Evidence map›Paper›PMID 40922632›Full record

ArticleSouth African family practice : official journal of the South African Academy of Family Practice/Primary Care2025

Generative AI tools in reflective essays: Moderating moral injuries and epistemic injustices.

Nontsikelelo O Mapukata

Abstract read
In one paragraph

Article in South African family practice : official journal of the South African Academy of Family Practice/Primary Care, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Nontsikelelo O MapukataSchool of Public Health, Faculty of Health Sciences, University of Cape Town, Cape Town. ntsiki.mapukata@uct.ac.za.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Artificial IntelligenceMoralsWritingCurriculumHumansSouth Africaacademic literacy skillsartificial intelligenceepistemic injusticehealth sciences studentsmoral injuryreflective essays

Identifiers

PMID40922632
PMCPMC12421476

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.