants were surveyed using a general information questionnaire,the Artificial Intelligence Technology Acceptance Scale,and the Medical Artificial Intelligence Readiness Scale for Medical Students.

Results

Among the 460 nursing undergraduates,the mean item score for artificial intelligence use intention was 5.53±1.13.Mean item scores for dimensions of the Medical Artificial Intelligence Readiness Scale for Medical Students,from lowest to highest,were cognition 2.85±0.79,ability 3.32±0.75,vision 3.54±0.72,and ethics 3.85±0.75.Path analysis results showed that cognition,ability,and ethics indirectly influenced artificial intelligence use intention through the mediating effect of perceived usefulness.Furthermore,cognition,ability,and vision indirectly influenced artificial intelligence use intention through the chain-mediating effects of perceived ease of use and perceived usefulness.

Conclusions

Nursing undergraduates' intention to use artificial intelligence is moderately high,but considerable room for improvement remained.Nursing educators should strengthen training in artificial intelligence knowledge and application skills for nursing undergraduates,while simultaneously fostering their forward-looking vision and awareness of ethics.These measures may enhance nursing undergraduates' artificial intelligence readiness,improve their perceived ease of use and perceived usefulness of artifical intelligence,and consequently increase their intention to use artificial intelligence.

nursing undergraduate students  /  artificial intelligence  /  readiness  /  intention to use  /  structural equation modeling  /  influencing factors  /  pathway analysis
梁超群, 张华雪, 范秀珍. 护理本科生人工智能准备度对其使用意愿的影响及作用路径. 护理研究, 2026 , 40 (14) : 2423 -2429 . DOI: 10.12102/j.issn.1009-6493.2026.14.005
Chaoqun LIANG, Huaxue ZHANG, Xiuzhen FAN. The influence of nursing undergraduates' artificial intelligence readiness on their use intention and path of action[J]. Chinese Nursing Research, 2026 , 40 (14) : 2423 -2429 . DOI: 10.12102/j.issn.1009-6493.2026.14.005
正文
