Evidence map›Paper›PMID 41975412›Full record

ArticleBMC nursing2026

Effects of ChatGPT-generated immediate feedback integrated into VR-based OSCEs on nursing students' performance: a randomized crossover study.

Xinjia Dai, Yaodong Gong, Ling-Yun Ma

Abstract read
In one paragraph

Article in BMC nursing, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–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 synthesis or guideline pooled it.

  1. Pooled it
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

3 authors.

Xinjia Dai *Department of Neurology, The First Affiliated Hospital of Soochow University, No. 899, Pinghai Road, Suzhou, Jiangsu, China.
Yaodong Gong *Department of Pediatrics, Kunshan Women and Children's Healthcare Hospital, No.5, Qingyang Road, Kunshan, Jiangsu, China.
Ling-Yun MaDepartment of Neurology, The First Affiliated Hospital of Soochow University, No. 899, Pinghai Road, Suzhou, Jiangsu, China. malingyun1996@126.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe Objective Structured Clinical Examination (OSCE) is a gold-standard assessment in nursing education, yet traditional and virtual reality (VR) formats often lack timely, individualized feedback, potentially limiting gains in higher-order competencies such as communication and decision-making. Advances in artificial intelligence (AI), particularly ChatGPT, offer opportunities to address this gap. To evaluate the impact of ChatGPT-generated immediate feedback integrated into VR-based OSCE stations on nursing students’ communication performance, clinical decision-making accuracy, and related learning outcomes.

methodsA randomized, two-period crossover trial was conducted among 65 final-year nursing students at a tertiary teaching hospital in China between March and May 2025. Participants completed two matched OSCE stations under VR-only and VR + ChatGPT conditions, with a one-week washout period between the two study phases. Primary outcomes included OSCE communication scores, clinical decision-making accuracy, and task completion time. Secondary outcomes comprised learning satisfaction, acceptance of AI-assisted feedback, academic and communication self-efficacy, reflection quality, and cognitive workload. Data were analyzed using paired statistical comparisons and linear mixed-effects models adjusting for sequence and period effects.

resultsCompared with VR-only, VR + ChatGPT significantly improved communication scores (mean difference [MD] = 3.38, 95% CI: 2.12–4.64, P < 0.001, Cohen’s d = 0.63) and decision accuracy (MD = 3.91%, 95% CI: 2.09–5.72, P < 0.001, Cohen’s d = 0.49), while reducing completion time (MD = − 0.78 min, 95% CI: − 1.12 to − 0.44, P < 0.001, Cohen’s d = − 0.62). VR + ChatGPT also yielded higher learning satisfaction (MD = 0.31, P < 0.001), greater reflection quality (MD = 0.62, P < 0.001), lower cognitive workload (MD = − 5.7, P = 0.001), and significant gains in academic and communication self-efficacy (both P < 0.001). No severe adverse events occurred. Effects were consistent across subgroups defined by prior VR experience and digital literacy.

conclusionsIntegrating ChatGPT-generated immediate feedback into VR OSCE scenarios significantly enhances nursing students’ communication and decision-making performance, accelerates skill acquisition, and reduces cognitive strain, without compromising safety. CLINICAL TRIAL NUMBER: Not applicable.

Indexed as

Artificial intelligence-assisted learningChatGPTCommunication skillsImmediate feedbackNursing educationObjective structured clinical examinationVirtual reality

Identifiers

PMID41975412
PMCPMC13112760

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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.