In this article I discuss the ethical ramifications for medical ethics training of the availability of large language models (LLMs) for medical students. My focus is on the practical ethical consequences for what we should expect of medical students in terms of medical professionalism and ethical reasoning, and how this can be tested in a context where LLMs are relatively easy available. If we continue to expect ethical competences of medical professionalism of future physicians, how much – if at all – should we worry that such generative AI may compromise adequate testing of medical students’ abilities in this regard? I mainly focus on assessment methods based on written assignments of the ‘student paper’ type and consider whether LLMs make it unfeasible for assessors to gauge whether output is student-generated or ‘machine-generated’ and, if so, whether this is a problem. My take on this research question unfolds in three interwoven arguments, claiming that the advent of LLMs may offer a momentum (i) to reaffirm the importance of context-sensitive interpretation and specification of ethical principles in medical ethics training, (ii) to provide more supportive circumstances to assessors to allow them to meet scoring demands entailed by the importance that is placed on medical professionalism, and (iii) to complement written assignments with verbal (group) discussion to train and test students’ skills to habitually recognize that ‘moral solutions’ can be normatively questioned and specified from various perspectives.
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Segers, S. Why we should (not) worry about generative AI in medical ethics teaching. International Journal of Ethics Education (2023). https://doi.org/10.1007/s40889-023-00179-5