ArticleJMIR research protocols2026
Effects of AI on Nursing Education: Protocol for a Systematic Review and Meta-Analysis.
Article in JMIR research protocols, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: AI demonstrates considerable potential in nursing education. However, its specific effects on knowledge acquisition, practical skills, satisfaction, competence, and confidence remain inadequately characterized. Objective: This study aims to assess the effects of AI on nursing students' education. Methods: We will follow the PRISMA-P (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols) guidelines. Systematic literature searches will be conducted across 6 electronic databases, namely, PubMed, Web of Science, Embase, CINAHL, MEDLINE (EBSCOhost), and the Cochrane Library. The inclusion criteria follow the population, intervention, comparator, outcome, and study design framework, encompassing nursing students from academic institutions and clinical internship settings. This review will examine studies comparing AI-based educational interventions with traditional teaching methodologies. The outcomes will encompass knowledge level, practical ability, satisfaction, competence, and confidence. Eligible study designs include randomized controlled trials and quasi-experimental studies. The search timeline is from the inception of each database to February 2026, with no language restrictions. Two independent reviewers will screen the studies and extract data. Any disputes will be resolved through discussion. Unresolved disputes will be decided by consulting the third author. For the risk-of-bias assessment, version 2 of the Cochrane risk-of-bias tool for randomized trials and the Risk of Bias in Nonrandomized Studies of Interventions tool will be used. Moreover, the RevMan software (version 5.4) will be used for meta-analysis. Results: Literature retrieval was finished in February 2026, and formal title and abstract screening and full-text evaluation are ongoing. Data extraction, risk-of-bias assessment, and quantitative meta-analysis are scheduled to start in January 2026, with the full review manuscript planned for submission by June 2026. Conclusions: This meta-analysis will systematically quantify the overall effects of AI-assisted teaching on nursing students' knowledge, practical ability, satisfaction, competence, and confidence. Synthesized evidence can facilitate standardized application of AI in nursing education and direct subsequent relevant research.
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Registered trials
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.