{"title":"Generative AI tools in reflective essays: Moderating moral injuries and epistemic injustices.","authors":"Nontsikelelo O Mapukata","doi":"10.4102/safp.v67i1.6123","DOIUrl":null,"url":null,"abstract":"<p><p>The emergence of large language models such as ChatGPT is already influencing health care delivery, research and training for the next cohort of health care professionals. In a consumer-driven market, their capabilities to generate new forms of knowing and doing for experts and novices present both promises and threats to the livelihood of patients. This article explores burdens imposed by the use of generative artificial intelligence tools in reflective essays submitted by a fifth of first-year health sciences students. In a curriculum centred around Vision 2030 at a South African university, deviations from prescribed guidelines in an essay requiring students to demonstrate an understanding of the models of disability are presented as moral injuries and epistemic injustices. Considering our obligations as educators to contribute to a humanising praxis, the author evaluates an eroded trust between educators and students and offers an interim solution for attaining skills in academic literacy in a developing country.Contribution: This article provides health sciences educators with an opportunity to pause and reflect on how they would like to integrate generative AI tools into their assessments.</p>","PeriodicalId":22040,"journal":{"name":"South African Family Practice","volume":"67 1","pages":"e1-e6"},"PeriodicalIF":1.4000,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12421476/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"South African Family Practice","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4102/safp.v67i1.6123","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MEDICINE, GENERAL & INTERNAL","Score":null,"Total":0}
引用次数: 0
Abstract
The emergence of large language models such as ChatGPT is already influencing health care delivery, research and training for the next cohort of health care professionals. In a consumer-driven market, their capabilities to generate new forms of knowing and doing for experts and novices present both promises and threats to the livelihood of patients. This article explores burdens imposed by the use of generative artificial intelligence tools in reflective essays submitted by a fifth of first-year health sciences students. In a curriculum centred around Vision 2030 at a South African university, deviations from prescribed guidelines in an essay requiring students to demonstrate an understanding of the models of disability are presented as moral injuries and epistemic injustices. Considering our obligations as educators to contribute to a humanising praxis, the author evaluates an eroded trust between educators and students and offers an interim solution for attaining skills in academic literacy in a developing country.Contribution: This article provides health sciences educators with an opportunity to pause and reflect on how they would like to integrate generative AI tools into their assessments.
期刊介绍:
South African Family Practice (SAFP) is a peer-reviewed scientific journal, which strives to provide primary care physicians and researchers with a broad range of scholarly work in the disciplines of Family Medicine, Primary Health Care, Rural Medicine, District Health and other related fields. SAFP publishes original research, clinical reviews, and pertinent commentary that advance the knowledge base of these disciplines. The content of SAFP is designed to reflect and support further development of the broad basis of these disciplines through original research and critical review of evidence in important clinical areas; as well as to provide practitioners with continuing professional development material.