Evidence map›Paper›PMID 40600244›Full record

ArticleMedical education2025

Can educators distinguish between medical student and generative AI-authored reflections?

Constance Wraith, Alasdair Carnegy, Celia Brown, Ana Baptista, Amir H Sam

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Article in Medical education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

5 authors.

Constance WraithImperial College School of Medicine, London, UK.ORCID 0009-0009-6058-4363
Alasdair CarnegyImperial College School of Medicine, London, UK.ORCID 0009-0004-0265-2034
Celia BrownImperial College School of Medicine, London, UK.ORCID 0000-0002-7526-0793
Ana BaptistaImperial College School of Medicine, London, UK.ORCID 0000-0002-9157-6464
Amir H SamImperial College School of Medicine, London, UK.ORCID 0000-0002-9599-9069

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionReflection is integral to the modern doctor's practice and, whilst it can take many forms, written reflection is commonly found on medical school curricula. Generative artificial intelligence (GenAI) is increasingly being used, including in the completion of written assignments in medical curricula. We sought to explore if educators can distinguish between GenAI- and student-authored reflections and what features they use to do so.

methodsThis was a mixed-methods study. Twenty-eight educators attended a 'think aloud' interview and were presented with a set of four reflections, either all authored by students, all by GenAI or a mixture. They were asked to identify who they thought had written the reflection, speaking aloud whilst they did so. Sensitivity (AI reflections correctly identified) and specificity (student reflections correctly identified) were then calculated, and the interview transcripts were analysed using thematic analysis.

resultsEducators were unable to reliably distinguish between student and GenAI-authored reflections. Sensitivity across the four reflections ranged from 0.36 (95% CI: 0.16-0.61) to 0.64 (95% CI: 0.39-0.84). Specificity ranged from 0.64 (95% CI: 0.39-0.84) to 0.86 (95% CI: 0.60-0.96). Thematic analysis revealed three main themes when considering what features of the reflection educators used to make judgements about authorship: features of writing, features of reflection and educators' preconceptions and experiences. DISCUSSION: This study demonstrates the challenges in differentiating between student- and GenAI-authored reflections, as well as highlighting the range of factors that influence this decision. Rather than developing ways to more accurately make this distinction or trying to stop students using GenAI, we suggest it could instead be harnessed to teach students reflective practice skills, and help students for whom written reflection in particular may be challenging.

Indexed as

Artificial IntelligenceAuthorshipFaculty, MedicalStudents, MedicalWritingCurriculumEducation, Medical, UndergraduateFemaleHumansMale

Identifiers

PMID40600244
PMCPMC12513547

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.