Knowledge, attitudes, and expectations of undergraduate dental students regarding Artificial Intelligence in prescription writing: a cross-sectional study

Aims: The aim of this study was to evaluate undergraduate dental students’ knowledge, attitudes, and expectations regarding the use of Artificial Intelligence (AI) in prescription writing and to investigate perceptual differences between pre-clinical and clinical students. Methods: This descriptive observational study included 354 undergraduate dental students. Data were collected using a structured online questionnaire consisting of 15 items assessing AI awareness, perceived impact on prescribing workflows, trust levels, and educational needs. Statistical analysis was performed using descriptive statistics, the Chi-square test, and binary logistic regression (p<0.05). Results: While 97.5% of participants reported prior use of AI tools and 87.9% expected AI-assisted systems to improve clinical efficiency, 59.9% expressed hesitation in trusting AI for appropriate drug selection. A significant divergence was observed by educational stage; pre-clinical students held more optimistic views, whereas clinical students demonstrated greater caution regarding professional responsibility, reliability, and the patient-dentist relationship. Furthermore, 83.1% of students supported integrating AI-related training into the undergraduate dental curriculum. Conclusion: Dental students exhibit a state of “cautious optimism” toward AI-assisted prescription writing. While the potential for reducing errors is widely recognized, greater clinical exposure fosters a more critical perspective toward emerging technologies. These findings underscore the necessity for structured dental curricula that position AI not as a substitute for clinical judgment, but as a supportive “scaffold” (augmented intelligence). Integrating ethical considerations and supervision into education is essential to prepare future clinicians for safe, rational prescribing in the digital era.