Factors associated with attitudes towards artificial intelligence among medical students: roles of digital literacy, emotional intelligence, and AI-related perceptions.
Digital literacy was the strongest independent factor associated with attitudes towards artificial intelligence and perceptions regarding the impact of artificial intelligence on clinical reasoning, and perceptions regarding the impact of artificial intelligence on clinical reasoning were also independently associated with attitude scores.
Abstract
Background
Artificial intelligence is increasingly used in health care and medical education. This study aimed to identify factors associated with medical students' attitudes towards artificial intelligence, with particular attention to digital literacy, emotional intelligence and artificial intelligence-related perceptions.
Methods
This cross-sectional study was conducted between November 2025 and January 2026 among 358 medical students. Data were collected using an online questionnaire including sociodemographic items, the Trait Emotional Intelligence Scale-Short Form, the Digital Literacy Scale and the General Attitude Towards Artificial Intelligence Scale. Group comparisons, correlation analyses, hierarchical linear regression and exploratory indirect-effect analysis were performed.
Results
Most students had previously used artificial intelligence (88.5%), while 59.5% reported ethical or legal concerns and 41.1% believed that artificial intelligence could reduce clinical reasoning. Digital literacy was positively correlated with attitudes towards artificial intelligence (r = 0.319, p < 0.001). In the final hierarchical regression model, digital literacy was the strongest independent factor associated with attitudes towards artificial intelligence (B = 0.242, β = 0.320, p< 0.001). Clinical educational stage, ethical or legal concerns, and perceptions regarding the impact of artificial intelligence on clinical reasoning were also independently associated with attitude scores. Previous artificial intelligence use and emotional intelligence were not independently associated with attitudes after adjustment.
Conclusions
Medical students' attitudes towards artificial intelligence were associated more strongly with digital literacy and artificial intelligence-related perceptions than with previous use alone. Undergraduate medical education should integrate digital literacy, ethical awareness and reflective discussion on clinical reasoning into artificial intelligence-related teaching.
BACKGROUND
Artificial intelligence (AI) is increasingly reshaping health professions education, but its relationship with students' future career outlook remains insufficiently understood, particularly in professions where human interaction remains central to practice. This study examined whether career optimism in physiotherapy and rehabilitation students was more closely associated with AI literacy than with AI anxiety.
METHODS
This cross-sectional study included 438 undergraduate physiotherapy and rehabilitation students from universities in Ankara and Konya, Türkiye. Data were collected online between 27 February 2026 and 10 March 2026 using Turkish versions of the Artificial Intelligence Literacy Scale, Artificial Intelligence Anxiety Scale, and the Career Optimism subscale of the Career Futures Inventory. Pearson correlations, subgroup comparisons, and hierarchical multiple linear regression analyses were performed. HC3 robust standard errors were used in sensitivity analyses.
RESULTS
AI literacy was positively associated with career optimism (r = 0.396, p < 0.001), whereas AI anxiety showed a weaker negative association (r = - 0.147, p = 0.002). In hierarchical regression analyses, AI literacy was the only variable independently associated with career optimism in both the crude model (B = 0.281, β = 0.384, p < 0.001) and the adjusted model controlling for age, gender, class year, grade point average, formal AI-related training, and weekly AI use frequency (B = 0.284, β = 0.388, p < 0.001). AI anxiety was not independently associated with career optimism in either model. Sensitivity analyses using HC3 robust standard errors supported these findings.
CONCLUSIONS
Among physiotherapy and rehabilitation students, career optimism was more strongly associated with AI literacy than with AI anxiety. Even in a strongly human-centered health profession, students' career-related expectations appear to be linked more closely to perceived AI-related competence than to AI-related anxiety. Educational strategies may therefore benefit from supporting AI literacy as part of future professional readiness.
TRIAL REGISTRATION
Not applicable.
M. Güler, Senem Demirdel, Ertuğrul Demirdel et al.· BMC Medical Education· 0 citations
Self-efficacy plays a critical mediating role in the relationship between digital literacy and AI anxiety among nursing students, and interventions aimed at enhancing both digital literacy and self-efficacy may be effective in reducing AI-related anxiety and supporting nursing students' psychological adaptation to emerging technologies.
