{"title":"探索护理专业学生对人工智能的态度和准备程度：横断面研究","authors":"Turgay Yalcinkaya , Eda Ergin , Sebnem Cinar Yucel","doi":"10.1016/j.teln.2024.07.008","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><p>Understanding nursing students' attitudes towards and readiness for artificial intelligence (AI) is crucial for the effective integration of AI into nursing education and practice. AI has the potential to enhance clinical decision-making and personalize patient care.</p></div><div><h3>Aim</h3><p>This study aimed to determine nursing students' attitudes towards and readiness for AI.</p></div><div><h3>Methods</h3><p>This was a cross-sectional descriptive study conducted at a nursing faculty in the west of Turkey and included 291 nursing students. Data were collected using the Individual Information Form, the General Attitudes towards Artificial Intelligence Scale (GAAIS), and the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS).</p></div><div><h3>Results</h3><p>The mean scores for Positive GAAIS, Negative GAAIS, and MAIRS-MS were 3.86 ± 0.62, 3.23 ± 0.82, and 76.93 ± 13.63, respectively. Fourth-year students scored significantly higher on the MAIRS-MS compared to second-year students (F = 3.750, p = 0.011). A positive correlation was found between MAIRS-MS and GAAIS scores (r = 0.330, p < 0.01).</p></div><div><h3>Conclusions</h3><p>The findings are anticipated to guide nursing faculties and academicians in incorporating AI into the curriculum.</p></div>","PeriodicalId":46287,"journal":{"name":"Teaching and Learning in Nursing","volume":"19 4","pages":"Pages e722-e728"},"PeriodicalIF":1.9000,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Exploring Nursing Students' Attitudes and Readiness for Artificial Intelligence: A Cross-Sectional Study\",\"authors\":\"Turgay Yalcinkaya , Eda Ergin , Sebnem Cinar Yucel\",\"doi\":\"10.1016/j.teln.2024.07.008\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><p>Understanding nursing students' attitudes towards and readiness for artificial intelligence (AI) is crucial for the effective integration of AI into nursing education and practice. AI has the potential to enhance clinical decision-making and personalize patient care.</p></div><div><h3>Aim</h3><p>This study aimed to determine nursing students' attitudes towards and readiness for AI.</p></div><div><h3>Methods</h3><p>This was a cross-sectional descriptive study conducted at a nursing faculty in the west of Turkey and included 291 nursing students. Data were collected using the Individual Information Form, the General Attitudes towards Artificial Intelligence Scale (GAAIS), and the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS).</p></div><div><h3>Results</h3><p>The mean scores for Positive GAAIS, Negative GAAIS, and MAIRS-MS were 3.86 ± 0.62, 3.23 ± 0.82, and 76.93 ± 13.63, respectively. Fourth-year students scored significantly higher on the MAIRS-MS compared to second-year students (F = 3.750, p = 0.011). A positive correlation was found between MAIRS-MS and GAAIS scores (r = 0.330, p < 0.01).</p></div><div><h3>Conclusions</h3><p>The findings are anticipated to guide nursing faculties and academicians in incorporating AI into the curriculum.</p></div>\",\"PeriodicalId\":46287,\"journal\":{\"name\":\"Teaching and Learning in Nursing\",\"volume\":\"19 4\",\"pages\":\"Pages e722-e728\"},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2024-08-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Teaching and Learning in Nursing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S155730872400146X\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"NURSING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Teaching and Learning in Nursing","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S155730872400146X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"NURSING","Score":null,"Total":0}
Exploring Nursing Students' Attitudes and Readiness for Artificial Intelligence: A Cross-Sectional Study
Background
Understanding nursing students' attitudes towards and readiness for artificial intelligence (AI) is crucial for the effective integration of AI into nursing education and practice. AI has the potential to enhance clinical decision-making and personalize patient care.
Aim
This study aimed to determine nursing students' attitudes towards and readiness for AI.
Methods
This was a cross-sectional descriptive study conducted at a nursing faculty in the west of Turkey and included 291 nursing students. Data were collected using the Individual Information Form, the General Attitudes towards Artificial Intelligence Scale (GAAIS), and the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS).
Results
The mean scores for Positive GAAIS, Negative GAAIS, and MAIRS-MS were 3.86 ± 0.62, 3.23 ± 0.82, and 76.93 ± 13.63, respectively. Fourth-year students scored significantly higher on the MAIRS-MS compared to second-year students (F = 3.750, p = 0.011). A positive correlation was found between MAIRS-MS and GAAIS scores (r = 0.330, p < 0.01).
Conclusions
The findings are anticipated to guide nursing faculties and academicians in incorporating AI into the curriculum.
期刊介绍：
Teaching and Learning in Nursing is the Official Journal of the National Organization of Associate Degree Nursing. The journal is dedicated to the advancement of Associate Degree Nursing education and practice, and promotes collaboration in charting the future of health care education and delivery. Topics include: - Managing Different Learning Styles - New Faculty Mentoring - Legal Issues - Research - Legislative Issues - Instructional Design Strategies - Leadership, Management Roles - Unique Funding for Programs and Faculty
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