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人工智能（artificial intelligence，AI）是计算机科学的一个分支，通过模拟人类的思维逻辑执行学习、推理和解决问题等智能化的任务[1]。目前，AI在护理实践和教育领域的应用日益广泛。在护理实践领域，AI已用于移动健康、智能医疗机器人以及健康数据管理等[2]，可辅助护士进行病人分诊、护理诊断和病例记录等[3]，有利于提高护士的技能水平、提升护理质量[4-5]。在护理教育领域，AI可依据学生的学习习惯和能力制定个性化的学习计划，提供实时学习反馈，应用智能聊天机器人、虚拟仿真技术教学等[6]，有利于培养学生自主学习能力，帮助学生更好地掌握护理知识和技能[6]。因此，提升护理本科生的AI准备度和使用意愿对于推动智慧护理实践和教育至关重要。技术接受模型（technology acceptance model，TAM）由Davis[7]提出，该模型提示多种前置因素可同时影响感知易用性和感知有用性，感知易用性和感知有用性是影响用户对新技术使用意愿的核心因素，其中感知易用性直接影响感知有用性。已有研究结果显示，技术准备度可影响感知易用性和感知有用性，高技术准备度用户更倾向于认为新技术有用且易于掌握[8]。护理本科生AI准备度是学生在知识、技能和态度上的一种准备状态，包括认知理解、应用能力、技术洞察和伦理规范4个维度。AI准备度能够帮助学生将专业知识与AI应用相结合，有效地为病人提供预防、诊断、治疗和康复等医疗护理服务[9]。本研究基于TAM，将护理本科生AI准备度的4个维度作为前置因素，构建结构方程模型，探究其通过感知易用性和感知有用性影响AI使用意愿的作用路径，以期制定干预策略以提升护理本科生AI使用意愿，推动AI技术在护理领域的持续、有效应用。
1　对象与方法
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1.1　研究对象
采用方便抽样法，于2025年3月—7月选取山东省、河南省、四川省5所高校的护理本科生为研究对象。纳入标准：全日制在读护理本科生；知情同意并自愿参与本研究。排除标准：休学。根据结构方程模型估算要求，样本量不低于200人[10]，在此基础上，考虑20%的缺失率，本研究所需样本量至少为250人。本研究已经山东大学护理与康复学院伦理委员会批准（编号：2025-R-075）。
1.2　调查工具
1.2.1　一般资料问卷
由研究者自行编制，包括性别、年龄、年级、家庭居住地、是否使用过AI应用程序或技术、是否接受过AI相关培训、就读学校。
1.2.2　AI技术接受量表
Venkatesh等[11]基于TAM开发技术接受模型量表，本研究团队根据研究目的对技术接受模型量表进行汉化修订形成AI技术接受量表。1）翻译与回译：由1名有海外学习经历的护理教育专业教授和1名护理学博士研究生独立将原量表翻译成中文量表，经课题组讨论后整合形成量表中文版初稿；随后由另外2名未接触过原量表的研究者将量表中文版初稿回译成英文；课题组对比原量表与回译量表，针对语义差异进行反复讨论与修改，确保中文版量表在概念和语义上与原量表保持一致。2）文化调适：基于我国护理教育背景及语言习惯对中文版量表措辞进行润色，形成预调查量表。3）预调查：采用便利抽样法，邀请山东大学护理与康复学院的15名护理本科生进行预调查，了解学生对条目的理解程度及作答感受，确认量表条目表述清晰，语句通顺，含义明确，易于理解。纳入标准：全日制在读护理本科生；知情同意并自愿参与本研究。排除标准：休学。最终形成的AI技术接受量表包括3个维度，共14个条目，其中感知易用性维度包含6个条目，感知有用性维度包含6个条目，使用意愿维度包含2个条目，各条目均采用Likert 7级评分法评分，“非常不同意”计1分，“非常同意”计7分，维度得分越高表示该维度水平越高。本研究中量表各维度的Cronbach's α系数为0.877~0.898，组合信度（CR）为0.872~0.899，表明各维度的内部一致性良好；量表各维度平均方差抽取量（AVE）为0.534~0.816，表明量表收敛效度合理；量表各维度AVE平方根均大于其与其他维度的相关系数，表明量表具有良好的区分效度[12]。
1.2.3　医学生AI准备度量表（Medical Artificial Intelligence Readiness Scale for Medical Students，MAIRS-MS）
医学生AI准备度量表由Karaca等[9]于2021年编制，包含4个维度，共22个条目，其中，认知理解维度包含8个条目，用于测量医学生对AI术语和数据科学的认知程度；应用能力维度包含8个条目，用于测量医学生选择合适的AI工具、正确使用AI工具以及向病人解释AI工具作用的能力；技术洞察维度包含3个条目，用于测量医学生解释AI在医学中的优势和局限性以及预测AI未来的机遇和挑战方面的能力；伦理规范维度包含3个条目，用于测量医学生在使用AI时遵守法律与伦理规范的能力。各条目均采用Likert 5级评分法，“完全不同意”计1分，“完全同意”计5分，维度得分越高表示该维度水平越高。本研究团队根据研究目的对医学生AI准备度量表进行汉化修订形成中文版医学生AI准备度量表，过程同AI技术接受量表。结果显示，本研究中量表各维度的Cronbach's α系数为0.757~0.890，CR为0.768~0.890，表明各维度的内部一致性良好；量表中除认知理解维度的AVE为0.493外，其余维度的AVE均>0.5（为0.504~0.603），已有研究显示，当CR>0.6时，AVE接近0.50也可接受[13]；量表各维度AVE平方根均大于其与其他维度的相关系数，表明量表具有良好的区分效度。
1.3　资料收集方法
