Agentic education · Learning science

Actionable companion · Cognitive load & attention

From AI evidence to responsible practice: Application of AI-Generated Content in Medical Education: Systematic Review of the Impact on Critical Thinking Abilities of Medical Students

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 With the rapid development of artificial intelligence technology, artificial intelligence–generated content (AIGC) is increasingly widely applied in the field of medical education. Large language models, such as ChatGPT, are a prominent type of AIGC technology. Critical thinking is a core ability in medical education, but the impact of AIGC technology on the critical thinking ability of medical students remains unclear. Medical students are at a crucial stage in cultivating critical thinking, and the intervention of AIGC technology may have a profound impact on this process. Objective This study aims to systematically review the impact of AIGC technology on the complex mechanisms affecting medical students’ critical thinking abilities and build a corresponding strategic framework. The findings are intended to provide theoretical support and practical guidance for applying AIGC in medical education. Methods This study followed 2020 PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, with the retrieval scope limited to English studies published between November 2022 and June 2025. Through the PubMed database, combined with the search methods of subject terms and free words, relevant studies involving the impact of AIGC on the critical thinking of medical students were screened for using keywords such as “AIGC,” “medical students,” and “critical thinking.” Two independent reviewers screened and evaluated the literature, and ultimately conducted a qualitative analysis based on the common themes extracted from the literature. Results AIGC technology in medical education is 2-fold. First, AIGC’s powerful information capabilities provide abundant learning resources and efficient tools. This accelerates knowledge acquisition and broadens learning scope. Second, overreliance on AIGC may lead to mental inertia, weaken critical thinking skills, and cause academic integrity issues among students. Research has found that strategies such as customized AIGC tools, virtual standardized patients, new models of resource integration, and proactive assessment of AI limitations can effectively make up for the deficiencies of AIGC in cultivating high-level critical thinking, helping medical students maintain and enhance their critical thinking and problem-solving abilities. Conclusions AIGC technology application in medical education needs to carefully weigh the pros and cons. By optimizing the design and usage of AIGC tools and combining them with the guidance and supervision of educators, they can be transformed into powerful tools for promoting the development of critical thinking among medical students. Future research should further expand the scope of study, optimize research methods, pay attention to individual differences, track long-term effects, and deeply explore the influence of ethical and cultural factors to more comprehensively assess the application potential and challenges of AIGC technology in medical education.

What kind of evidence is this?

Supporting AI learning science · Evidence synthesis

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 systematic review aims to methodically identify, evaluate, and synthesize existing literature to elucidate the specific effects of AIGC on the critical thinking abilities of medical students. The review was conducted in strict accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines and used a systematic methodology to assess the influence of AIGC on medical students’ critical thinking (Checklist 1). Additionally, the review protocol was registered with PROSPERO under the registration number CRD420251116204.

Limitations and uncertainty · source passage

Laupichler et al [26] conducted a comparative analysis of the quality of questions generated by ChatGPT-3.5 versus those created artificially, revealing that AI-generated questions were more susceptible to being “guessed,” while artificially constructed questions were more effective in accurately assessing students’ true capabilities. Thomae et al [27] further illustrated the benefits of engaging students in the design of solutions and subsequent comparisons with AI-generated alternatives within the same academic year, particularly in the context of a “placebo control condition” unit. This comparative exercise not only enables students to visually discern the strengths and weaknesses of AI but also promotes critical thinking through facilitated discussions. Additionally, Mondal [28] emphasized the necessity for students to actively articulate their evaluations of AI-generated information and engage in discourse regarding its potential inaccuracies and biases. This practice is not only fundamental to the development of critical thinking skills but also essential for navigating the limitations of AI technology. Collectively, these studies underscore that through the processes of comparison, evaluation, and discussion of AI-generated content, medical students can enhance their critical thinking abilities, thereby enabling them to apply AI technology more judiciously in their future medical practices.

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.
  • 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

Jinlei Li; Fen Ai; Jueyan Wang; Bingxin Cheng; Yu Li; Zhen Chen. Application of AI-Generated Content in Medical Education: Systematic Review of the Impact on Critical Thinking Abilities of Medical Students. JMIR Medical Education (2026-02-27). https://doi.org/10.2196/79939

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.