The AI-personality nexus: How nursing students' traits shape use, learning and adaptation in artificial intelligence-driven education in Saudi Arabia
Abstract
Aim This study explored how personality traits influence undergraduate nursing students' use, attitudes and satisfaction with artificial intelligence (AI)-based educational tools in Saudi Arabia. It also examined the mediating role of AI use frequency in the relationship between personality and satisfaction.Background With AI increasingly integrated into nursing education, understanding how individual learner traits shape engagement with these tools is essential for optimizing learning experiences.Design A cross-sectional, correlational design was used.Methods A total of 159 undergraduate nursing students from King Faisal University completed an online survey, including the NEO Five-Factor Inventory and the AI in Nursing Education Questionnaire (AINEQ). Descriptive statistics, logistic regression and path analysis were performed to assess associations and mediation.Results Openness and Conscientiousness significantly predicted frequent AI use (p < 0.01), while Neuroticism negatively predicted satisfaction (p = 0.03). AI use frequency significantly mediated the relationship between personality traits (Openness, Conscientiousness) and satisfaction (indirect βs = 0.13 and 0.11, p < 0.01). Extraversion and Agreeableness were not significantly related to AI use. Female students and those in advanced academic levels reported greater AI engagement.Conclusion Personality traits influence AI adoption and satisfaction among nursing students. Personalizing AI-based learning strategies to account for personality differences may enhance student engagement and educational outcomes.
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Ali and Shaban highlighted that AI applications can enhance nursing students' engagement in clinical practice, deepening their reflective thinking and professional communication.
“In line with this, Ali and Shaban highlighted that AI applications can enhance nursing students’ engagement in clinical practice, deepening their reflective thinking and professional communication [48].”
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“In line with this, Ali and Shaban highlighted that AI applications can enhance nursing students’ engagement in clinical practice, deepening their reflective thinking and professional communication [48].”
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“31 32 This readiness gap risks producing graduates who are capable AI-device manipulators but insufficiently literate AI residents, who are able to click but not to critique.”
- Authors
- Ali, S. I., & Shaban, M.
- Affiliations
- King Faisal University (SA)
- Cairo University (EG)
- Corresponding author
- Shaban, Mostafa
- Venue
- Nurse Education in Practice
- Publisher
- Elsevier BV
- Published
- 2025-08
- Volume
- 87
- Pages
- 104498
- ISSN
- 1471-5953 (ISSN-L)
- OpenAlex
- W4413053595
- Cited by
- 6
- References
- 32
- Citations by year
- 2026: 4 · 2025: 2
- FWCI
- 0.35
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Nurse Education in Practice (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Deanship of Scientific Research, King Faisal University
- King Faisal University (KFU252787)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Keywords
- artificial intelligence in education, nursing students, personality traits, Big Five personality traits, AI use frequency, student satisfaction, artificial intelligence adoption, openness to experience, conscientiousness, neuroticism, mediation, personalized learning, Saudi Arabia
- MeSH terms
- Adult, Artificial Intelligence, Cross-Sectional Studies, Education, Nursing, Baccalaureate, Female, Humans, Learning, Male, Personality, Surveys and Questionnaires, Saudi Arabia, Students, Nursing, Young Adult
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