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Preparing future dentists for artificial intelligence: a cross-sectional study of perceptions and educational needs
Yanru Wu1, Yusheng Bao1, Ze Jiang1
1State Key Laboratory of Oral and Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School and Hospital of Stomatology, Wuhan University, Wuhan, China.
Dental students broadly support artificial intelligence (AI) in care and education, but their understanding and concerns vary by training level. Tailored AI education is crucial for dental students across undergraduate, master's, and doctoral programs.
Area of Science:
- Dental Education
- Artificial Intelligence
- Medical Technology
Background:
- Artificial intelligence (AI) integration into dental education and practice is increasing.
- Understanding dental students' perceptions of AI's use, risks, and educational value is limited.
- This study investigates AI familiarity, attitudes, concerns, and learning needs across different dental student training stages.
Purpose of the Study:
- To examine AI-related familiarity, attitudes, concerns, and educational needs among undergraduate, master's, and doctoral dental students.
- To identify differences in perceptions and learning requirements based on dental training stage.
- To inform the development of stage-specific AI curricula for dental education.
Main Methods:
- A cross-sectional online survey was administered to dental students at Wuhan University.
- The 20-item questionnaire assessed AI exposure, perceived applications, attitudes, concerns, responsibility attribution, and educational needs.
- Descriptive statistics and Pearson's chi-square tests were used to analyze data and compare training stages.
Main Results:
- 343 dental students (201 undergraduate, 92 master's, 50 doctoral) participated.
- Most students supported AI in dental care (89.8%) and were interested in AI education (84.2%).
- Significant differences emerged in information sources, application perceptions, responsibility attribution, and concerns (data privacy, interpretability, dependence) across training levels.
Conclusions:
- Dental students exhibit general openness to AI, with varying perceptions and educational needs across training stages.
- AI education in dentistry should be progressively aligned with student training levels.
- Future research should include multicenter, longitudinal studies with practical assessments to track AI competence development.