Artificial Intelligence in Nursing Education: A Cross-sectional UTAUT Analysis Study
Introduction: Artificial Intelligence (AI) is a transformative force in nursing education, applicable in academic and clinical settings. It equips nursing students with skills to evaluate and apply AI in future patient care, preparing the nursing workforce for a healthcare landscape increasingly sup...
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| Format: | Article |
| Language: | English |
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JCDR Research and Publications Private Limited
2025-01-01
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| Series: | Journal of Clinical and Diagnostic Research |
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| Online Access: | https://jcdr.net/articles/PDF/20521/76894_CE[Ra1]_QC(NN)_F(SS)_PF1(AG_IS)_PFA(IS)_PB(AG_IS)_PN(IS).pdf |
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| _version_ | 1832590536373960704 |
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| author | Latifah H Alenazi |
| author_facet | Latifah H Alenazi |
| author_sort | Latifah H Alenazi |
| collection | DOAJ |
| description | Introduction: Artificial Intelligence (AI) is a transformative force in nursing education, applicable in academic and clinical settings. It equips nursing students with skills to evaluate and apply AI in future patient care, preparing the nursing workforce for a healthcare landscape increasingly supported by AI. However, lack of studies focus on nursing students as AI users and the behavioural intention to accept and utilise AI.
Aim: This study investigated the factors influencing nursing students’ acceptance and use of AI based on the Unified Theory of Acceptance and Use of Technology (UTAUT).
Materials and Methods: A cross-sectional study was conducted at one of the oldest and most prominent universities, collecting data from April to May 2022. The survey included 213 nursing students and aimed to evaluate the influence of the four UTAUT constructs- Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), and Facilitating Conditions (FC)- on behavioural intention and usage behaviour. Additionally, the study explored the moderating effects of age and gender on the UTAUT model. Data were analysed using Statistical Package for the Social Sciences (SPSS) version 29.0 for descriptive statistics and SmartPLS version 4 for Partial Least Squares (PLS) structural equation modeling.
Results: The findings indicated that PE positively influenced the behavioural intention of nursing students to adopt and use AI in nursing education. Regarding moderation effects, age moderated the relationship between PE and behavioural intention, whereas gender did not exhibit any moderation effect.
Conclusion: This study provides a foundation for its integration to enhance learning outcomes and prepare students for technology-driven healthcare. It highlights the importance of evidence-based strategies tailored to meet diverse educational needs, ensuring effective adoption and utilisation. |
| format | Article |
| id | doaj-art-6337b27875b34472be8bd88807b11045 |
| institution | Kabale University |
| issn | 2249-782X 0973-709X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | JCDR Research and Publications Private Limited |
| record_format | Article |
| series | Journal of Clinical and Diagnostic Research |
| spelling | doaj-art-6337b27875b34472be8bd88807b110452025-01-23T12:01:46ZengJCDR Research and Publications Private LimitedJournal of Clinical and Diagnostic Research2249-782X0973-709X2025-01-011901172210.7860/JCDR/2025/76894.20521Artificial Intelligence in Nursing Education: A Cross-sectional UTAUT Analysis StudyLatifah H Alenazi0PhD Candidate, College of Nursing, King Saud University, Riyadh, Saudi Arabia.Introduction: Artificial Intelligence (AI) is a transformative force in nursing education, applicable in academic and clinical settings. It equips nursing students with skills to evaluate and apply AI in future patient care, preparing the nursing workforce for a healthcare landscape increasingly supported by AI. However, lack of studies focus on nursing students as AI users and the behavioural intention to accept and utilise AI. Aim: This study investigated the factors influencing nursing students’ acceptance and use of AI based on the Unified Theory of Acceptance and Use of Technology (UTAUT). Materials and Methods: A cross-sectional study was conducted at one of the oldest and most prominent universities, collecting data from April to May 2022. The survey included 213 nursing students and aimed to evaluate the influence of the four UTAUT constructs- Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), and Facilitating Conditions (FC)- on behavioural intention and usage behaviour. Additionally, the study explored the moderating effects of age and gender on the UTAUT model. Data were analysed using Statistical Package for the Social Sciences (SPSS) version 29.0 for descriptive statistics and SmartPLS version 4 for Partial Least Squares (PLS) structural equation modeling. Results: The findings indicated that PE positively influenced the behavioural intention of nursing students to adopt and use AI in nursing education. Regarding moderation effects, age moderated the relationship between PE and behavioural intention, whereas gender did not exhibit any moderation effect. Conclusion: This study provides a foundation for its integration to enhance learning outcomes and prepare students for technology-driven healthcare. It highlights the importance of evidence-based strategies tailored to meet diverse educational needs, ensuring effective adoption and utilisation.https://jcdr.net/articles/PDF/20521/76894_CE[Ra1]_QC(NN)_F(SS)_PF1(AG_IS)_PFA(IS)_PB(AG_IS)_PN(IS).pdfbehavioural intentionnursing studentsuse behaviour |
| spellingShingle | Latifah H Alenazi Artificial Intelligence in Nursing Education: A Cross-sectional UTAUT Analysis Study Journal of Clinical and Diagnostic Research behavioural intention nursing students use behaviour |
| title | Artificial Intelligence in Nursing Education: A Cross-sectional UTAUT Analysis Study |
| title_full | Artificial Intelligence in Nursing Education: A Cross-sectional UTAUT Analysis Study |
| title_fullStr | Artificial Intelligence in Nursing Education: A Cross-sectional UTAUT Analysis Study |
| title_full_unstemmed | Artificial Intelligence in Nursing Education: A Cross-sectional UTAUT Analysis Study |
| title_short | Artificial Intelligence in Nursing Education: A Cross-sectional UTAUT Analysis Study |
| title_sort | artificial intelligence in nursing education a cross sectional utaut analysis study |
| topic | behavioural intention nursing students use behaviour |
| url | https://jcdr.net/articles/PDF/20521/76894_CE[Ra1]_QC(NN)_F(SS)_PF1(AG_IS)_PFA(IS)_PB(AG_IS)_PN(IS).pdf |
| work_keys_str_mv | AT latifahhalenazi artificialintelligenceinnursingeducationacrosssectionalutautanalysisstudy |