Untold stories of digital transformation in medical education: AI overdependence and nomophobia among medical students

Korkmaz G., TORAMAN Ç.

BMC Medical Education, vol.26, no.1, 2026 (SCI-Expanded, SSCI, Scopus) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 26 Issue: 1
  • Publication Date: 2026
  • Doi Number: 10.1186/s12909-026-08675-0
  • Journal Name: BMC Medical Education
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus, MEDLINE, Directory of Open Access Journals
  • Keywords: Artificial intelligence, Faculty of medicine, Medical education, Medical student, Nomophobia
  • Çanakkale Onsekiz Mart University Affiliated: Yes

Abstract

Background: This study aims to investigate the relationship between AI dependence and nomophobia among medical students based on various demographic characteristics. Methods: This study adopts a quantitative correlational research design. Dependence on Artificial Intelligence Scale (DAI) and the Nomophobia Questionnaire (NMP-Q) were used to collect data. Data were collected online via Google Forms. Medical students from four public and two private universities in Turkey participated in the study. Data were analyzed using Pearson correlation analysis, Two-Way ANOVA, and Multiple Regression Analysis. Results: The results revealed a significant positive correlation between AI dependence and nomophobia among medical students. Students who reported higher levels of AI dependence were also more likely to exhibit signs of smartphone addiction and internet access dependency. Demographic factors, such as gender, age and year of study, were found to have significant effects on the relationship. Conclusions: The study highlights a significant relationship between AI dependence and nomophobia among medical students, suggesting that overreliance on AI technologies may contribute to several negative consequences.