Evidence map›Paper›PMID 41919166›Full record

ArticleFrontiers in medicine2026

Application of self-organised learning environments integrated with generative AI in standardised training for residents.

Haiping Luo, Hongmin Yu

Abstract read
In one paragraph

Article 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

2 authors.

Haiping LuoDepartment of Gastrointestinal and Anorectal Surgery, Huangshi Central Hospital, Huangshi, Hubei, China.
Hongmin YuDepartment of Breast and Thyroid Surgery, Ward 2 (Clinical Skills Training Center), Huangshi Central Hospital, Huangshi, Hubei, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This study aimed to evaluate the effectiveness of the GAiSOLEs teaching model, which integrates Self-Organised Learning Environments (SOLEs) with Generative Artificial Intelligence (GAI), in improving teaching effectiveness, digital literacy and teaching satisfaction in the standardized training of residents. Methods: A single-center, double-blind randomized controlled trial was conducted in this study. A total of 114 standardized training participants for rotating residents were enrolled and randomly divided into two groups: the intervention group (GAiSOLEs teaching model, Results: Post-intervention teaching effectiveness scores were significantly elevated relative to baseline levels in both the intervention and control groups (all Conclusion: The GAiSOLE teaching model was associated with improved teaching effectiveness, digital literacy and teaching satisfaction among residents, and thus holds promising potential as a novel approach for the standardized training of residents.

Indexed as

digital literacygenerative AI (GenAI)self-organised learning environmentsteaching effectivenessteaching satisfaction

Identifiers

PMID41919166
PMCPMC13033667

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.