Evidence map›Paper›PMID 41788699›Full record

SynthesisFrontiers in medicine2026

Effects of an AI-enhanced BOPPPS teaching model in nursing courses: a meta-analysis of randomized controlled trials.

Huiling Zhang, Zheyuan Xia, Yahui Meng, Shuang Yu, Hui Shi

Abstract readSystematic Review
In one paragraph

Synthesis in Frontiers in medicine, 2026. 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

5 authors.

Huiling ZhangLaboratory of Geriatric Nursing and Health, Anhui University of Traditional Chinese Medicine, Hefei, China.
Zheyuan XiaLaboratory of Geriatric Nursing and Health, Anhui University of Traditional Chinese Medicine, Hefei, China.
Yahui MengLaboratory of Geriatric Nursing and Health, Anhui University of Traditional Chinese Medicine, Hefei, China.
Shuang YuLaboratory of Geriatric Nursing and Health, Anhui University of Traditional Chinese Medicine, Hefei, China.
Hui ShiLaboratory of Geriatric Nursing and Health, Anhui University of Traditional Chinese Medicine, Hefei, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aim: To evaluate the effectiveness of an artificial intelligence (AI)-enhanced BOPPPS teaching model in nursing courses, with a focus on academic performance, self-directed learning ability, and teaching satisfaction. Design: Systematic review and meta-analysis of randomized controlled trials (RCTs) conducted in accordance with PRISMA and registered in INPLASY (INPLASY202590123). Methods: Chinese- and English-language databases were searched for eligible RCTs comparing AI-enhanced BOPPPS with traditional teaching (or BOPPPS alone). Data extraction and risk-of-bias assessment followed Cochrane guidance. Pooled analyses were performed using standardized mean difference (SMD) for continuous outcomes and risk ratio (RR) for dichotomous outcomes, with heterogeneity assessed using I Results: Four RCTs involving 459 nursing students were included. Academic performance showed a significant improvement in the AI-enhanced BOPPPS group (SMD = 1.06, 95% CI 0.63-1.48; Conclusion: AI-enhanced BOPPPS teaching demonstrates consistent benefits in improving nursing students' academic performance, self-directed learning ability and teaching satisfaction, these findings support the potential value of integrating AI into structured instructional design to enhance learning processes and student experience in nursing education. Systematic review registration: https://inplasy.com/inplasy-2026-2-0026/, Identifier: INPLASY202620026.

Indexed as

AIBOPPSeducationmetanursing

Identifiers

PMID41788699
PMCPMC12956507

What OpenQuestion holds

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LicenceCC BY
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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.