Assessment of dependence on artificial intelligence and associated factors among medical students

Öz

This study aimed to assess the level of dependence on artificial intelligence (AI) among medical students and to identify its associated factors. The study population of this cross-sectional study consisted of 236 medical students in years 1–4 at a university in November 2025. Data were collected using a questionnaire that included sociodemographic characteristics, AI usage characteristics, the Scale for Dependence on AI (DAI), the Academic Self-Efficacy Scale (ASES), and the Multidimensional Scale of Perceived Social Support (MSPSS). The mean age of the participants was 20.02±1.65 years, and 49.2% were male. DAI scores ranged from 5 to 25, with a mean of 11.04±4.08. Higher mean DAI scores were observed among students who used AI for more than 30 minutes daily, checked AI without intending to use it, and used it for academic and social chatting/emotional sharing purposes (p<0.05). DAI scores showed a weak negative correlation with ASES scores (r=-0.235) and a moderate negative correlation with MSPSS scores (r=-0.376; p<0.001). In hierarchical regression analysis, dependence on AI was associated with daily AI use duration, checking AI without intention, using AI for academic and social chatting/emotional sharing purposes, academic self-efficacy, and perceived social support. In line with these findings, raising awareness, promoting balanced use, and strengthening academic self-efficacy and social support may be beneficial in reducing the risk of AI dependence.

Anahtar Kelimeler

• Artificial Intelligence
• dependence
• medical students
• academic self-efficacy
• perceived social support

Tıp öğrencilerinde yapay zekaya bağımlılık ve ilişkili faktörlerin değerlendirilmesi

Öz

Çalışmada tıp öğrencilerinin yapay zekaya (YZ) bağımlılık düzeyleri ve ilişkili faktörlerin değerlendirilmesi amaçlandı. Kesitsel tasarımdaki çalışmanın araştırma grubunu, Kasım 2025’te bir üniversitede öğrenim gören 1–4. sınıf tıp öğrencisi 236 kişi oluşturmuştur. Veriler sosyodemografik özellikler, yapay zeka kullanım özellikleri ile Yapay Zekaya Bağımlılık Ölçeği (YZBÖ), Akademik Özyeterlik Ölçeği (AÖÖ) ve Çok Boyutlu Algılanan Sosyal Destek Ölçeği (ÇBASDÖ)’nün yer aldığı anket formu ile toplandı. Katılımcıların yaş ortalaması 20,02±1,65 olup, %49,2’si erkekti. YZBÖ puanları 5–25 arasında değişmekte olup ortalaması 11,04±4,08 idi. Yapay zekayı günlük 30 dakikadan fazla kullananlarda, kullanma niyeti olmadığı halde kontrol edenlerde ve akademik ile sosyal sohbet/duygusal paylaşım amaçlı kullananlarda YZBÖ puan ortalaması daha yüksekti (p<0,05). YZBÖ ile AÖÖ arasında zayıf (r=-0,235), ÇBASDÖ ile orta düzeyde negatif ilişki saptandı (r=-0,376; p<0,001). Hiyerarşik doğrusal regresyon analizinde, yapay zekaya bağımlılığın; günlük yapay zekâ kullanım süresi, kullanma niyeti olmadan yapay zekayı kontrol etme, yapay zekayı akademik ve sosyal sohbet/duygusal paylaşım amacıyla kullanma, akademik öz-yeterlik ve algılanan sosyal destek ile ilişkili olduğu bulundu. Bu bulgular doğrultusunda, farkındalığın artırılması, dengeli kullanımın teşvik edilmesi ve akademik öz-yeterlik ile sosyal desteğin güçlendirilmesi, yapay zekâ bağımlılığı riskini azaltmada yararlı olabilir.

Anahtar Kelimeler

• Yapay zeka
• bağımlılık
• tıp öğrencileri
• akademik özyeterlik
• algılanan sosyal destek

