ChatGPT sits the DFPH exam: large language model performance and potential to support public health learning

Davies, Nathan P; Wilson, Robert; Winder, Madeleine S; Tunster, Simon J; McVicar, Kathryn; Thakrar, Shivan; Williams, Joe; Reid, Allan

Abstract

Background: Artificial intelligence-based large language models, like ChatGPT, have been rapidly assessed for both risks and potential in health-related assessment and learning. However, their applications in public health professional exams have not yet been studied. We evaluated the performance of ChatGPT in part of the Faculty of Public Health’s Diplomat exam (DFPH). Methods: ChatGPT was provided with a bank of 119 publicly available DFPH question parts from past papers. Its performance was assessed by two active DFPH examiners. The degree of insight and level of understanding apparently displayed by ChatGPT was also assessed. Results: ChatGPT passed 3 of 4 papers, surpassing the current pass rate. It performed best on questions relating to research methods. Its answers had a high floor. Examiners identified ChatGPT answers with 73.6% accuracy and human answers with 28.6% accuracy. ChatGPT provided a mean of 3.6 unique insights per question and appeared to demonstrate a required level of learning on 71.4% of occasions. Conclusions: Large language models have rapidly increasing potential as a learning tool in public health education. However, their factual fallibility and the difficulty of distinguishing their responses from that of humans pose potential threats to teaching and learning.

Citation

Davies, N. P., Wilson, R., Winder, M. S., Tunster, S. J., McVicar, K., Thakrar, S., Williams, J., & Reid, A. (2024). ChatGPT sits the DFPH exam: large language model performance and potential to support public health learning. BMC Medical Education, 24(1), Article 57. https://doi.org/10.1186/s12909-024-05042-9

• Journal Article Type: Article
• Acceptance Date: Jan 6, 2024
• Online Publication Date: Jan 11, 2024
• Publication Date: 2024
• Deposit Date: Jan 12, 2024
• Publicly Available Date: Jan 12, 2024
• Journal: BMC Medical Education
• Electronic ISSN: 1472-6920
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Volume: 24
• Issue: 1
• Article Number: 57
• DOI: https://doi.org/10.1186/s12909-024-05042-9
• Keywords: Public health, Theory, Artificial intelligence, Examination
• Public URL: https://nottingham-repository.worktribe.com/output/29553618
• Publisher URL: https://bmcmededuc.biomedcentral.com/articles/10.1186/s12909-024-05042-9