| Autor: |
Aravind P Gandhi, Felista Karen Joesph, Vineeth Rajagopal, P Aparnavi, Sushma Katkuri, Sonal Dayama, Prakasini Satapathy, Mahalaqua Nazli Khatib, Shilpa Gaidhane, Quazi Syed Zahiruddin, Ashish Behera |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
JMIR Formative Research, Vol 8, p e49964 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2561-326X |
| DOI: |
10.2196/49964 |
| Popis: |
BackgroundMedical students may increasingly use large language models (LLMs) in their learning. ChatGPT is an LLM at the forefront of this new development in medical education with the capacity to respond to multidisciplinary questions. ObjectiveThe aim of this study was to evaluate the ability of ChatGPT 3.5 to complete the Indian undergraduate medical examination in the subject of community medicine. We further compared ChatGPT scores with the scores obtained by the students. MethodsThe study was conducted at a publicly funded medical college in Hyderabad, India. The study was based on the internal assessment examination conducted in January 2023 for students in the Bachelor of Medicine and Bachelor of Surgery Final Year–Part I program; the examination of focus included 40 questions (divided between two papers) from the community medicine subject syllabus. Each paper had three sections with different weightage of marks for each section: section one had two long essay–type questions worth 15 marks each, section two had 8 short essay–type questions worth 5 marks each, and section three had 10 short-answer questions worth 3 marks each. The same questions were administered as prompts to ChatGPT 3.5 and the responses were recorded. Apart from scoring ChatGPT responses, two independent evaluators explored the responses to each question to further analyze their quality with regard to three subdomains: relevancy, coherence, and completeness. Each question was scored in these subdomains on a Likert scale of 1-5. The average of the two evaluators was taken as the subdomain score of the question. The proportion of questions with a score 50% of the maximum score (5) in each subdomain was calculated. ResultsChatGPT 3.5 scored 72.3% on paper 1 and 61% on paper 2. The mean score of the 94 students was 43% on paper 1 and 45% on paper 2. The responses of ChatGPT 3.5 were also rated to be satisfactorily relevant, coherent, and complete for most of the questions (>80%). ConclusionsChatGPT 3.5 appears to have substantial and sufficient knowledge to understand and answer the Indian medical undergraduate examination in the subject of community medicine. ChatGPT may be introduced to students to enable the self-directed learning of community medicine in pilot mode. However, faculty oversight will be required as ChatGPT is still in the initial stages of development, and thus its potential and reliability of medical content from the Indian context need to be further explored comprehensively. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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