Kaynakça

1. Soori M, Arezoo B, Dastres R. Artificial intelligence, machine learning and deep learning in advanced robotics, a review. Cogn Robot. 2023;3:54-70. DOI:10.1016/j.cogr.2023.04.001.
2. Suva M, Bhatia G. Artificial intelligence in addiction: challenges and opportunities. Indian J Psychol Med. 2024 Aug 31:02537176241274148. DOI:10.1177/02537176241274148.
3. Zhang X, Li H, Yin M, Zhang M, Li Z, Chen Z. Investigating AI chatbot dependence: associations with internet and smartphone dependence, mental health outcomes, and the moderating role of usage purposes. Int J Hum Comput Interact. 2025;1-13. DOI:10.1080/10447318.2025.2545464.
4. Sağlam RK, Kalanlar B. Living with and without AI: a mixed-methods study on AI usage, addiction, and ‘AIlessphobia’ in nursing students. Nurse Educ Pract. 2025;88:104530. DOI:10.1016/j.nepr.2025.104530.
5. Zhou T, Zhang C. Examining generative AI user addiction from a CAC perspective. Technol Soc. 2024;78:102653. DOI:10.1016/j. techsoc.2024.102653.
6. Eurostat. 32.7% of EU people used generative AI tools in 2025 [Internet]. Luxembourg: European Commission; 2025 Dec 16 [cited 2026 Mar 24]. Available from: https://ec.europa.eu/eurostat/web/products-eurostat-news/w/ddn-20251216-3
7. Revesai Z. Generative AI dependency: the emerging academic crisis and its impact on student performance—a case study of a university in Zimbabwe. Cogent Educ. 2025;12(1):2549787. DOI :10.1080/2331186X.2025.2549787.
8. Morales-García WC, Sairitupa-Sanchez LZ, Morales-García SB, Morales-García M. Development and validation of a scale for dependence on artificial intelligence in university students. Front Educ. 2024;9:1323898. DOI:10.3389/feduc.2024.1323898.

