Agentic education · Learning science

Actionable companion · Assessment & reasoning

From AI evidence to responsible practice: Shaping the future of medical education: A cross-sectional study on ChatGPT attitude and usage among medical students in Sudan

AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not findings or recommendations attributed to the source authors. No independent peer review of this guide is asserted.

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Evidence and context

Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): Higher education. Health-professions education findings do not establish effectiveness in K–12 classrooms.

Original authors’ abstract, reproduced under the linked source license:

Abstract Background Artificial 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. Methods This 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. Objective This 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. Results Among 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 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. Conclusions The 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.

What kind of evidence is this?

Supporting AI learning science · Survey / observational study

Stage: Higher education. Screening labels describe reported context and study-design language. They are not a risk-of-bias rating or proof of effectiveness for an age band.

Study methods · source passage

This study employed an analytical cross-sectional design to evaluate the awareness, attitude, and usage of ChatGPT among medical students in Sudan. Data were collected from June 20th, 2024, to October 30th, 2024, ensuring adequate time to capture a diverse and representative sample.

Limitations and uncertainty · source passage

However, the study’s reliance on convenience sampling and digital distribution limits its generalizability, particularly as students from rural or low-resource settings may be underrepresented. The self-reported nature of the data introduces the potential for social desirability bias, and the cross-sectional design and convenience sampling restrict the ability to assess changes over time or causality. Additionally, while the questionnaire’s validation ensures reliability, the lack of contextual adaptation to Sudan’s unique educational and cultural landscape may affect the depth of the analysis. Moreover, the study’s narrow focus on ChatGPT, without a broader exploration of institutional or technological factors, limits its capacity to situate the findings within the wider context of AI adoption in medical education and hinders its applicability to other AI technologies and educational chatbots.

Design the system before the pilot

Use this study to inform a learning mechanism or implementation question. It is not direct evidence that autonomous agents improve learning. Keep the proposed agent behavior separate from what the source actually tested.

  • Use common rubrics, blind scoring where feasible and a fresh unaided reasoning task. Document permissible assistance and avoid treating AI-detector scores as proof of misconduct.

A 90-day district pilot

  1. Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
  2. Days 16–30 · Design. Choose one disciplinary task. Require a first attempt, a documented AI consultation, a critique of the output and an independent explanation. Align the comparison group and scoring rubric with the same objective. Name an accountable owner and set the baseline, success threshold and budget before implementation.
  3. Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
  4. Days 61–90 · Decide. Blind-score reasoning and accuracy on a new task without AI. Distinguish model examination scores, student perceptions and actual learner gains; only the last directly tests educational benefit. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

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. Shaping the future of medical education: A cross-sectional study on ChatGPT attitude and usage among medical students in Sudan. PLOS One (2025-05-23). https://doi.org/10.1371/journal.pone.0324698

Companion prepared 2026-11-01. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.