本研究通过“问卷星”在线平台进行问卷调查。由2名经过统一培训的调研员向护理本科生发放问卷链接。问卷首页详细说明本次调查的目的、意义以及内容等。为保证问卷作答质量，限制每人填写1次，剔除作答时间<3 min或规律作答的问卷。共发放问卷500份，剔除无效问卷40份，最终回收有效问卷460份，问卷有效回收率为92%。
1.4　统计学方法
采用SPSS 26.0进行数据整理和分析。采用Q-Q图分析定量资料分布状况，呈正态分布时采用均数±标准差（x±s）表示，定性资料采用频数表示。采用AMOS 24.0软件构建结构方程模型，采用Bootstrap法检验中介效应。以P<0.05为差异有统计学意义。
2　结果
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2.1　护理本科生一般资料
460名护理本科生年龄为18~24（20.59±1.48）岁，其余一般资料见表1。
2.2　护理本科生的医学生AI准备度量表与AI技术接受量表得分（见表2）
2.3　护理本科生AI准备度对使用意愿的作用路径
2.3.1　模型的构建
根据修正指数调整后的模型拟合度较好，卡方自由度比值为2.642，拟合优度指数为0.834，增值拟合指数为0.903，比较拟合指数为0.903，近似误差均方根为0.060。结构方程模型见图1。
2.3.2　模型中各变量的作用路径分析
护理本科生的认知理解和应用能力直接影响感知易用性和感知有用性，技术洞察直接影响感知易用性，伦理规范和感知易用性直接影响感知有用性；感知有用性直接影响使用意愿。护理本科生的认知理解、应用能力、伦理规范和感知易用性可通过感知有用性的中介效应间接影响使用意愿，认知理解、应用能力和技术洞察可通过感知易用性和感知有用性的链式中介效应间接影响使用意愿。变量间的路径及其效应见表3。
3　讨论
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3.1　护理本科生AI使用意愿与准备度现状
本研究结果显示，护理本科生AI技术接受量表的使用意愿得分处于中等偏高水平，略高于Al Omari等[14]对护理本科生的调查结果。已有研究结果显示，学生普遍认识到了AI对于护理教育和护理实践的重要性[5-6]，因而对AI持有积极态度和使用意愿，但由于缺乏系统的AI教育，学生的AI知识和使用经验不足[15]，且对AI的伦理问题存在一定的担忧[16]，仍有部分学生对AI的使用持不确定态度。本研究结果也显示，护理本科生的医学生AI准备度量表中认知理解维度和应用能力维度得分相对较低，与Ziapour等[17]调查结果相似。原因可能为：1）在当前护理本科教育的课程设置中，AI相关内容较少且滞后于技术发展，缺乏系统的AI教育课程体系[18]。2）学生对AI技术的接触多停留在学习应用表层，如使用生成式AI工具辅助完成作业或查阅资料[19]，对其工作原理缺乏深入理解，且对其临床应用的进一步探究较少。3）部分学生担心AI可能削弱护理的人文属性[16]，也会一定程度上导致其对AI的重视度和准备度降低。提示护理院校应重视对护理本科生AI知识的提升和技术应用能力的培养，以提高其AI准备度。护理教育者应顺应AI技术发展趋势，及时将AI前沿纳入教学内容；加强与临床的融合与协作，通过真实或模拟的AI临床应用情境，增强学生的技术使用信心和实践感知，从而不断提升护理本科生的AI准备度，进而提升AI使用意愿。
3.2　护理本科生认知理解对AI使用意愿的影响
本研究结果显示，护理本科生的认知理解可对感知易用性和感知有用性产生直接影响，其也可通过感知有用性的中介作用对AI使用意愿产生间接影响，其还可通过感知易用性和感知有用性的链式中介作用对AI使用意愿产生间接影响，与以往研究结果[20]相似。但在本研究中，护理本科生认知理解对AI使用意愿的影响效应值均为负值，与已有研究结果[20]不同，提示学生对AI相关知识掌握越不足，其感知易用性和感知有用性越强，使用意愿越强。这一现象可能与达克效应[21]及当前生成式AI（如ChatGPT）的技术特性[22]有关。本研究中，护理本科生AI准备度中的认知理解水平相对较低，容易高估AI的潜力和便利性，在评估感知易用性和感知有用性时会给予更积极的评价；此外，随着ChatGPT等大语言模型整合入护理教育[22]，简易的操作交互界面极大地降低了技术门槛，学生可以应用AI完成学术写作、照护模拟和数据建模等学习任务。部分认知水平较低的学生容易被AI的“表面易用性”迷惑，误认为AI工具易于掌握且便利实用，从而产生过高的“虚假”感知有用性。但认知理解水平较低的学生往往对AI的潜在风险了解不足，容易造成AI使用不当[23-24]。提示护理教育者应将AI基础知识纳入护理课程[25]，一方面可通过结构化AI课程帮助学生建立关于AI的认知框架，避免因知识缺乏对AI产生过高评价；另一方面可通过将AI生成的内容融入案例分析和辩证讨论，引导学生在形成积极态度的同时，保持对AI应用的理性认识和评判性思维，从而产生更科学、持久的AI使用意愿。此外，护理教育者还应不断更新自己的AI相关知识，理解和评估不同AI平台的特点、优势与局限性，以选择最适合教育教学的AI工具。
3.3　护理本科生应用能力对AI使用意愿的影响
