ChatGPT versus a customized AI chatbot (Anatbuddy) for anatomy education: a comparative pilot study
Large Language Models (LLMs) have the potential to improve education by personalizing learning. However, ChatGPT-generated content has been criticized for sometimes producing false, biased, and/or hallucinatory information. To evaluate AI's ability to return clear and accurate anatomy informati...
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sg-ntu-dr.10356-1804082024-10-13T15:38:05Z ChatGPT versus a customized AI chatbot (Anatbuddy) for anatomy education: a comparative pilot study Arun, Gautham Perumal, Vivek Urias, Francis Paul John Bato Ler, Yan En Tan, Bryan Wen Tao Vallabhajosyula, Ranganath Tan, Emmanuel Ng, Olivia Ng, Kian Bee Mogali, Sreenivasulu Reddy Lee Kong Chian School of Medicine (LKCMedicine) Medicine, Health and Life Sciences Anatomical education Artificial intelligence Large Language Models (LLMs) have the potential to improve education by personalizing learning. However, ChatGPT-generated content has been criticized for sometimes producing false, biased, and/or hallucinatory information. To evaluate AI's ability to return clear and accurate anatomy information, this study generated a custom interactive and intelligent chatbot (Anatbuddy) through an Open AI Application Programming Interface (API) that enables seamless AI-driven interactions within a secured cloud infrastructure. Anatbuddy was programmed through a Retrieval Augmented Generation (RAG) method to provide context-aware responses to user queries based on a predetermined knowledge base. To compare their outputs, various queries (i.e., prompts) on thoracic anatomy (n = 18) were fed into Anatbuddy and ChatGPT 3.5. A panel comprising three experienced anatomists evaluated both tools' responses for factual accuracy, relevance, completeness, coherence, and fluency on a 5-point Likert scale. These ratings were reviewed by a third party blinded to the study, who revised and finalized scores as needed. Anatbuddy's factual accuracy (mean ± SD = 4.78/5.00 ± 0.43; median = 5.00) was rated significantly higher (U = 84, p = 0.01) than ChatGPT's accuracy (4.11 ± 0.83; median = 4.00). No statistically significant differences were detected between the chatbots for the other variables. Given ChatGPT's current content knowledge limitations, we strongly recommend the anatomy profession develop a custom AI chatbot for anatomy education utilizing a carefully curated knowledge base to ensure accuracy. Further research is needed to determine students' acceptance of custom chatbots for anatomy education and their influence on learning experiences and outcomes. Nanyang Technological University Submitted/Accepted version This project is supported by a Special Project Grant from the Medical Education, Research and Scholarship Unit, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore. 2024-10-07T05:08:00Z 2024-10-07T05:08:00Z 2024 Journal Article Arun, G., Perumal, V., Urias, F. P. J. B., Ler, Y. E., Tan, B. W. T., Vallabhajosyula, R., Tan, E., Ng, O., Ng, K. B. & Mogali, S. R. (2024). ChatGPT versus a customized AI chatbot (Anatbuddy) for anatomy education: a comparative pilot study. Anatomical Sciences Education, 17(7), 1396-1405. https://dx.doi.org/10.1002/ase.2502 1935-9772 https://hdl.handle.net/10356/180408 10.1002/ase.2502 39169464 2-s2.0-85201691243 7 17 1396 1405 en Anatomical Sciences Education © 2024 American Association for Anatomy. All rights reserved. This article may be downloaded for personal use only. Any other use requires prior permission of the copyright holder. The Version of Record is available online at http://doi.org/10.1002/ase.2502. application/pdf |
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Medicine, Health and Life Sciences Anatomical education Artificial intelligence Arun, Gautham Perumal, Vivek Urias, Francis Paul John Bato Ler, Yan En Tan, Bryan Wen Tao Vallabhajosyula, Ranganath Tan, Emmanuel Ng, Olivia Ng, Kian Bee Mogali, Sreenivasulu Reddy ChatGPT versus a customized AI chatbot (Anatbuddy) for anatomy education: a comparative pilot study |
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Large Language Models (LLMs) have the potential to improve education by personalizing learning. However, ChatGPT-generated content has been criticized for sometimes producing false, biased, and/or hallucinatory information. To evaluate AI's ability to return clear and accurate anatomy information, this study generated a custom interactive and intelligent chatbot (Anatbuddy) through an Open AI Application Programming Interface (API) that enables seamless AI-driven interactions within a secured cloud infrastructure. Anatbuddy was programmed through a Retrieval Augmented Generation (RAG) method to provide context-aware responses to user queries based on a predetermined knowledge base. To compare their outputs, various queries (i.e., prompts) on thoracic anatomy (n = 18) were fed into Anatbuddy and ChatGPT 3.5. A panel comprising three experienced anatomists evaluated both tools' responses for factual accuracy, relevance, completeness, coherence, and fluency on a 5-point Likert scale. These ratings were reviewed by a third party blinded to the study, who revised and finalized scores as needed. Anatbuddy's factual accuracy (mean ± SD = 4.78/5.00 ± 0.43; median = 5.00) was rated significantly higher (U = 84, p = 0.01) than ChatGPT's accuracy (4.11 ± 0.83; median = 4.00). No statistically significant differences were detected between the chatbots for the other variables. Given ChatGPT's current content knowledge limitations, we strongly recommend the anatomy profession develop a custom AI chatbot for anatomy education utilizing a carefully curated knowledge base to ensure accuracy. Further research is needed to determine students' acceptance of custom chatbots for anatomy education and their influence on learning experiences and outcomes. |
author2 |
Lee Kong Chian School of Medicine (LKCMedicine) |
author_facet |
Lee Kong Chian School of Medicine (LKCMedicine) Arun, Gautham Perumal, Vivek Urias, Francis Paul John Bato Ler, Yan En Tan, Bryan Wen Tao Vallabhajosyula, Ranganath Tan, Emmanuel Ng, Olivia Ng, Kian Bee Mogali, Sreenivasulu Reddy |
format |
Article |
author |
Arun, Gautham Perumal, Vivek Urias, Francis Paul John Bato Ler, Yan En Tan, Bryan Wen Tao Vallabhajosyula, Ranganath Tan, Emmanuel Ng, Olivia Ng, Kian Bee Mogali, Sreenivasulu Reddy |
author_sort |
Arun, Gautham |
title |
ChatGPT versus a customized AI chatbot (Anatbuddy) for anatomy education: a comparative pilot study |
title_short |
ChatGPT versus a customized AI chatbot (Anatbuddy) for anatomy education: a comparative pilot study |
title_full |
ChatGPT versus a customized AI chatbot (Anatbuddy) for anatomy education: a comparative pilot study |
title_fullStr |
ChatGPT versus a customized AI chatbot (Anatbuddy) for anatomy education: a comparative pilot study |
title_full_unstemmed |
ChatGPT versus a customized AI chatbot (Anatbuddy) for anatomy education: a comparative pilot study |
title_sort |
chatgpt versus a customized ai chatbot (anatbuddy) for anatomy education: a comparative pilot study |
publishDate |
2024 |
url |
https://hdl.handle.net/10356/180408 |
_version_ |
1814777736474918912 |