Evidence map›Paper›PMID 42351276›Full record

ArticleAdvances in simulation (London, England)2026

Artificial intelligence to support debriefing in simulation-based healthcare education: a scoping review.

Aseelah Alnazawi, Mohammed Almarhabi

Abstract read
In one paragraph

Article in Advances in simulation (London, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

2 authors.

Aseelah AlnazawiJeddah Clinical Skills and Simulation Center, College of Medicine, King Saud Bin Abdulaziz University for Health Sciences (KSAU-HS), King Abdulaziz Medical City, Jeddah, Saudi Arabia. aseelah.alnazawi@kcl.ac.uk.ORCID http://orcid.org/0009-0003-4771-7041
Mohammed AlmarhabiFlorence Nightingale Faculty of Nursing, Midwifery & Palliative Care, King's College London, 57 Waterloo Road, London, SE1 8WA, United Kingdom.ORCID http://orcid.org/0009-0009-2529-773X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial intelligence is increasingly being integrated into healthcare education and simulation-based education. However, its role in supporting the debriefing phase of simulation remains underexplored and inconsistently described. This scoping review aimed to map the existing literature on the use of artificial intelligence to support debriefing in simulation-based healthcare education.

methodsA scoping review was conducted in accordance with Arksey and O'Malley's framework and Joanna Briggs Institute guidance and reported in line with PRISMA-ScR. MEDLINE, Scopus, Web of Science, and CINAHL were searched without date restrictions. Eligible studies examined the use of artificial intelligence to support debriefing-related processes within healthcare simulation. Data were charted using a structured extraction form and synthesised descriptively and thematically.

resultsSeven studies published between 2023 and 2026 met the inclusion criteria. Studies were conducted in the United States, Switzerland, Chile, and South Korea. Artificial intelligence applications clustered into three domains: communication and performance analytics using speech recognition and natural language processing; generative artificial intelligence systems supporting facilitator feedback and structured report generation; and learner-facing reflective dialogue systems. Across the included studies, artificial intelligence was mainly positioned as an adjunct to human facilitation rather than as a replacement for facilitators. Reported outcomes focused primarily on feasibility, usability, technical accuracy, and perceived educational value, with limited evidence of objective improvements in learner performance or clinical outcomes.

conclusionsArtificial intelligence is emerging as a supportive tool for debriefing in simulation-based healthcare education. Current evidence remains limited, exploratory, and largely single-institutional, indicating the need for more rigorous research on educational effectiveness, ethical implementation, and the continuing role of human facilitation.

Indexed as

Artificial intelligenceDebriefingHealthcare simulationHealth professions educationScoping reviewSimulation-based education

Identifiers

PMID42351276
PMCPMC13560297

What OpenQuestion holds

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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.