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Journals / Uluslararası Batı Karadeniz Sosyal ve Beşeri Bilimler Dergisi / 2026 / Cilt: 10 Sayı: 2

INVESTIGATION OF MEDICAL ARTIFICIAL INTELLIGENCE READINESS: THE CASE OF BITLIS EREN UNIVERSITY

TIBBİ YAPAY ZEKÂ HAZIRBULUNUŞLUĞUNUN İNCELENMESİ: BİTLİS EREN ÜNİVERSİTESİ ÖRNEĞİ

Mansur Beştaş(BİTLİS EREN ÜNİVERSİTESİ)0000-0002-8192-2044
Serap Okdayan(BİTLİS EREN ÜNİVERSİTESİ)0000-0002-8873-6164
Pages
269–304
Publication Date
2026-07-06
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AI Summary & Key Takeaways

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

The findings indicate that students’ overall levels of artificial intelligence awareness are above moderate across all dimensions.

📌 3 Key Takeaways

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Objective & Hypothesis:This study aims to examine the levels of cognitive awareness, practical skills, foresight, and ethical sensitivity regarding artificial intelligence among university students enrolled in health sciences programs, and to evaluate the relationships between these dimensions and selected demographic and educational variables.
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Key Findings & Data:The study is conducted with 409 undergraduate students enrolled in midwifery and nursing departments using a cross-sectional survey design.
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Conclusion & Impact:The findings indicate that students’ overall levels of artificial intelligence awareness are above moderate across all dimensions.
🔬Design:Survey & Empirical Study
📍Scope:Comprehensive Sample
⚙️Variables:Yapay Zekâ · Tıbbi Yapay Zekâ · Yapay Zekâ Eğitimi

Abstract

This study aims to examine the levels of cognitive awareness, practical skills, foresight, and ethical sensitivity regarding artificial intelligence among university students enrolled in health sciences programs, and to evaluate the relationships between these dimensions and selected demographic and educational variables. The study is conducted with 409 undergraduate students enrolled in midwifery and nursing departments using a cross-sectional survey design. Data are collected through a structured questionnaire that includes demographic information and a multidimensional artificial intelligence awareness scale. Descriptive statistics, independent samples t-tests, one-way ANOVA, and Pearson correlation analyses are employed to analyze the data. The findings indicate that students’ overall levels of artificial intelligence awareness are above moderate across all dimensions. While most demographic variables do not demonstrate significant differences, the use of ChatGPT or similar artificial intelligence applications is significantly associated with higher levels of skills, foresight, and ethical awareness. Grade level and place of upbringing emerge as significant predictors of artificial intelligence awareness, whereas income level and daily internet usage duration are not significant predictors. Positive and moderate-to-strong correlations among the scale dimensions suggest a coherent and multidimensional structure of artificial intelligence awareness. The results indicate that artificial intelligence awareness among health sciences students is shaped primarily by experiential and contextual factors rather than by basic demographic characteristics. Integrating artificial intelligence education into health sciences curricula through a holistic approach that emphasizes ethical considerations, practical applications, and critical foresight may contribute to the development of competent and responsible future healthcare professionals.

