Knowledge, Attitudes, and Perceptions of Artificial Intelligence among Yemeni Dental Students and Interns: A Multi-Institutional Cross-Sectional Study
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Abstract
Background: Artificial intelligence (AI) is rapidly entering dental education and practice, yet preparedness in low-resource settings is unclear.
Objective: This study aims to assess the existing knowledge, attitude, and perception levels of incorporation of AI into dental education and practice among Yemeni students.
Methods: We conducted a descriptive cross-sectional survey (January–March 2023) among clinical-year dental students and interns at three Yemeni institutions (Sana’a University, University of Aden, and University of Science and Technology). A validated questionnaire assessed demographics, AI knowledge (scored items), perceptions/attitudes (Likert scales), and training needs. Descriptive statistics, χ² tests, and planned multivariable models were used; significance was set at p < 0.05.
Results: Of the 761 participants (mean age 24.8±4.2 years), most lived in cities and were either fifth-year students or interns. A substantial correlation (p < 0.05) was found between the participants' feelings towards artificial intelligence (AI), their degree of university education, and their interaction with online social networks. Levels of AI expertise varied among regions. Scores were significantly higher for participants residing in Sana'a and outside of Yemen compared to those from other locations (p=0.012). None of the other demographic variables significantly altered these outcomes.
Conclusion: As a whole, dentistry students in Yemen have positive impressions of AI. However, their levels of comprehension varied greatly between regions, indicating that not everyone had the same exposure to and access to instructional resources about AI.
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Artificial intelligence; dental education; students; perceptions; knowledge; Yemen; curriculum
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