The implementation of artificial intelligence in nursing education and its psychological impact on nursing students: A cross-sectional study
Abstract
Background The integration of artificial intelligence (AI) into education has the potential to transform learning experiences, particularly in nursing. Understanding students' perceptions of AI and its impact on psychological factors, such as self-efficacy and psychological distress, is crucial for effective implementation. Objective The purpose of this study was to identify the factors that influence nursing students' perceptions of AI, psychological distress, and self-efficacy. Design A cross-sectional study design was used. Participants and setting This study was conducted using a convenience sample of 108 nursing students from a public university in Saudi Arabia. Methods Three instruments were used: The Perceptions of AI, Kessler Psychological Distress, and Self-Efficacy scales. Data were analyzed using independent sample t-tests, one-way ANOVA, Pearson's correlation, and multiple linear regression. Results The study found that participants who used AI displayed significant differences in their self-efficacy. Additionally, notable associations were found between perceptions of AI, self-efficacy, and psychological distress. However, while the regression models were significant, the perceptions of AI did not meaningfully explain the variance in these outcomes (p > 0.05). Conclusions This study highlights the significance of addressing students' perceptions of AI to improve their educational experiences and mental well-being. Future research should explore the longitudinal impact of mental health interventions in technology-driven learning environments.
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Knowledge about AI risks does not automatically translate into responsible interaction practices.
“The difference between awareness and critical engagement suggests that knowledge about AI risks does not automatically translate into responsible interaction practices, reinforcing arguments for stronger AI literacy and pedagogical scaffolding [6, 17, 28].”
From From adoption to interaction: examining human-GenAI engagement patterns, ethical tensions, and educational trade-offs in nursing education (2026) · cites this paper as [6]
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- Authors
- Bahari, G., Alenazi, L., & Alkathiri, A. M.
- Affiliations
- King Saud University (SA)
- Corresponding author
- Bahari, Ghareeb
- Venue
- Nurse Education Today
- Publisher
- Elsevier BV
- Published
- 2025-10
- Volume
- 153
- Pages
- 106816
- ISSN
- 0260-6917 (ISSN-L)
- OpenAlex
- W4411461312
- Cited by
- 10
- References
- 44
- Citations by year
- 2026: 9 · 2025: 1
- FWCI
- 10.48
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Nurse Education Today (publishedVersion)
- PubMed (publishedVersion)
- Funders
- King Saud University
- King Saud University Deanship of Scientific Research (ORF-2025-438)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Psychology › Developmental and Educational Psychology › Student Stress and Coping
- Keywords
- artificial intelligence in education, nursing students, perceptions of AI, self-efficacy, psychological distress, student mental health, nursing education, technology-enhanced learning
- MeSH terms
- Adult, Artificial Intelligence, Cross-Sectional Studies, Education, Nursing, Baccalaureate, Female, Humans, Male, Surveys and Questionnaires, Saudi Arabia, Stress, Psychological, Students, Nursing, Self Efficacy, Young Adult
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