Journals / Bingöl Üniversitesi Sağlık Dergisi / 2026 / Cilt: 6 Sayı: 1

Examining the Relationship Between Nursing Students' Attitudes Towards Artificial Intelligence and Their Readiness for Medical Artificial Intelligence; A University Example

Hemşirelik Öğrencilerinin Yapay Zekâya Yönelik Tutumları ile Tıbbi Yapay Zekâya Hazır Bulunuşlukları Arasındaki İlişkisinin İncelenmesi; Bir Üniversite Örneği

Erhan Elmaoğlu(KİLİS 7 ARALIK ÜNİVERSİTESİ)0000-0002-4830-1625
Hacı Yusuf Güleç(Kilis 7 Aralık Üniversitesi)0000-0002-4277-2514
Pages
127–133
Publication Date
2025-04-26
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

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

It was determined that the general positive attitude of nursing students towards artificial intelligence had a moderate positive relationship with their readiness for medical artificial intelligence.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:It was conducted to determine the relationship between nursing students' attitudes towards artificial intelligence and their readiness for medical artificial intelligence.
📊
Key Findings & Data:The universe of the study consists of 400 students studying in the nursing department of a state university, and the sample consists of 318 students.
💡
Conclusion & Impact:It was determined that the general positive attitude of nursing students towards artificial intelligence had a moderate positive relationship with their readiness for medical artificial intelligence.
🔬Design:Araştırma Makalesi
📍Scope:Comprehensive Sample
⚙️Variables:Hemşirelik · Yapay Zekâ · Tıbbi Yapay Zekâ

Abstract

It was conducted to determine the relationship between nursing students' attitudes towards artificial intelligence and their readiness for medical artificial intelligence. It is a descriptive cross-sectional study. The universe of the study consists of 400 students studying in the nursing department of a state university, and the sample consists of 318 students. In the study, ‘’Personal Information Form’’, ‘’General Attitude Scale Towards Artificial Intelligence’’ and ‘’Medical Artificial Intelligence Readiness Scale’’ were used. In addition to descriptive statistics in the analysis of the data, t-test was used to compare two independent variables, One-Way ANOVA in independent groups in multiple comparisons, and single factor ANOVA in dependent samples. It was determined that 39.3% of the participants were in the first grade and 48.4% had an average of 2.5-3.0 points. The total average score of the Medical Artificial Intelligence Readiness Scale was calculated as 76.72±11.87 points. It was determined that the general positive attitude of nursing students towards artificial intelligence had a moderate positive relationship with their readiness for medical artificial intelligence.

Özet

Hemşirelik öğrencilerin yapay zekâya yönelik tutumlarının ve tıbbi yapay zekâya hazır bulunuşlukları arasındaki ilişkisinin belirlenmesi amacıyla yapılmıştır. Tanımlayıcı kesitsel araştırmadır. Araştırmanın evrenini bir devlet üniversitesinde hemşirelik bölümünde eğitim gören 400 öğrenci, örneklemini ise 318 öğrenci oluşturmaktadır. Çalışmada ‘’Kişisel Bilgi Formu’’, ‘’Yapay Zekâya Yönelik Genel Tutum Ölçeği’’ ve ‘’Tıbbi Yapay Zekâ Hazır Bulunuşluk Ölçeği’’ kullanılmıştır. Verilerin analizinde tanımlayıcı istatistiklerin yanı sıra iki bağımsız değişkenin karşılaştırılmasında t testi, çoklu karşılaştırmalarda bağımsız gruplarda One-Way ANOVA, bağımlı örneklemlerde tek faktörlü ANOVA kullanılmıştır. Katılımcıların %39,3’nun 1. Sınıf ve %48,4’nün ortalamasının 2,5-3,0 puan arasında olduğu belirlendi. Tıbbi Yapay Zekâ Hazır Bulunuşluk Ölçeği toplam puan ortalamasının 76,72±11.87 puan olduğu hesaplandı. Hemşirelik öğrencilerin yapay zekâya yönelik genel pozitif tutumunun, tıbbi yapay zekâya yönelik hazır bulunuşlarını pozitif yönde ortada düzeyde ilişki olduğu belirlendi.