Özet

Bu çalışma, sağlık bilimleri alanında öğrenim gören üniversite öğrencilerinin yapay zekâya ilişkin bilişsel farkındalık, pratik beceriler, öngörü ve etik duyarlılık düzeylerini incelemeyi ve bu boyutlar ile seçilen demografik ve eğitimsel değişkenler arasındaki ilişkileri değerlendirmeyi amaçlamıştır. Çalışma, kesitsel bir anket tasarımı kullanılarak ebelik ve hemşirelik bölümlerinde kayıtlı 409 lisans öğrencisiyle gerçekleştirilmiştir. Veriler, demografik bilgiler ve çok boyutlu bir yapay zekâ farkındalık ölçeği içeren yapılandırılmış bir anket kullanılarak toplanmıştır. Verilerin analizinde tanımlayıcı istatistikler, bağımsız örneklem t-testleri, tek yönlü ANOVA ve Pearson korelasyon analizleri kullanılmıştır. Bulgular, öğrencilerin genel yapay zekâ farkındalık düzeylerinin tüm boyutlarda orta düzeyin üzerinde olduğunu ortaya koymuştur. Çoğu demografik değişken anlamlı farklılıklar göstermezken, ChatGPT veya benzeri yapay zekâ uygulamalarının kullanımı, beceri, öngörü ve etik farkındalık düzeylerinde anlamlı derecede daha yüksek seviyelerle ilişkilendirilmiştir. Sınıf düzeyi ve yetişme yeri, yapay zekâ farkındalığının önemli belirleyicileri olarak bulunurken, gelir düzeyi ve günlük internet kullanım süresi istatistiksel olarak anlamlı bulunmamıştır. Ölçek boyutları arasındaki pozitif ve orta-güçlü korelasyonlar, yapay zekâ farkındalığının tutarlı ve çok boyutlu bir yapısını göstermiştir. Sonuçlar, sağlık bilimleri öğrencilerinde yapay zekâ farkındalığının temel demografik özelliklerden ziyade öncelikle deneyimsel ve bağlamsal faktörler tarafından şekillendirildiğini göstermektedir. Etik hususları, pratik uygulamaları ve eleştirel öngörüyü vurgulayan bütüncül bir yaklaşımla yapay zekâ eğitiminin sağlık bilimleri müfredatına entegre edilmesi, yetkin ve sorumlu geleceğin sağlık profesyonellerinin gelişimine katkıda bulunabilir.

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Cite This Article

Beştaş, M., Okdayan, S. (2026). INVESTIGATION OF MEDICAL ARTIFICIAL INTELLIGENCE READINESS: THE CASE OF BITLIS EREN UNIVERSITY. Uluslararası Batı Karadeniz Sosyal ve Beşeri Bilimler Dergisi, Cilt: 10 Sayı: 2, 269–304. https://doi.org/10.46452/baksoder.1911708

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References (43)

