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Evaluating Dental Students' Perspectives on Artificial Intelligence (AI)-Driven Large Language Models in Education in
Khalifa S Al-Khalifa1, Walaa Magdy Ahmed2, Amr Ahmed Azhari2
1Department of Preventive Dental Sciences, College of Dentistry, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia.
Objectives:
This study explores the perspectives of dental students in Saudi Arabia regarding the integration of large language models (LLMs) in dental education. It aims to understand their familiarity, utilisation and perceptions of these tools, while addressing the potential benefits, risks, and ethical considerations associated with their use.
Methods:
A cross-sectional survey was conducted between January and March 2024, involving 1370 dental students from various institutions across Saudi Arabia. The survey included multiple-choice questions and Likert scale items, assessing familiarity, usage patterns, and perceptions of LLMs. Statistical analyses were performed to identify significant associations between demographic variables and students' familiarity, utilisation, and perceptions of LLMs.
Results:
The survey revealed broad familiarity with LLMs, with 58.1% of participants being aware of their capabilities. Usage patterns varied, with high engagement in tools like ChatGPT and Google Translate. Significant gender, educational level and regional differences were observed, with males and postgraduate residents showing higher familiarity and utilisation rates. However, ethical concerns, including the potential for plagiarism and academic dishonesty, were prevalent, with only 15% of students reporting that their institutions had specific guidelines for LLM use.
Conclusion:
The study highlights the need for a controlled and ethically informed approach to integrating LLMs into dental education. While LLMs offer potential benefits, their use must be regulated to prevent misuse and ensure that educational outcomes are enhanced rather than compromised. Institutions should develop clear guidelines, provide ethical training and emphasise the importance of critical evaluation when using LLMs.
