Evidence map›Paper›PMID 40408361›Full record

ArticlePloS one2025

Shaping the future of medical education: A cross-sectional study on ChatGPT attitude and usage among medical students in Sudan.

Weam Mohamed Meargni Ahmed, Malaz M Abdalmotalib, Mohamed H Elbadawi, Galia Tajelsir Fadulelmula Mohammed, Waad Mohamed Ibrahim Mohamed, Fatima Salih Babiker Mohammed, Hajar Saad Salih, Hiba Omer Yousif Mohamed

Abstract read
In one paragraph

Article in PloS one, 2025. 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. Pooled it
  2. Observational
  3. Article
  4. Article
  5. Whose Voice is it Anyway?Perspectives on medical education · 2026
    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

8 authors.

Weam Mohamed Meargni AhmedFaculty of Medicine, University of Khartoum, Khartoum, Sudan.
Malaz M AbdalmotalibFaculty of Medicine, University of Khartoum, Khartoum, Sudan.ORCID https://orcid.org/0000-0002-7259-0439
Mohamed H ElbadawiFaculty of Medicine, University of Khartoum, Khartoum, Sudan.
Galia Tajelsir Fadulelmula MohammedFaculty of Medicine, University of Khartoum, Khartoum, Sudan.
Waad Mohamed Ibrahim MohamedFaculty of Medicine, University of Khartoum, Khartoum, Sudan.
Fatima Salih Babiker MohammedFaculty of Medicine, University of Khartoum, Khartoum, Sudan.
Hajar Saad SalihFaculty of Medicine, University of Khartoum, Khartoum, Sudan.
Hiba Omer Yousif MohamedFaculty of Medicine, University of Khartoum, Khartoum, Sudan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial intelligence (AI) is revolutionizing education globally, yet its adoption in medical education remains inadequately understood. ChatGPT, a generative AI tool, offers promising yet doubtful potential for enhancing academic and clinical training.

methodsThis study employed an analytical cross-sectional design, involving 1,443 Sudanese medical students who participated through an online, structured questionnaire. The questionnaire was designed to assess ChatGPT awareness, usage, and associated factors. Statistical analysis was performed using SPSS software to identify key determinants influencing ChatGPT awareness and usage among the participants.

objectiveThis study investigates the levels of awareness, attitude, and usage of ChatGPT among Sudanese medical students, identifying key socio-demographic, economic, and institutional factors influencing its adoption.

resultsAmong the participants, 65.8% were aware of ChatGPT, yet only 41.9% reported using it. Gender differences were statistically significant, with males demonstrating higher usage rates (p < 0.001). Single and unemployed students were more likely to use ChatGPT compared to their counterparts. Students residing in private accommodations and those with higher family incomes (>300,000 SDGs) showed significantly greater usage (p < 0.001). Factors such as residency type, internet quality, and institutional orientation were identified as key influences on ChatGPT adoption, highlighting a substantial digital influence. Additionally, inadequate infrastructure and reliance on traditional curricula were significant barriers to wider usage.

conclusionsThe findings underscore the urgent need for targeted interventions, including curriculum reform to integrate AI literacy, enhanced digital infrastructure, and gender-equity initiatives. Addressing these systemic gaps will scale up AI adoption in medical education. This study provides actionable insights for educators and policymakers, emphasizing the urgency of bridging socio-economic and institutional inequities to foster equitable access to AI tools in medical training.

Indexed as

Artificial IntelligenceEducation, MedicalStudents, MedicalAdultCross-Sectional StudiesFemaleGenerative Artificial IntelligenceHumansMaleSudanSurveys and QuestionnairesYoung Adult

Identifiers

PMID40408361
PMCPMC12101695

What Socratic holds

Textmetadata
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 Socratic graph.