Mapping the Landscape of Generative Language Models in Dental Education: A Comparison Between ChatGPT and Google Bard.

Abstract

Generative language models (LLMs) have shown great potential in various fields, including medicine and education. This study evaluated and compared ChatGPT 3.5 and Google Bard within dental education and research.

Methods: We developed seven dental education-related queries to assess each model across various domains: their role in dental education, creation of specific exercises, simulations of dental problems with treatment options, development of assessment tools, proficiency in dental literature and their ability to identify, summarise and critique a specific article. Two blind reviewers scored the responses using defined metrics. The means and standard deviations of the scores were reported, and differences between the scores were analysed using Wilcoxon tests.

Results: ChatGPT 3.5 outperformed Bard in several tasks, including the ability to create highly comprehensive, accurate, clear, relevant and specific exercises on dental concepts, generate simulations of dental problems with treatment options and develop assessment tools. On the other hand, Bard was successful in retrieving real research, and it was able to critique the article it selected. Statistically significant differences were noted between the average scores of the two models (p ≤ 0.05) for domains 1 and 3.

Conclusion: This study highlights the potential of LLMs as dental education tools, enhancing learning through virtual simulations and critical performance analysis. However, the variability in LLMs' performance underscores the need for targeted training, particularly in evidence-based content generation. It is crucial for educators, students and practitioners to exercise caution when considering the delegation of critical educational or healthcare decisions to computer systems.