AI-based medical ethics education: examining the potential of large language models as a tool for virtue cultivation
Abstract Background With artificial intelligence (AI) increasingly revolutionising medicine, this study critically evaluates the integration of large language models (LLMs), known for advanced text processing and generation capabilities, in medical ethics education, focusing on promoting virtue. Pos...
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Format: | Article |
Language: | English |
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BMC
2025-02-01
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Series: | BMC Medical Education |
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Online Access: | https://doi.org/10.1186/s12909-025-06801-y |
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author | Shimpei Okamoto Masanori Kataoka Makoto Itano Tsutomu Sawai |
author_facet | Shimpei Okamoto Masanori Kataoka Makoto Itano Tsutomu Sawai |
author_sort | Shimpei Okamoto |
collection | DOAJ |
description | Abstract Background With artificial intelligence (AI) increasingly revolutionising medicine, this study critically evaluates the integration of large language models (LLMs), known for advanced text processing and generation capabilities, in medical ethics education, focusing on promoting virtue. Positing LLMs as central to mimicking nuanced human communication, it examines their use in medical education and the ethicality of embedding AI in such contexts. Method Using a hybrid approach that combines principlist and non-principlist methodologies, we position LLMs as exemplars and advisors. Results We discuss the imperative for including AI ethics in medical curricula and its utility as an educational tool, identify the lack of educational resources in medical ethics education, and advocate for future LLMs to mitigate this problem as a “second-best” tool. We also emphasise the critical importance of instilling virtue in medical ethics education and illustrate how LLMs can effectively impart moral knowledge and model virtue cultivation. We address expected counter-arguments to using LLMs in this area and explain their profound potential to enrich medical ethics education, including facilitating the acquisition of moral knowledge and developing ethically grounded practitioners. Conclusions The study involved a comprehensive exploration of the function of LLMs in medical ethics education, positing that tools such as ChatGPT can profoundly enhance the learning experience in the future. This is achieved through tailored, interactive educational encounters while addressing the ethical nuances of their use in educational settings. |
format | Article |
id | doaj-art-0a290eda074743249b77f2a5b969d37c |
institution | Kabale University |
issn | 1472-6920 |
language | English |
publishDate | 2025-02-01 |
publisher | BMC |
record_format | Article |
series | BMC Medical Education |
spelling | doaj-art-0a290eda074743249b77f2a5b969d37c2025-02-09T12:42:34ZengBMCBMC Medical Education1472-69202025-02-012511910.1186/s12909-025-06801-yAI-based medical ethics education: examining the potential of large language models as a tool for virtue cultivationShimpei Okamoto0Masanori Kataoka1Makoto Itano2Tsutomu Sawai3Graduate School of Humanities and Social Sciences, Hiroshima UniversityUehiro Division for Applied Ethics, Graduate School of Humanities and Social Sciences, Hiroshima UniversityGraduate School of Humanities and Social Sciences, Hiroshima UniversityGraduate School of Humanities and Social Sciences, Hiroshima UniversityAbstract Background With artificial intelligence (AI) increasingly revolutionising medicine, this study critically evaluates the integration of large language models (LLMs), known for advanced text processing and generation capabilities, in medical ethics education, focusing on promoting virtue. Positing LLMs as central to mimicking nuanced human communication, it examines their use in medical education and the ethicality of embedding AI in such contexts. Method Using a hybrid approach that combines principlist and non-principlist methodologies, we position LLMs as exemplars and advisors. Results We discuss the imperative for including AI ethics in medical curricula and its utility as an educational tool, identify the lack of educational resources in medical ethics education, and advocate for future LLMs to mitigate this problem as a “second-best” tool. We also emphasise the critical importance of instilling virtue in medical ethics education and illustrate how LLMs can effectively impart moral knowledge and model virtue cultivation. We address expected counter-arguments to using LLMs in this area and explain their profound potential to enrich medical ethics education, including facilitating the acquisition of moral knowledge and developing ethically grounded practitioners. Conclusions The study involved a comprehensive exploration of the function of LLMs in medical ethics education, positing that tools such as ChatGPT can profoundly enhance the learning experience in the future. This is achieved through tailored, interactive educational encounters while addressing the ethical nuances of their use in educational settings.https://doi.org/10.1186/s12909-025-06801-yMedical ethicsEducationMorals |
spellingShingle | Shimpei Okamoto Masanori Kataoka Makoto Itano Tsutomu Sawai AI-based medical ethics education: examining the potential of large language models as a tool for virtue cultivation BMC Medical Education Medical ethics Education Morals |
title | AI-based medical ethics education: examining the potential of large language models as a tool for virtue cultivation |
title_full | AI-based medical ethics education: examining the potential of large language models as a tool for virtue cultivation |
title_fullStr | AI-based medical ethics education: examining the potential of large language models as a tool for virtue cultivation |
title_full_unstemmed | AI-based medical ethics education: examining the potential of large language models as a tool for virtue cultivation |
title_short | AI-based medical ethics education: examining the potential of large language models as a tool for virtue cultivation |
title_sort | ai based medical ethics education examining the potential of large language models as a tool for virtue cultivation |
topic | Medical ethics Education Morals |
url | https://doi.org/10.1186/s12909-025-06801-y |
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