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Journals / AJIT-e: Bilişim Teknolojileri Online Dergisi / 2026 / Cilt: 17 Sayı: 2

How Medical AI Readiness, Anxiety, and Attitudes Shape Nursing Students' AI Competence – A Path Analysis

Tıbbi Yapay Zekâ Hazırbulunuşluğu, Kaygı ve Tutumlar Hemşirelik Öğrencilerinin Yapay Zekaya Yeterliliğini Nasıl Şekillendiriyor? – Yol Analizi

Hilal Büyüktopaç Çakıcı(BARTIN ÜNİVERSİTESİ)0000-0002-4411-788X
İlknur Dolu(Bartın Üniversitesi)0000-0002-0958-8395
Pages
164–185
Publication Date
2026-05-31
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

AI-focused interventions, along with the creation of a supportive learning environment for nursing students during their practicum, may be key strategies to enhance their AI competence.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study conducted a path analysis to examine the impact of several related variables on artificial intelligence (AI) user competence.
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Key Findings & Data:The participants included 310 nursing students from a single nursing school in Turkey.
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Conclusion & Impact:AI-focused interventions, along with the creation of a supportive learning environment for nursing students during their practicum, may be key strategies to enhance their AI competence.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Yapay zeka · Kaygı · Tutum

Abstract

This study conducted a path analysis to examine the impact of several related variables on artificial intelligence (AI) user competence. The participants included 310 nursing students from a single nursing school in Turkey. Hypothetical paths were established to assess the influence of medical artificial intelligence readiness, AI-related anxiety, and both positive and negative attitudes toward AI on AI competence. Positive attitudes toward AI and medical AI readiness had significant effects on AI competence. Additionally, both positive and negative attitudes toward AI had a significant negative effect on AI-related anxiety; however, the total, direct, and indirect effects of anxiety on AI competence were not significant. The model explained 33.5% of the variance in AI user competence. AI-focused interventions, along with the creation of a supportive learning environment for nursing students during their practicum, may be key strategies to enhance their AI competence.

Özet

Bu çalışmada, yapay zeka konusundaki kullanıcı yeterliliği üzerinde etkili olan çeşitli değişkenlerin etkisini öngörmek amacıyla bir yol analizi yapılmıştır. Katılımcılar, Türkiye'deki bir hemşirelik okulundan 310 hemşirelik öğrencisinden oluşmuştur. Bu çalışmada, tıbbi yapay zekâya hazırlık, yapay zekâ kaygısı ve yapay zekâya yönelik hem olumlu hem de olumsuz tutumların yapay zeka yeterliliği üzerindeki etkisine ilişkin varsayımsal yollar oluşturulmuştur. Yapay zekâya yönelik olumlu tutumlar ve tıbbi yapay zekâya hazırlık, yapay zeka yeterliliği üzerinde anlamlı etkilere sahip bulunmuştur. Ayrıca, yapay zekâya yönelik olumlu ve olumsuz tutumların yapay zekâ kaygısı üzerinde anlamlı ve olumsuz bir etkisi olduğu belirlenmiştir. Ancak, kaygının yapay zeka yeterliliği üzerindeki toplam etkisi anlamlı bulunmamış; dolaylı ve doğrudan etkiler de anlamlı değildir. Model, yapay zekâ kullanıcı yeterliliğindeki varyansın %33,5’ini açıkladı. Hemşirelik öğrencilerinin uygulama eğitimi sırasında sağlık kurumları tarafından destekleyici bir ortam yaratılmasıyla birlikte, yapay zeka odaklı müdahaleler, onların yapay zeka yeterliliklerini artırmada kilit müdahaleler olabilir.

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Cite This Article

Çakıcı, H. B., Dolu, İ. (2026). How Medical AI Readiness, Anxiety, and Attitudes Shape Nursing Students' AI Competence – A Path Analysis. AJIT-e: Bilişim Teknolojileri Online Dergisi, Cilt: 17 Sayı: 2, 164–185. https://doi.org/10.5824/ajite.2026.02.003.x

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