BMC Med Educ. 2026 Feb 9. doi: 10.1186/s12909-026-08699-6. Online ahead of print.
ABSTRACT
AIMS: To explore undergraduate dental students’ AI knowledge, perceptions, and concerns, and to identify their educational needs based on these findings.
METHODS: A cross-sectional, anonymous survey was conducted using a 30-item online questionnaire distributed to dental schools across multiple countries. The survey employed an exploratory, observational approach with convenience and snowball sampling methods. The population included dental students from all academic semesters, and participation in the survey was voluntary. The questionnaire consisted of multiple-choice and Likert-scale questions organized into five sections: consent form, demographic data, knowledge/awareness, perceptions/attitudes, and ethics-related questions. Data were analysed using Jamovi and R. Descriptive statistics summarised the demographic characteristics and responses to survey questions. Non-parametric correlation analysis was employed as a primary measure of association for relationships between ordinal variables. For regression analyses, ordinal logistic regression models were constructed to identify predictors for specific outcomes. For each Likert scale question, an ordinal logistic regression model was constructed (dependent variable), with the knowledge questions score as a covariate and the nominal answered questions as factors.
RESULTS: 508 students completed the questionnaire. Most students (76.2%) agreed they understood what AI entails, and 67.4% were familiar with generic AI tools; however, only 34.7% knew AI’s dental applications. 70.3% supported AI education during undergraduate studies, favoring case-based teaching, and 53.7% felt their current education had not adequately prepared them for AI technologies. Students declared that AI would be beneficial in diagnostic analysis (64.5%), enhance clinical practice (69.5%), and improve patient care (60.4%). Also, 41.7% believed that AI would cause a reduction in professionals’ skills and dehumanize healthcare (29.2%). 3/4 students agreed that AI ethics should be taught from a multidisciplinary perspective, and 65.3% declared AI in healthcare should be legally regulated.
CONCLUSIONS: This study establishes baseline data on dental students’ AI knowledge and educational requirements across multiple countries. Despite general AI familiarity, understanding of dental applications remains limited. The results highlight the need for structured AI education programs tailored to students’ knowledge gaps and learning preferences. Dental students’ understanding and perceptions of AI can effectively guide the identification of their learning needs and inform curriculum integration.
PMID:41652399 | DOI:10.1186/s12909-026-08699-6
