MD Student Perceptions of ChatGPT for Reflective Writing Feedback in Undergraduate Medical Education

Abstract

At the Michael G. DeGroote School of Medicine, a significant component of the MD curriculum involves written narrative reflections on topics related to professional identity in medicine, with written feedback provided by their in-person longitudinal facilitators (LFs). However, it remains to be understood how generative artificial intelligence chatbots, such as ChatGPT (GPT-4), augment the feedback process and how MD students perceive feedback provided by ChatGPT versus the feedback provided by their LFs. In this study, 15 MD students provided their written narrative reflections along with the feedback they received from their LFs. Their reflections were input into ChatGPT (GPT-4) to generate instantaneous personalized feedback. MD students rated both modalities of feedback using a Likert-scale survey, in addition to providing open-ended textual responses. Quantitative analysis involved mean comparisons and t-tests, while qualitative responses were coded for themes and representational quotations. The results showed that while the LF-provided feedback was rated slightly higher in six out of eight survey items, these differences were not statistically significant. In contrast, ChatGPT scored significantly higher in helping to identify strengths and areas for improvement, as well as in providing actionable steps for improvement. Criticisms of ChatGPT included a discernible “AI tone” and paraphrasing or misuse of quotations from the reflections. In addition, MD students valued LF feedback for being more personal and reflective of the real, in-person relationships formed with LFs. Overall, findings suggest that although skepticism regarding ChatGPT’s feedback exists amongst MD students, it represents a viable avenue for deepening reflective practice and easing some of the burden on LFs.

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