🔖

Cite This Article

Elmaoğlu, E., Güleç, H. Y. (2025). Examining the Relationship Between Nursing Students' Attitudes Towards Artificial Intelligence and Their Readiness for Medical Artificial Intelligence; A University Example. Bingöl Üniversitesi Sağlık Dergisi, Cilt: 6 Sayı: 1, 127–133. https://doi.org/10.58605/bingolsaglik.1600963

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (22)

  1. Abou Hashish, E. A., & Alnajjar, H. (2024). Digital proficiency: Assessing knowledge, attitudes, and skills in digital transformation, health literacy, and artificial intelligence among university nursing students. BMC Medical Education, 24(1), 508. https://doi.org/10.1186/s12909-024-05482-3
  2. Kong, W., Ning, Y., Ma, T., Song, F., Mao, Y., Yang, C., Li, X., Guo, Y., Liu, H., Shi, J., & Liu, L. (2024). Experience of undergraduate nursing students participating in artificial intelligence + project task driven learning at different stages: A qualitative study. BMC Nursing, 23(1), 314. https://doi.org/10.1186/s12912-024-01982-1.
  3. Lukić, A., Kudelić, N., Antičević, V., Lazić-Mosler, E., Glunčić, V., Hren, D., & Lukić, I. K. (2023). First- year nursing students’ attitudes towards artificial intelligence: Cross-sectional multi-center study. Nurse Education in Practice, 71, 103735. https://doi.org/10.1016/j.nepr.2023.103735
  4. Süner, A. F., & Bilgin, A. C. (2024). Yapay Zekâ ve Sağlık Konusunda Yayınlanmış Tezler. Sosyal Bilimler ve Sağlık Bülteni, 10, Article 10. https://doi.org/10.5281/zenodo.11397660
  5. Tarcan, G. Y., Balçık, P. Y., & Sebik, N. B. (2024). Türkiye ve Dünyada Sağlık Hizmetlerinde Yapay Zekâ. Mersin Üniversitesi Tıp Fakültesi Lokman Hekim Tıp Tarihi ve Folklorik Tıp Dergisi, 14(1), Article 1. https://doi.org/10.31020/mutftd.1278529
  6. WHO. (2019). Recommendations on digital interventions for health system strengthening. https://www.who.int/publications/i/item/9789241550505
  7. Amiri, H., Peiravi, S., Rezazadeh Shojaee, S. S., Rouhparvarzamin, M., Nateghi, M. N., Etemadi, M. H., ShojaeiBaghini, M., Musaie, F., Anvari, M. H., & Asadi Anar, M. (2024). Medical, dental, and nursing students’ attitudes and knowledge towards artificial intelligence: A systematic review and meta-analysis. BMC Medical Education, 24(1), 412. https://doi.org/10.1186/s12909-024-05406-1
  8. Gökalp, M. G., & Üzer, M. A. (2024). Yapay Zeka Çağında Hemşirelik Bakımı. Sağlık Bilimleri Üniversitesi Hemşirelik Dergisi, 6(1), Article 1. https://doi.org/10.48071/sbuhemsirelik.1349981
  9. Heyn, L. G., Brembo, E. A., Byermoen, K. R., Cruaud, C., Eide, H., Flo, J., Nordsteien, A., Overgaard, G., & Egilsdottir, H. Ö. (2023). Exploring facilitation in virtual simulation in nursing education: A scoping review. PEC innovation, 100233. https://www.sciencedirect.com/science/article/pii/S2772628223001139
  10. Karaca, O., Çalışkan, S. A., & Demir, K. (2021). Medical artificial intelligence readiness scale for medical students (MAIRS-MS) – development, validity and reliability study. BMC Medical Education, 21(1), 112. https://doi.org/10.1186/s12909-021-02546-6
