Evidence map›Paper›PMID 38889074›Full record

ReviewJMIR medical education2024

Evolution of Chatbots in Nursing Education: Narrative Review.

Fang Zhang, Xiaoliu Liu, Wenyan Wu, Shiben Zhu

Abstract readReview
In one paragraph

Review in JMIR medical education, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. The Role of AI in Nursing Education and Practice: Umbrella Review.Journal of medical Internet research · 2025
    Pooled it
  2. Trial
  3. Article
  4. Article
  5. Article
  6. Article
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

4 authors.

Fang Zhang *Department of Science and Education, Shenzhen Baoan Women's and Children's Hospital, Shenzhen, China.ORCID 0009-0005-8263-0755
Xiaoliu Liu *Medical Laboratory of Shenzhen Luohu People's Hospital, Shenzhen, China.ORCID 0009-0003-2474-7555
Wenyan Wu *Medical Laboratory of Shenzhen Luohu People's Hospital, Shenzhen, China.ORCID 0009-0003-2567-4482
Shiben ZhuSchool of Nursing and Health Studies, Hong Kong Metropolitan University, Hong Kong, China.ORCID 0000-0002-0846-0453

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The integration of chatbots in nursing education is a rapidly evolving area with potential transformative impacts. This narrative review aims to synthesize and analyze the existing literature on chatbots in nursing education. Objective: This study aims to comprehensively examine the temporal trends, international distribution, study designs, and implications of chatbots in nursing education. Methods: A comprehensive search was conducted across 3 databases (PubMed, Web of Science, and Embase) following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram. Results: A total of 40 articles met the eligibility criteria, with a notable increase of publications in 2023 (n=28, 70%). Temporal analysis revealed a notable surge in publications from 2021 to 2023, emphasizing the growing scholarly interest. Geographically, Taiwan province made substantial contributions (n=8, 20%), followed by the United States (n=6, 15%) and South Korea (n=4, 10%). Study designs varied, with reviews (n=8, 20%) and editorials (n=7, 18%) being predominant, showcasing the richness of research in this domain. Conclusions: Integrating chatbots into nursing education presents a promising yet relatively unexplored avenue. This review highlights the urgent need for original research, emphasizing the importance of ethical considerations.

Indexed as

Artificial IntelligenceEducation, NursingHumansartificial intelligencechatbotsChatGPTnarrative reviewnursing education

Identifiers

PMID38889074
PMCPMC11186796

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.