9. Durmus İ. The role of techno-ethical classroom behavior and metacognitive experiences over time in students’ artificial intelligence addictions. Turk Online J Distance Educ. 2025;26(4):36-54. DOI:10.17718/tojde.1580082.
10. Zhang S, Zhao X, Zhou T, et al. Do you have AI dependency? The roles of academic self-efficacy, academic stress, and performance expectations on problematic AI usage behavior. Int J Educ Technol High Educ. 2024;21:34. DOI:10.1186/s41239-024-00467-0.
11. Hill MR, Goicochea S, Merlo LJ. In their own words: stressors facing medical students in the millennial generation. Med Educ Online. 2018 Dec;23(1):1530558. DOI:10.1080/10872981.2018. 1530558.
12. Nechita F, Nechita D, Pîrlog MC, Rogoveanu I. Stress in medical students. Rom J Morphol Embryol. 2014;55(3 Suppl):1263-6.
13. Korkmaz G, Toraman Ç. Untold stories of digital transformation in medical education: AI overdependence and nomophobia among medical students. BMC Med Educ. 2026;26(1):306. DOI:10.1186/s12909-026-08675-0.
14. Lai L, Pan Y, Xu R, Jiang Y. Depression and the use of conversational AI for companionship among college students: the mediating role of loneliness and the moderating effects of gender and mind perception. Front Public Health. 2025;13:1580826. DOI:10.3389/fpubh.2025.1580826.
15. Zhou M, Hu Y. The effects of loneliness, self-esteem, perceived risk, and perceived threat on users’ willingness to use AI chatbots. Telemat Inform Rep. 2026;21:100291. DOI:10.1016/j.teler.2026.100291.
16. Savaş BÇ. Yapay zekâya bağımlılık ölçeğinin Türkçe’ye uyarlanması: geçerlik ve güvenirlik çalışması. J Sport All Recreat. 2024;6(3):306-15. DOI:10.56639/jsar.1509301.
17. Jerusalem M, Schwarzer R. Fragebogen zur Erfassung von “Selbstwirksamkeit”. In: Schwarzer R, editor. Skalen zur Befindlichkeit und Persoenlichkeit. Berlin: Freie Universitaet, Institut fuer Psychologie; 1981. (Forschungsbericht No. 5).
18. Yılmaz M, Gürçay D, Ekici G. Akademik özyeterlik ölçeğinin Türkçe’ye uyarlanması. Hacettepe Üniversitesi Eğitim Fakültesi Dergisi. 2007;33:253-9.
19. Zimet GD, Dahlem NW, Zimet SG, Farley GK. The multidimensional scale of perceived social support. J Pers Assess. 1988;52(1):30-41. DOI:10.1207/s15327752jpa5201_2.
20. Eker D, Arkar H, Yaldız H. Çok boyutlu algılanan sosyal destek ölçeği’nin gözden geçirilmiş formunun faktör yapısı, geçerlik ve güvenirliği. Türk Psikiyatri Dergisi. 2001;12(1):17-25.
21. Estrada-Araoz EG, Mamani-Roque M, Quispe-Aquise J, Manrique-Jaramillo YV, Cruz-Laricano EO. Academic self-efficacy and dependence on artificial intelligence in a sample of university students. Sapienza Int J Interdiscip Stud. 2025;6(1):e25008. DOI:10.51798/sijis.v6i1.916.
22. Olarewaju Y, Hammed LA, Raheem AO. Impact of artificial intelligence tools on student initiative: examining academic laziness among education undergraduate students at University of Ilorin. J Contemp Educ Res. 2025;7(8):147-58. DOI:10.70382/hujcer.v7i8.012.
23. Fietta V, Zecchinato F, Stasi BD, Polato M, Monaro M. Dissociation between users’ explicit and implicit attitudes toward artificial intelligence: an experimental study. IEEE Trans Hum Mach Syst. 2022;52(3):481-9. DOI:10.1109/THMS.2021.3125280.
24. Kaya F, Aydin F, Schepman A, Rodway P, Yetişensoy O, Demir Kaya M. The roles of personality traits, AI anxiety, and demographic factors in attitudes toward artificial intelligence. Int J Hum Comput Interact. 2024;40(2):497-514. DOI:10.1080/10447318.2022. 2151730.
25. Griffiths M. A ‘components’ model of addiction within a biopsychosocial framework. J Subst Use. 2005;10(4):191-7. DOI:10.1080/14659890500114359.
26. Bányai F, Zsila Á, Király O, Maraz A, Elekes Z, Griffiths MD, et al. Problematic social media use: results from a large-scale nationally representative adolescent sample. PLoS One. 2017;12(1):e0169839. DOI:10.1371/journal.pone.0169839.
27. Oulasvirta A, Rattenbury T, Ma L, et al. Habits make smartphone use more pervasive. Pers Ubiquit Comput. 2012;16:105-14. DOI:10.1007/s00779- 011- 0412-2.
28. Turel O, Serenko A. The benefits and dangers of enjoyment with social networking websites. Eur J Inf Syst. 2012;21(5):512-28. DOI:10.1057/ejis.2012.1.
29. Rahman MM, Watanobe Y. ChatGPT for education and research: opportunities, threats, and strategies. Appl Sci. 2023;13(9):5783. DOI:10.3390/app13095783.
30. Croes EAJ, Antheunis ML, van der Lee C, de Wit JMS. Digital confessions: the willingness to disclose intimate information to a chatbot and its impact on emotional well-being. Interact Comput. 2024;36(5):279-92. DOI:10.1093/iwc/iwae016.
31. Skjuve M, Følstad A, Brandtzæg PB. A longitudinal study of self-disclosure in human–chatbot relationships. Interact Comput. 2023;35(1):24-39. DOI:10.1093/iwc/iwad022.
32. Ali F, Zhang Q, Tauni MZ, Shahzad K. Social chatbot: my friend in my distress. Int J Hum Comput Interact. 2024;40(7):1702-12. DOI:10.1080/10447318.2022. 2150745.
33. Yao R, Qi G, Sheng D, Sun H, Zhang J. Connecting self-esteem to problematic AI chatbot use: the multiple mediating roles of positive and negative psychological states. Front Psychol. 2025;16:1453072. DOI:10.3389/fpsyg.2025.1453072.
34. Bandura A. Self-efficacy: the exercise of control. New York: W.H. Freeman and Company
35. Jackson A, Mentzer N, Kramer-Bottiglio R. Pilot analysis of the impacts of soft robotics design on high-school student engineering perceptions. Int J Technol Des Educ. 2019;29:1083-104. DOI:10.1007/s10798-018-9478-8.
36. Caplan SE. A social skill account of problematic internet use. J Commun. 2005;55:721-36. DOI:10.1111/j.1460-2466.2005.tb03019.x.; 1997. p.36-78.
37. Lei H, Li S, Chiu MM, Lu M. Social support and internet addiction among mainland Chinese teenagers and young adults: a meta-analysis. Comput Hum Behav. 2018;85:200-9. DOI:10.1016/j.chb.2018.03.041.
38. Hatun O, Türk Kurtça T. Examining the relationship between social support and social media addiction: role of self-esteem. Addicta. 2023;10(2):142-7. DOI:10.5152/ADDICTA.2023.23033.
39. Brand M, Young KS, Laier C, Wölfling K, Potenza MN. Integrating psychological and neurobiological considerations regarding the development and maintenance of specific internet-use disorders: an interaction of person-affect-cognition-execution (I-PACE) model. Neurosci Biobehav Rev. 2016 Dec;71:252-66. DOI:10.1016/j. neubiorev.2016.08.033.
40. Brand M, Wegmann E, Stark R, Müller A, Wölfling K, Robbins TW, Potenza MN. The interaction of person-affect-cognition-execution (I-PACE) model for addictive behaviors: update, generalization to addictive behaviors beyond internet-use disorders, and specification of the process character of addictive behaviors. Neurosci Biobehav Rev. 2019 Sep;104:1-110. DOI:10.1016/j.neubiorev.2019.06.032.