  1. Abuadas, M. H., Albikawi, Z. M., & Rayani, A. M. (2025). The impact of an AI-focused ethics education program on nursing students' AI ethical awareness, moral sensitivity, attitudes, and intentions to use generative AI in healthcare. BMC Nursing, 24(1), 720. https://doi.org/10.1186/s12912-025-03458-2
  2. Almalki, M., Alkhamis, M. A., Khairallah, F. M., & Choukou, M. (2025). Perceived artificial intelligence readiness in medical and health sciences education: a survey study of students in Saudi Arabia. BMC Medical Education, 25(1), 439. https://doi.org/10.1186/s12909-025-06995-1
  3. Al-Qerem, W., Eberhardt, J., Jarab, A., Abdel Qader, A. B., Hammad, A., Fawaz Alasmaridi’ah Alazab, Daoud, A. H., Alazab, J., & Al-Beool, S. (2023). Exploring knowledge, attitudes, and practices towards artificial intelligence among health professions’ students in Jordan. BMC Medical Informatics and Decision Making, 23, 1-7. https://doi.org/10.1186/s12911-023-02403-0
  4. Amiri, H., Peiravi, S., Seyedeh sara, r. s., Rouhparvarzamin, M., Mohammad, N. N., Mohammad, H. E., ShojaeiBaghini, M., Musaie, F., Mohammad, H. A., & Mahsa, A. A. (2024). Medical, dental, and nursing students’ attitudes and knowledge towards artificial intelligence: a systematic review and meta-analysis. BMC Medical Education, 24, 1-12. https://doi.org/10.1186/s12909-024-05406-1
  5. Bajwa, J., Munir, U., Nori, A., & Williams, B. (2021). Artificial intelligence in healthcare: Transforming the practice of medicine. Future Healthcare Journal, 8(2), e188–e194. https://doi.org/10.7861/fhj.2021-0095
  6. Baseer, S., Jamil, B., Khan, S. A., Khan, M., Syed, A., & Ali, L. (2025). Readiness towards artificial intelligence among medical and dental undergraduate students in Peshawar, Pakistan: a cross-sectional survey. BMC medical education, 25(1), 632. https://doi.org/10.1186/s12909-025-06911-7
  7. Briganti, G., & Le Moine, O. (2020). Artificial intelligence in medicine: Today and tomorrow. Frontiers of Medicine, 7, 27. https://doi.org/10.3389/fmed.2020.00027
  8. Celikturk Doruker, N., & Kara, R. H. (2026). Attitudes and readiness of nursing students practising in surgical units towards artificial intelligence: a cross-sectional study. BMC Medical Education, 26(1), 540. https://doi.org/10.1186/s12909-026-08583-3
  9. Chang, L., & Sreedhar, R. (2026). Integrating AI literacy into medical education: Preparing future clinicians for an AI-driven healthcare system. Medical Science Educator, 36, 17-2. https://doi.org/10.1007/s40670-025-02599-y
  10. Chen, M., Zhang, B., Cai, Z., Seery, S., Gonzalez, M. J., Ali, N. M., Ren, R., Qiao, Y., Xue, P., & Jiang, Y. (2022). Acceptance of clinical artificial intelligence among physicians and medical students: A systematic review with cross-sectional survey. Frontiers in Medicine, 9, 990604. https://doi.org/10.3389/fmed.2022.990604
  11. David, S. L., Sawyer, J., Luna, A., Aoun, J., Wang, J., Boachie, L., Halabi, S., & Bina, J. (2022). Perceptions of US Medical Students on Artificial Intelligence in Medicine: Mixed Methods Survey Study. JMIR Medical Education, 8(4). https://doi.org/10.2196/38325
  12. Elmaoğlu, E., & Güleç, H. Y. (2025). Hemşirelik Öğrencilerinin Yapay Zekâya Yönelik Tutumları ile Tıbbi Yapay Zekâya Hazır Bulunuşlukları Arasındaki İlişkisinin İncelenmesi; Bir Üniversite Örneği. Bingöl Üniversitesi Sağlık Dergisi, 6(1), 127-133. https://doi.org/10.58605/bingolsaglik.1600963
  13. Gencer, K., & Gencer, G. (2024). Tıp Fakültesi Öğrencilerinin Tıbbi Yapay Zeka Hazır Bulunuşluluğunun İncelenmesi. Kocatepe Tıp Dergisi, 25(2), 143-149. https://doi.org/10.18229/kocatepetip.1295779
  14. Gong, B., Nugent, J. P., Guest, W., Parker, W., Chang, P. J., Khosa, F., & Nicolaou, S. (2019). Influence of artificial intelligence on Canadian medical students' preference for radiology specialty: A national survey study. Academic Radiology, 26(4), 566–577. https://doi.org/10.1016/j.acra.2018.10.007
