Evidence map›Paper›PMID 38720326›Full record

ArticleBMC nursing2024

Experience of undergraduate nursing students participating in artificial intelligence + project task driven learning at different stages: a qualitative study.

Weijuan Kong, Yanhua Ning, Ting Ma, Fei Song, Yuxin Mao, Cailing Yang, Xinjin Li, Yahong Guo, Haiyan Liu, Jing Shi and 1 more

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Article in BMC nursing, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
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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

11 authors.

Weijuan KongSchool of Nursing, Ningxia Medical University, Yinchuan, Ningxia, 750004, China.
Yanhua NingSchool of Nursing, Ningxia Medical University, Yinchuan, Ningxia, 750004, China. nyhl_ning@126.com.
Ting MaSchool of Nursing, Ningxia Medical University, Yinchuan, Ningxia, 750004, China.
Fei SongSchool of Nursing, Ningxia Medical University, Yinchuan, Ningxia, 750004, China.
Yuxin MaoSchool of Nursing, Ningxia Medical University, Yinchuan, Ningxia, 750004, China.
Cailing YangSchool of Nursing, Ningxia Medical University, Yinchuan, Ningxia, 750004, China.
Xinjin LiSchool of Nursing, Ningxia Medical University, Yinchuan, Ningxia, 750004, China.
Yahong GuoSchool of Nursing, Ningxia Medical University, Yinchuan, Ningxia, 750004, China.
Haiyan LiuSchool of Nursing, Ningxia Medical University, Yinchuan, Ningxia, 750004, China.
Jing ShiSchool of Nursing, Ningxia Medical University, Yinchuan, Ningxia, 750004, China.
Lingna LiuSchool of Nursing, Ningxia Medical University, Yinchuan, Ningxia, 750004, China.

Funding

Ningxia Medical University bjg2023040
6 · The paper itself

Abstract

backgroundArtificial intelligence is a growing phenomenon that will soon facilitate wide-scale changes in many professions, and is expected to play an important role in the field of medical education. This study explored the realistic feelings and experiences of nursing undergraduates participating in different stages of artificial intelligence + project task driven learning, and provide a basis for artificial intelligence participation in nursing teaching.

methodsWe conducted face-to-face semi-structured interviews with nursing undergraduates participating in Nursing Research Course which adopts artificial intelligence + project task driven learning from a medical university in Ningxia from September to November 2023, to understand their experience of using artificial intelligence for learning and the emotional changes at different stages. The interview guide included items about their personal experience and feelings of completing project tasks through dialogue with artificial intelligence, and suggestions for course content. Thematic analysis was used to analyze interview data. This study followed the COREQ checklist.

resultsAccording to the interview data, three themes were summarized. Undergraduate nursing students have different experiences in participating in artificial intelligence + project task driven learning at different stages, mainly manifested as diverse emotional experiences under initial knowledge deficiency, the individual growth supported by external forces during the adaptation period, and the expectations and suggestions after the birth of the results in the end period.

conclusionsNursing undergraduates can actively adapt to the integration of artificial intelligence into nursing teaching, dynamically observe students' learning experience, strengthen positive guidance, and provide support for personalized teaching models, better leveraging the advantages of artificial intelligence participation in teaching.

Indexed as

Artificial intelligenceExperienceProject task driven learningUndergraduate nursing students

Identifiers

PMID38720326
PMCPMC11077879

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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.