Actionable companion · Cognitive load & attention
From AI evidence to responsible practice: The ethical challenges in the integration of artificial intelligence and large language models in medical education: A scoping review
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.
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): Age / stage not established. Health-professions education findings do not establish effectiveness in K–12 classrooms.
Original authors’ abstract, reproduced under the linked source license:
With the rapid development of artificial intelligence (AI), large language models (LLMs), such as ChatGPT have shown potential in medical education, offering personalized learning experiences. However, this integration raises ethical concerns, including privacy, autonomy, and transparency. This study employed a scoping review methodology, systematically searching relevant literature published between January 2010 and August 31, 2024, across three major databases: PubMed, Embase, and Web of Science. Through rigorous screening, 50 articles which met inclusion criteria were ultimately selected from an initial pool of 1,192 records. During data processing, the Kimi AI tool was utilized to facilitate preliminary literature screening, extraction of key information, and construction of content frameworks. Data reliability was ensured through a stringent cross-verification process whereby two independent researchers validated all AI-generated content against original source materials. The study delineates ethical challenges and opportunities arising from the integration of AI and LLMs into medical education, identifying seven core ethical dimensions: privacy and data security, algorithmic bias, accountability attribution, fairness assurance, technological reliability, application dependency, and patient autonomy. Corresponding mitigation strategies were formulated for each challenge. Future research should prioritize establishing dedicated ethical frameworks and application guidelines for AI in medical education while maintaining sustained attention to the long-term ethical implications of these technologies in healthcare domains.
What kind of evidence is this?
Supporting AI learning science · Design requires full-text appraisal
Stage: Age / stage not established. 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 scoping review was conducted according to the framework established by Arksey and O’Malley (Arksey et al., 2005) as well as the PRISMA Extension for Scoping Reviews (PRISMA-ScR) [16]. The PRISMA-ScR checklist is shown in S1 File. The study has been pre-registered on the OSF platform (https://doi.org/10.17605/OSF.IO/65TYD).
Limitations and uncertainty · source passage
The strengths of this scoping review are diverse and highlight its essential contribution to the field. First, a systematic search strategy was utilized across PubMed, Embase, and Web of Science, ensuring a comprehensive literature capture and enhancing the review’s reliability. Second, including perspectives from various countries reflects a global dialogue on the ethical challenges posed by AI and large language models (LLMs) in medical education. Third, the review’s timely focus on the increase in scholarly outputs from 2021 to 2024 emphasizes the relevance of these issues in the continuously evolving landscape of medical education. Fourth, many articles’ structured analysis of ethical challenges provides a clear overview of the considerations involved in integrating AI and LLMs. Lastly, the review presents concrete solutions and recommendations, offering actionable guidance for future research and policy formulation regarding the ethical application of AI in medical education. These strengths establish the review as a solid foundation for advancing the ethical discourse and practical application of AI in this field.
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.
- Limit simultaneous instructions and unnecessary dialogue. Compare task performance and a suitable mental-effort measure; easier-feeling work is not automatically better learning.
- Co-design accommodations with learners and specialists. Check access and error burden across groups without inferring subgroup effectiveness from small or unreported samples.
A 90-day district pilot
- 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.
- Days 16–30 · Design. Treat the paper as background evidence until its population is verified in the full text. Do not assign an age-specific intervention or extend adult results to children. Name an accountable owner and set the baseline, success threshold and budget before implementation.
- 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.
- Days 61–90 · Decide. Extract the participant ages or education stage, recruitment, task and comparator before selecting a local pilot. If these cannot be established, retain the paper for design discussion rather than age-band efficacy claims. 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
Xinrui Li; Xiaodan Yan; Han Lai. The ethical challenges in the integration of artificial intelligence and large language models in medical education: A scoping review. PLOS One (2025-10-22). https://doi.org/10.1371/journal.pone.0333411
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.