  15. Gordon, M., Daniel, M., Ajiboye, A., Uraiby, H., Xu, N. Y., Bartlett, R.,. & Thammasitboon, S. (2024). A scoping review of artificial intelligence in medical education: BEME guide no. 84. Medical Teacher, 46(4), 446–470. https://doi.org/10.1080/0142159X.2024.2314198
  16. Gürbüz, S., & Şahin, F. (2018). Sosyal bilimlerde araştırma yöntemleri (3. baskı). Seçkin Yayıncılık.
  17. Güven, G. Ö., Yilmaz, Ş., & Inceoğlu, F. (2024). Determining medical students' anxiety and readiness levels about artificial intelligence. Heliyon, 10(4). https://doi.org/10.1016/j.heliyon.2024.e25894
  18. Hamad, M., Qtaishat, F., Mhairat, E., Al-Qunbar, A., Jaradat, M., Mousa, A., Faidi, B., & Alkhaldi, S. (2024). Artificial intelligence readiness among Jordanian medical students: Using Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS). Journal of Medical Education and Curricular Development, 11, 23821205241281648. https://doi.org/10.1177/23821205241281648
  19. Hanifa, H., Atia, M., Daboul, R., Alhamid, A. A. H., Alayyoubi, A., Naima, H. A., Alkassar, D., Nabhan, M. G., Alsaleh, B., & Abdula, F. (2025). Artificial intelligence in focus: assessing awareness and perceptions among medical students in three private Syrian universities. BMC Medical Education, 25(1), 801. https://doi.org/10.1186/s12909-025-07396-0
  20. Holmes, W., & Tuomi, I. (2022). State of the art and practice in AI in education. European Journal of Education, 57(4), 542–570. https://doi.org/10.1111/ejed.12533
  21. Hornberger, M., Bewersdorff, A., & Nerdel, C. (2023). What do university students know about artificial intelligence? Development and validation of an AI literacy test. Computers and Education: Artificial Intelligence, 5, 100165. https://doi.org/10.1016/j.caeai.2023.100165
  22. 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, 112. https://doi.org/10.1186/s12909-021-02546-6
  23. Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., … Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, Article 102274. https://doi.org/10.1016/j.lindif.2023.102274
  24. Kaya, G., Büyükyılmaz, F., Çulha, Y., & Akyürek, P. (2025). Investigation of the relationship between medical artificial intelligence readiness and individual innovativeness levels in nursing students. Nurse Education Today, 151, 106721. https://doi.org/10.1016/j.nedt.2025.106721
  25. Koç, A. D. (2025). Expanding the scope of AI readiness: Validation of the MAIRS scale among dental, nursing, and midwifery students [Preprint]. Research Square. https://doi.org/10.21203/rs.3.rs-6414139/v1
  26. Lane, M., Williams, M., & Broecke, S. (2023). The impact of AI on the workplace: Main findings from the OECD AI surveys of employers and workers. In OECD social, employment and migration working papers. Advance online publication. https://doi.org/10.1787/ea0a0fe1-en
  27. Li, C., Sian Hsiang-Te Tsuei, & Wu, H. (2026). AI Literacy Among Chinese Medical Students: Cross-Sectional Examination of Individual and Environmental Factors. JMIR Medical Education, 12, 15. https://doi.org/10.2196/80604
  28. Luo, J., Peng, Q., Cao, H., Li, W., & Tan, S. (2026). Medical undergraduate students’ readiness and anxiety toward artificial intelligence: a systematic review and meta-analysis. BMC Medical Education. 26, 44. https://doi.org/10.1186/s12909-025-08388-w
  29. Lugito, N., Cucunawangsih, C., Suryadinata, N., Kurniawan, A., Wijayanto, R., Sungono, V., Sabran, M., Albert, N., Budianto, C., Rubismo, K., Purushotama, N., & Zebua, A. (2024). Readiness, knowledge, and perception towards artificial intelligence of medical students at faculty of medicine, Pelita Harapan University, Indonesia: A cross sectional study. BMC Medical Education, 24. https://doi.org/10.1186/s12909-024-06058-x
  30. Muro, M., & Methkupally, S. (2025). Mapping the AI economy: Which regions are ready for the next technology leap? Brookings Institution. https://www.brookings.edu/articles/mapping-the-ai-economy-which-regions-are-ready-for-the-next-technology-leap/
