Publicly accessible conversational artificial intelligence (AI) increasingly informs self-care, yet research among university students has primarily examined awareness, acceptance, and information seeking rather than the progression from AI output to medication action and safety experiences. This cross-sectional study examined 504 University of Jordan students who had previously used AI for self-treatment or self-medication. A 39-item questionnaire assessed symptom consultation, professional verification, medication-related behaviors, and self-reported adverse effects. We derived attitudinal domains using exploratory factor analysis, and modified Poisson regression estimated adjusted prevalence ratios (aPRs). AI was the first source consulted for new symptoms by 36.5% of participants; 42.5% reported purchasing medication following AI advice without consulting a physician or pharmacist (19.0% frequently), 20.0% selected antibiotics following AI-generated information or recommendations, 36.9% reported adverse effects following an AI-recommended medication, and 35.7% modified physician instructions because of conflicting AI advice. Per one-standard-deviation increase, trust/personalization was associated with higher prevalence of all five evaluated outcomes, including any unconsulted purchasing (aPR 1.49, 95% confidence interval 1.33–1.66), whereas risk recognition showed consistent inverse associations (aPR range 0.73–0.80). Physician/pharmacist verification, reported by 64.7%, was associated with lower prevalence of frequent unconsulted purchasing (aPR 0.57, 95% confidence interval 0.40–0.83), reported adverse effects (aPR 0.78, 0.64–0.96), and modification of physician instructions (aPR 0.74, 0.60–0.91), but not with any purchasing or antibiotic selection. Cross-sectional self-report precludes causal attribution. By identifying output appraisal and professional verification as distinct correlates of medication action, this study extends adoption-focused research to clinically relevant behaviors.