Survey on Current Status and Analysis of Influencing Factors of AI Self-Efficacy, AI Anxiety Level, and AI Acceptance in the Nurse Population: A Cross-Sectional Study

Survey on Current Status and Analysis of Influencing Factors of AI Self-Efficacy, AI Anxiety Level, and AI Acceptance in the Nurse Population: A Cross-Sectional Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Survey on Current Status and Analysis of Influencing Factors of AI Self-Efficacy, AI Anxiety Level, and AI Acceptance in the Nurse Population: A Cross-Sectional Study Pinyue Tao, Lilin Qiu, Shuyao Li, Yumei Liang, Shuyu Lu, Dongna Zhou, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8126097/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Objective : This study investigates the current status of self-efficacy, anxiety levels, and acceptance of artificial intelligence among nursing professionals, analyzes influencing factors, and provides a reference framework for enhancing nurses’ AI tool application capabilities and promoting the effective use of AI in clinical nursing practice. Method : Using convenience sampling, 240 clinical nurses in Guangxi Zhuang Autonomous Region were selected as study subjects from May to August 2025. Questionnaire surveys were conducted using a general information questionnaire, the AI Self-Efficacy Scale, the Artificial Intelligence Anxiety Scale (AIAS), and the Artificial Intelligence Acceptance Scale. Data analysis was performed using SPSS 26.0 statistical software, including descriptive statistics, t-tests, analysis of variance (ANOVA), Pearson correlation analysis, and multiple linear regression analysis. Results :The frequency of AI tool usage and AI-related research experience are statistically significant factors influencing nurses’ AI self-efficacy ( P < 0.05). AI-related research experience is the primary factor affecting AI anxiety ( P < 0.05). AI training experience is the primary factor influencing AI acceptance ( P 0, P < 0.01; r = 0.309, P < 0.01); AI self-efficacy and AI acceptance demonstrated a moderate positive correlation (r = 0.570, P < 0.01). Conclusion : The current nursing population exhibits a psychological state characterized by low self-efficacy, high pre-anxiety, and moderate acceptance. It is recommended to adopt a dual-pronged strategy of “empowerment” and “emotional support” to promote the application of AI tools in nursing. This approach aims to provide evidence and actionable guidance for healthcare institutions on how to advance the intelligent transformation of nursing in a people-centered and scientifically grounded manner. Nurses AI self-efficacy AI anxiety levels AI acceptance Influencing factors Full Text Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 29 Dec, 2025 Reviews received at journal 22 Dec, 2025 Reviewers agreed at journal 22 Dec, 2025 Reviewers agreed at journal 19 Dec, 2025 Reviewers agreed at journal 16 Dec, 2025 Reviewers invited by journal 11 Dec, 2025 Editor invited by journal 19 Nov, 2025 Editor assigned by journal 18 Nov, 2025 Submission checks completed at journal 18 Nov, 2025 First submitted to journal 16 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8126097","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":561199665,"identity":"6fa76c59-7ace-4f9b-823f-0f9681581a7c","order_by":0,"name":"Pinyue Tao","email":"","orcid":"","institution":"Second Affiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Pinyue","middleName":"","lastName":"Tao","suffix":""},{"id":561199666,"identity":"28cc4d19-ddb6-47dd-8fce-019d47f2e7f0","order_by":1,"name":"Lilin Qiu","email":"","orcid":"","institution":"Second Affiliated Hospital of Guangxi 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Nursing","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Nurses, AI self-efficacy, AI anxiety levels, AI acceptance, Influencing factors","lastPublishedDoi":"10.21203/rs.3.rs-8126097/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8126097/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e: This study investigates the current status of self-efficacy, anxiety levels, and acceptance of artificial intelligence among nursing professionals, analyzes influencing factors, and provides a reference framework for enhancing nurses’ AI tool application capabilities and promoting the effective use of AI in clinical nursing practice.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod\u003c/strong\u003e: Using convenience sampling, 240 clinical nurses in Guangxi Zhuang Autonomous Region were selected as study subjects from May to August 2025. Questionnaire surveys were conducted using a general information questionnaire, the AI Self-Efficacy Scale, the Artificial Intelligence Anxiety Scale (AIAS), and the Artificial Intelligence Acceptance Scale. Data analysis was performed using SPSS 26.0 statistical software, including descriptive statistics, t-tests, analysis of variance (ANOVA), Pearson correlation analysis, and multiple linear regression analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e:The frequency of AI tool usage and AI-related research experience are statistically significant factors influencing nurses’ AI self-efficacy (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). AI-related research experience is the primary factor affecting AI anxiety (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). AI training experience is the primary factor influencing AI acceptance (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01). Furthermore, both nurses’ AI self-efficacy and AI acceptance showed weak positive correlations with AI anxiety (r = 0.343 \u0026gt; 0, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01; r = 0.309, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01); AI self-efficacy and AI acceptance demonstrated a moderate positive correlation (r = 0.570, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: The current nursing population exhibits a psychological state characterized by low self-efficacy, high pre-anxiety, and moderate acceptance. It is recommended to adopt a dual-pronged strategy of “empowerment” and “emotional support” to promote the application of AI tools in nursing. This approach aims to provide evidence and actionable guidance for healthcare institutions on how to advance the intelligent transformation of nursing in a people-centered and scientifically grounded manner.\u003c/p\u003e","manuscriptTitle":"Survey on Current Status and Analysis of Influencing Factors of AI Self-Efficacy, AI Anxiety Level, and AI Acceptance in the Nurse Population: A Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-17 11:53:06","doi":"10.21203/rs.3.rs-8126097/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-12-29T12:08:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-22T12:20:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"194200134082429226161480671448346687408","date":"2025-12-22T07:02:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"193414735943630300025632687987356607709","date":"2025-12-19T18:13:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"95604080447450179181403910894722228871","date":"2025-12-16T13:19:10+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-11T18:00:02+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-19T08:45:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-18T10:25:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-18T10:25:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nursing","date":"2025-11-16T08:31:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-nursing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurs","sideBox":"Learn more about [BMC Nursing](http://bmcnurs.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurs/default.aspx","title":"BMC Nursing","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3a1b3a78-0d5e-4250-a01a-c147b9333b5b","owner":[],"postedDate":"December 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-17T11:53:06+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-17 11:53:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8126097","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8126097","identity":"rs-8126097","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}