Evidence map›Paper›PMID 38816721›Full record

ArticleBMC medical education2024

Enhancing AI competence in health management: students' experiences with ChatGPT as a learning Tool.

Lior Naamati-Schneider

Abstract read
In one paragraph

Article in BMC medical education, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 2 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

17 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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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

1 author.

Lior Naamati-SchneiderHealth Systems Management Department, Hadassah Academic College, Jerusalem, Israel. liorna@hac.ac.il.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe healthcare industry has had to adapt to significant shifts caused by technological advancements, demographic changes, economic pressures, and political dynamics. These factors are reshaping the complex ecosystem in which healthcare organizations operate and have forced them to modify their operations in response to the rapidly evolving landscape. The increase in automation and the growing importance of digital and virtual environments are the key drivers necessitating this change. In the healthcare sector in particular, processes of change, including the incorporation of artificial intelligent language models like ChatGPT into daily life, necessitate a reevaluation of digital literacy skills.

methodsThis study proposes a novel pedagogical framework that integrates problem-based learning with the use of ChatGPT for undergraduate healthcare management students, while qualitatively exploring the students' experiences with this technology through a thematic analysis of the reflective journals of 65 students.

resultsThrough the data analysis, the researcher identified five main categories: (1) Use of Literacy Skills; (2) User Experiences with ChatGPT; (3) ChatGPT Information Credibility; (4) Challenges and Barriers when Working with ChatGPT; (5) Mastering ChatGPT-Prompting Competencies. The findings show that incorporating digital tools, and particularly ChatGPT, in medical education has a positive impact on students' digital literacy and on AI Literacy skills.

conclusionsThe results underscore the evolving nature of these skills in an AI-integrated educational environment and offer valuable insights into students' perceptions and experiences. The study contributes to the broader discourse about the need for updated AI literacy skills in medical education from the early stages of education.

Indexed as

Artificial IntelligenceComputer LiteracyEducation, Medical, UndergraduateFemaleHumansMaleProblem-Based LearningStudents, MedicalAI digital literacyChatGPT in educationHealthcare management education

Identifiers

PMID38816721
PMCPMC11140890

What OpenQuestion holds

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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.