Evidence map›Paper›PMID 41491181›Full record

ArticleBMC medical education2026

Student voices: synergising artificial and human intelligence in health science education.

Zijing Hu, Caixia Qiu, Anqi Wang, Shungui Xu, Jing Ye, Jiawen Zou, Shuting Liu, Weixiang Yuan, Jiazhe Lv

Abstract read
In one paragraph

Article in BMC medical education, 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
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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

9 authors.

Zijing HuDepartment of Complementary Medicine, University of Johannesburg, Johannesburg, South Africa.
Caixia QiuPeople's Hospital Affiliated to Fujian University of Traditional Chinese Medicine, No. 602, 817 Road, Taijiang District, Fuzhou, Fujian Province, China. 343832754@qq.com.
Anqi WangFujian University of Traditional Chinese Medicine, Fuzhou, China.
Shungui XuPeople's Hospital Affiliated to Fujian University of Traditional Chinese Medicine, No. 602, 817 Road, Taijiang District, Fuzhou, Fujian Province, China. xushungui@sohu.com.
Jing YePeople's Hospital Affiliated to Fujian University of Traditional Chinese Medicine, Fuzhou, China.
Jiawen ZouFujian University of Traditional Chinese Medicine, Fuzhou, China.
Shuting LiuFujian University of Traditional Chinese Medicine, Fuzhou, China.
Weixiang YuanNakhonratchasima College, 290, Ban Mai, Mueang Nakhon Ratchasima District, Nakhon Ratchasima, 30000, Thailand. helloywx@163.com.
Jiazhe LvSchool of Mathematics and Statistics, Fuzhou University, Fuzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThere is a dire need to enhance health science education through technology to meet the demands of 21st-century education globally. This study explored the integration of Artificial Intelligence (AI) and Human Intelligence (HI) in health science education.

methodsGuided by the Technological, Pedagogical, and Content Knowledge and Diffusion of Innovation frameworks, a descriptive qualitative single-case study was conducted within an interpretivist paradigm. Purposive sampling recruited six postgraduate and residency students with dual exposure to conventional and AI-assisted learning. Semi-structured interviews were thematically analysed.

resultsFindings revealed that students perceived AI as a valuable supplementary tool that enhanced diagnostic confidence, accelerated knowledge access, and simulated rare clinical scenarios. AI facilitated self-directed learning, structured clinical reasoning, and supported overburdened educators. However, participants cautioned against over-reliance, citing risks such as reduced critical thinking, algorithmic bias, and potential erosion of humanistic care. The adoption of AI was influenced by its relative advantages, alignment with learning preferences, and the possibility of low-risk experimentation; however, challenges arose from the complexity of prompt design and the lack of formal training.

conclusionThe study concluded that effective AI-HI integration requires AI literacy, ethical governance, and hybrid models combining technological efficiency with mentorship. Institutional investment in curricula, faculty development, and policy frameworks is essential to ensure responsible, context-sensitive adoption.

Indexed as

Artificial IntelligenceStudents, MedicalCurriculumFemaleHumansInterviews as TopicMaleQualitative ResearchArtificial intelligenceHealth sciencesHigher educationHuman intelligence

Identifiers

PMID41491181
PMCPMC12870363

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.