Gamze Akay, Uğur Saruhan, Yeşim Saruhan et al.· BMC Nursing· 0 citations
Objective This study aimed to determine the level of perceived influence of internet-based information on decision-making among pregnant women and to examine its associations with artificial intelligence (AI) literacy and attitudes toward AI. Methods This cross-sectional study was conducted among 423 pregnant women attending a university hospital in Balikesir, Türkiye, between March and December 2025. Data were collected using a Personal Information Form, the Decision-Making Scale via Internet on Pregnancy (DMSIP), the Artificial Intelligence Literacy Scale (AILS), and the General Attitudes toward Artificial Intelligence Scale (GAAIS). Descriptive statistics, group comparisons, correlation analyses, hierarchical multiple linear regression, structural equation modeling (SEM), and mediation analysis within the SEM framework were performed. Results The mean DMSIP score was 34.39 ± 7.73. The hierarchical regression model explained 59.0% of the variance, with positive attitudes toward AI emerging as the strongest associated factor. In the structural equation model, the contextual latent composite construct representing selected sociodemographic and obstetric characteristics (SDS) showed the strongest direct association with DMSIP, followed by attitudes toward AI. AI literacy was not significantly associated with DMSIP in the structural model. Mediation analysis showed that attitudes toward AI mediated the association between AI literacy and DMSIP. Conclusion The role of AI should also be considered when examining the perceived influence of internet-based information on pregnancy-related decision-making. As AI becomes increasingly integrated into pregnancy-related information environments, the development and validation of pregnancy-specific instruments to assess AI literacy and attitudes toward AI may strengthen future research in this field.
Esra Çevik· Frontiers in Public Health· 0 citations
Positive correlations were identified among all measured dimensions, with the strongest association observed between hope and adaptation, and the findings indicate that students’ evaluations of artificial intelligence involve interrelated perceptions of knowledge, anxiety, positive expectations, and educational preparation needs.
The findings indicate that nursing students had generally positive levels of AI literacy and attitudes toward AI, and higher AI literacy was associated with more positive attitudes toward AI.
M. Çil, Berna Eren Fidancı, D. Yildiz· Journal of Education and Res...· 0 citations
Objective: As the growing use of artificial intelligence in healthcare creates both new opportunities and emerging concerns for nursing practice, this study aimed to examine nursing students’ attitudes toward artificial intelligence and to explore the extent to which AI-related anxiety and technological dependency predict these attitudes.Methods: A descriptive, cross-sectional, and correlational design was employed with a sample of 373 nursing students studying at a public university in Türkiye. The data were collected using three instruments: the General Attitudes Toward Artificial Intelligence Scale, the Artificial Intelligence Anxiety Scale, and the Artificial Intelligence Dependency Scale. The statistical analyses encompassed a range of methodologies, including descriptive measures, Pearson correlation coefficients, and multiple regression models.Results: The mean score for the General Attitudes toward Artificial Intelligence Scale Positive Attitudes subscale was 42.17 ± 8.71, while the mean score for the Negative Attitudes subscale was 24.44 ± 6.69. The mean total scores for the Artificial Intelligence Anxiety Scale and the Dependence on Artificial Intelligence Scale were 73.57 ± 25.54 and 12.39 ± 4.28, respectively. Multiple regression analyses revealed that AI-related anxiety and dependency significantly predicted positive attitude scores (R² = 0.060, F = 11.722, p < 0.001) and negative attitude scores (R² = 0.349, F = 98.985, p < 0.001).Conclusions: The findings underscore the notion that nursing students’ perceptions of artificial intelligence are shaped by a complex interplay of emotional, cognitive, and behavioral factors. In accordance with a holistic nursing framework, incorporating artificial intelligence into education should extend beyond the acquisition of technical skills to encompass the maintenance of core care values, such as empathy, ethical awareness, and effective communication. Educational approaches that encourage critical engagement with technology and its balanced use may facilitate the ethical and efficient integration of artificial intelligence into nursing practice.
Eren Sarıtaş, Pınar ÇİÇEKOĞLU ÖZTÜRK· Sakarya Üniversitesi Holisti...· 0 citations
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