Tag: News Approved

Background: The growing integration of artificial intelligence (AI) in higher education has transformed learning processes but also raised concerns about potential mental health risks. Medical students represent a particularly vulnerable group due to high academic stress and increasing reliance on generative AI tools for study and decision-making tasks. Despite this, the relationship between AI dependence and psychological distress remains underexplored in Latin American contexts. Objective: This study aimed to evaluate the association between generative AI dependence and levels of stress, anxiety, and depression among medical students. Methods: A cross-sectional study was conducted with 187 human medicine students from a Peruvian university during the first academic semester of 2025. The Dependence on Artificial Intelligence Scale and the Depression, Anxiety, and Stress Scale–21 were applied. Negative binomial regression models, both crude and adjusted for sex, age, income, and year of study, were used to assess associations, reporting rate ratios (RRs) and 95% CIs. Results: Participants had a median age of 22 (IQR 19‐24) years, and 58.8% (110/187) were female. The median Dependence on Artificial Intelligence Scale score was 10 (IQR 7‐14). Generative AI dependence showed significant correlations with anxiety (ρ=0.336, 95% CI 0.22‐0.44) and depression (ρ=0.316, 95% CI 0.20‐0.43) and a smaller correlation with stress (ρ=0.277, 95% CI 0.16‐0.39). In the adjusted regression models, each 1-point increase in generative AI dependence was associated with a 5% higher expected anxiety score (RR 1.05, 95% CI 1.01‐1.09; =.01) and a 4% higher depression score (RR 1.04, 95% CI 1.01‐1.08; =.03), whereas the association with stress was positive but nonsignificant (RR 1.03, 95% CI 1.00‐1.07; =.08). Fifth-year students had significantly greater anxiety levels than their sixth-year peers (RR 1.82, 95% CI 1.09‐3.01; =.02). No significant effects were observed for sex, age, or income. Conclusions: This study empirically examined generative AI dependence as a distinct behavioral construct and its association with mental health symptoms in medical students. Unlike prior research, this study evaluated psychological dependence on generative AI and modeled its relationship with anxiety and depression using appropriate count-based regression techniques. By providing early evidence from a Latin American context, it contributes to the emerging field of digital mental health and medical education research. These findings underscore the need for universities to promote balanced and responsible AI use, integrate digital literacy with mental health support strategies, and develop preventive policies that mitigate potential maladaptive reliance on generative AI tools.
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