本研究结果显示，护理本科生的应用能力可对感知易用性和感知有用性产生直接影响，其也可通过感知有用性的中介作用对AI使用意愿产生间接影响，其还可通过感知易用性和感知有用性的链式中介作用对AI使用意愿产生间接影响，与已有研究结果[26]相似。AI应用能力较强的学生能更熟练地使用AI工具（感知易用性），也易于在实践中体会到AI带来的便利和效益（感知有用性），进而提高使用意愿。已有研究表明，当学生认为自己技术熟练时会更愿意将该技术融入学习中[27]，对于AI技术理解较深入的学生，在认识到其潜在价值后，会更愿意将其整合到学习和未来实践中。TAM中用户的技能水平和使用经验会影响其对新技术易用性和有用性的感知，而感知易用性和感知有用性作为TAM的核心变量，能够有效解释并预测个体的技术使用意愿[7]。因此，培养护理本科生有效运用AI的必要技能，对于推动护理创新、实现以病人为中心的照护模式以及在人工智能驱动的医疗健康时代促进护理事业发展具有重要意义[27]。护理教育者在开发AI相关课程时应着重提升学生的应用能力，如增加AI实践操作、案例训练等内容，帮助学生切身体会AI对学习与实践的价值，提升其AI操作熟练度，进而增强其持续使用AI的意愿。
3.4　护理本科生技术洞察对AI使用意愿的影响
本研究结果显示，护理本科生的技术洞察可对感知易用性产生直接影响，其也可通过感知易用性和感知有用性的链式中介作用对AI使用意愿产生间接影响。技术洞察主要测量学生对AI技术优缺点的认识以及预测AI未来机遇和挑战的能力[9]，具备较高技术洞察水平的学生，能够更全面、批判性地看待AI现状及未来发展趋势，因而在认知层面对AI的技术难度评估相对较低，易用性感知较高[28]。同时，当学生认为AI易于使用，并在使用过程中逐步体验到AI能够解决实际问题、提高学习/工作效率等价值时[29]，便会提升对AI的感知有用性水平，最终提升使用意愿。提示护理教育者不仅需要培养学生的AI技术操作技能，还需要重视其技术洞察能力。护理教育者可通过举办AI主题相关研讨会或学术沙龙，向学生介绍AI的优势、局限性以及未来发展趋势，引导他们思考AI在护理领域的发展前景。这不仅有利于帮助学生在AI使用方面降低技术门槛，还有利于激励学生主动探索AI在护理领域的应用，促进AI感知易用性向感知有用性转化，进一步提升其AI使用意愿。
3.5　护理本科生伦理规范对AI使用意愿的影响
本研究结果显示，护理本科生的伦理规范可对感知有用性产生直接影响，其也可通过感知有用性的中介作用对AI使用意愿产生间接影响。已有研究表明，信任是影响AI使用的关键因素，而伦理相关因素是影响用户对AI信任的核心前因变量[30]。如果学生可以认识到AI技术应用严格遵循伦理规范，如保障用户隐私、避免算法偏见等，他们就会在临床或学习环境中将其视为“放心使用”的辅助工具，提升对AI的信任，认可AI的价值，从而增强AI感知有用性，最终提升使用意愿。提示护理教育者在传授AI知识和技能过程中，还应履行监督与引导职责，重视对护理本科生的AI伦理教育[31]；通过分析数据隐私泄露、学术不端等AI伦理案例以及研读AI使用伦理指南等，提升学生使用AI的风险防范意识，培养既熟悉AI使用方法，又具有高度责任感和伦理意识的护理专业人才。
4　小结
收起
本研究基于TAM，探讨了护理本科生AI准备度对使用意愿的影响及作用路径。研究结果表明，护理本科生AI使用意愿处于中等偏高水平，但还有一定提升空间；其AI准备度中的认知理解和应用能力水平相对较低。护理本科生AI准备度中的认知理解、应用能力和伦理规范可通过感知有用性的中介作用间接影响AI使用意愿；同时，AI准备度中的认知理解、应用能力和技术洞察可通过感知易用性和感知有用性的链式中介作用间接影响AI使用意愿。提示护理教育者应关注学生AI准备度对使用意愿的影响及作用路径，从而制定针对性的干预措施。将AI知识和应用方法纳入护理教育课程体系，有利于提高学生的AI认知理解、应用能力、技术洞察和伦理规范水平，进而有利于提升其AI准备度和使用意愿。但本研究也存在一定局限性，如本研究为横断面设计，无法推断因果关系；虽然以TAM为研究框架，但未能全面涵盖可能影响AI使用意愿的因素。未来需开展纵向研究，阐明AI准备度、感知易用性、感知有用性与使用意愿间的因果关系。同时，可进一步探究AI使用意愿的潜在影响机制，为护理教育实践提供更加全面、可靠的依据。

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2026年第40卷第14期
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doi: 10.12102/j.issn.1009-6493.2026.14.005
  • 接收时间：2025-09-19
  • 首发时间：2026-10-30
  • 出版时间：2026-07-25
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  • 收稿日期：2025-09-19
  • 修回日期：2026-06-11
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    1山东大学护理与康复学院，山东 250012

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范秀珍，E-mail:
FAN Xiuzhen, E-mail:
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
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