  11. Mutlu, İ. S., & Yılmaz, A. Y. (2024). Yaşlı Bakımında Yapay Zeka Tabanlı Eğitim Uygulamaları: Etkinlik ve Uygulama Alanları. Dijital Teknolojiler ve Eğitim Dergisi, 3(1), Article 1. https://doi.org/10.5281/zenodo.12637413
  12. Menekli̇, T. (2024). Hemşirelerin Sağlık Hizmetlerinde Teknoloji ve Verilerle Kapatılabilecek Boşlukları Analiz Etme Yetisi. Turkiye Klinikleri Internal Medicine Nursing - Special Topics, 10(2), 114-118.
  13. Ünal, A. S., & Avcı, A. (2024). Pediatri Hemşireliğinde Yapay Zeka. Akdeniz Hemşirelik Dergisi, 3(1), Article 1. https://doi.org/10.59398/ahd.1346089
  14. Benfatah, M., Youlyouz-Marfak, I., Saad, E., Hilali, A., Nejjari, C., & Marfak, A. (2024). Impact of artificial intelligence-enhanced debriefing on clinical skills development in nursing students: A comparative study. Teaching and Learning in Nursing, 19(3), e574-e579. https://doi.org/10.1016/j.teln.2024.04.007
  15. Taskiran, N. (2023). Effect of Artificial Intelligence Course in Nursing on Students’ Medical Artificial Intelligence Readiness: A Comparative Quasi-Experimental Study. Nurse Educator, 48(5), E147-E152. https://doi.org/10.1097/NNE.0000000000001446
  16. White, A., Maguire, M. B., Brown, A., & Keen, D. (2024). Impact of Artificial Intelligence on Nursing Students’ Attitudes toward Older Adults: A Pre/Post-Study. Nursing Reports, 14(2), 1129-1135. https://doi.org/10.3390/nursrep14020085
  17. Kandemir, F., & Azizoğlu, F. (2024). Hemşirelerin Yapay Zekaya Yönelik Genel Tutumlarının İncelenmesi. Yoğun Bakım Hemşireliği Dergisi, 28(2), Article 2. https://doi.org/10.62111/ybhd.1502758
  18. Park, S. Y., Park, M., Choi, N. Y., & Park, S. J. (2022). A study on the artificial intelligence interview experience of nursing students in the COVID-19 situation. Nursing Practice Today. https://doi.org/10.18502/npt.v9i3.10224
  19. Demir-Kaymak, Z., Turan, Z., Unlu-Bidik, N., & Unkazan, S. (2024). Effects of midwifery and nursing students’ readiness about medical Artificial intelligence on Artificial intelligence anxiety. Nurse Education in Practice, 78, 103994. https://doi.org/10.1016/j.nepr.2024.103994
  20. Gencer, K., & Gencer, G. (2024). Tıp Fakültesi Öğrencilerinin Tıbbi Yapay Zeka Hazır Bulunuşluluğunun İncelenmesi. Kocatepe Tıp Dergisi, 25(2), Article 2. https://doi.org/10.18229/kocatepetip.1295779
  21. Simsek-Cetinkaya, S., & Cakir, S. K. (2023). Evaluation of the effectiveness of artificial intelligence assisted interactive screen-based simulation in breast self-examination: An innovative approach in nursing students. Nurse Education Today, 127, 105857. https://doi.org/10.1016/j.nedt.2023.105857
  22. Kaya, F., Aydin, F., Schepman, A., Rodway, P., Yetişensoy, O., & Demir Kaya, M. (2024). The Roles of Personality Traits, AI Anxiety, and Demographic Factors in Attitudes toward Artificial Intelligence. International Journal of Human–Computer Interaction, 40(2), 497-514. https://doi.org/10.1080/10447318.2022.2151730