  31. Oliver-Trobat, M., de la Serna, J., Rosselló, M. R., & Cantallops, V. A. S. (2025). Student perceptions of ChatGPT: benefits, costs, and attitudinal differences between users and non-users toward AI integration in higher education. Education and Information Technologies, 30, 19741–19764. https://doi.org/10.1007/s10639-025-13575-9
  32. Park, C. J., Paul, H. Y., & Siegel, E. L. (2021). Medical student perspectives on the impact of artificial intelligence on the practice of medicine. Current Problems in Diagnostic Radiology, 50(5), 614–619. https://doi.org/10.1067/j.cpradiol.2020.06.011
  33. Park, C. W., Seo, S. W., Kang, N., Ko, B., Choi, B. W., Park, C. M., et al. (2020). Artificial intelligence in health care: Current applications and issues. Journal of Korean Medical Science, 35(42). https://doi.org/10.3346/jkms.2020.35.e379
  34. Pinto dos Santos, D., Giese, D., Brodehl, S., Chon, S. H., Staab, W., Kleinert, R.,. & Baeßler, B. (2019). Medical students' attitude towards artificial intelligence: A multicentre survey. European Radiology, 29(4), 1640–1646. https://doi.org/10.1007/s00330-018-5601-1
  35. Ravšelj, D., Keržič, D., Tomaževič, N., Umek, L., Brezovar, N., A Iahad, N., Abdulla, A. A., Akopyan, A., Aldana Segura, M. W., AlHumaid, J., Allam, M. F., Alló, M., Andoh, R. P. K., Andronic, O., Arthur, Y. D., Aydın, F., Badran, A., Balbontín-Alvarado, R., Ben Saad, H., Bencsik, A., … Aristovnik, A. (2025). Higher education students' perceptions of ChatGPT: A global study of early reactions. PloS one, 20(2), e0315011. https://doi.org/10.1371/journal.pone.0315011
  36. Rezazadeh, H., Ahmadipour, H., & Salajegheh, M. (2023). Psychometric evaluation of Persian version of medical artificial intelligence readiness scale for medical students. BMC Medical Education, 23, 1-7. https://doi.org/10.1186/s12909-023-04516-6
  37. Richardson, R., Schultz, J. M., & Crawford, K. (2019). Dirty data, bad predictions: How civil rights violations impact police data, predictive policing systems, and justice. NYUL Review Online, 94, 15.
  38. Southworth, J., Migliaccio, K., Glover, J., Glover, J. N., Reed, D., McCarty, C.,. & Thomas, A. (2023). Developing a model for AI across the curriculum: Transforming the higher education landscape via innovation in AI literacy. Computers and Education: Artificial Intelligence, 4, 100127. https://doi.org/10.1016/j.caeai.2023.100127
  39. Verdoliva, L. (2020). Media forensics and deepfakes: An overview. IEEE Journal of Selected Topics in Signal Processing, 14(5), 910–932. https://doi.org/10.1109/JSTSP.2020.3002101
  40. Xuan, P. Y., Fahumida, M. I. F., Al Nazir Hussain, M. I., Jayathilake, N. T., Khobragade, S., Soe, H. H. K., Moe, S., & Htay, M. N. N. (2023). Readiness towards artificial intelligence among undergraduate medical students in Malaysia. Education in Medicine Journal, 15(2), 49–60. https://doi.org/10.21315/eimj2023.15.2.4
  41. Gordon, M., Daniel, M., Ajiboye, A., Uraiby, H., Xu, N. Y., Bartlett, R.,. & Thammasitboon, S. (2024). A scoping review of artificial intelligence in medical education: BEME guide no. 84. Medical Teacher, 46(4), 446–470. https://doi.org/10.1080/0142159X.2024.2314198
  42. Pinto dos Santos, D., Giese, D., Brodehl, S., Chon, S. H., Staab, W., Kleinert, R.,. & Baeßler, B. (2019). Medical students' attitude towards artificial intelligence: A multicentre survey. European Radiology, 29(4), 1640–1646. https://doi.org/10.1007/s00330-018-5601-1
  43. Southworth, J., Migliaccio, K., Glover, J., Glover, J. N., Reed, D., McCarty, C.,. & Thomas, A. (2023). Developing a model for AI across the curriculum: Transforming the higher education landscape via innovation in AI literacy. Computers and Education: Artificial Intelligence, 4, 100127. https://doi.org/10.1016/j.caeai.2023.100127