Actionable companion · Retention & transfer

Learning with agency: Research progress on the application of virtual simulation in medical education

AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not claims 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): Age / stage not established. Health-professions learning must be distinguished from K–12 instruction.

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

Abstract Medical education is fundamental to developing clinical competence, directly influencing care quality and patient safety. Traditional teaching models have long faced structural limitations, including scarce physical resources and restricted clinical exposure, which hinder the training of versatile professionals needed in modern health care. Virtual simulation (VS), by providing a high-fidelity, repeatable, and low-risk learning environment, offers a promising solution to these challenges. This narrative article reviews recent applications of VS in basic medicine, clinical medicine, and nursing education. Although existing evidence supports its educational benefits, most studies rely on short-term, small-sample designs and lack evaluation of long-term clinical transfer or cost-effectiveness. Persistent issues include high device heterogeneity and insufficient integration of nontechnical skills training. Future efforts should prioritize multicenter longitudinal research, platform standardization, and the incorporation of artificial intelligence to enable personalized adaptive learning—moving VS toward a core component of medical education. Keywords: basic medicine, clinical medicine, medical education, nursing medicine, virtual simulation

What kind of evidence is this?

Intelligent tutoring & adaptive learning · 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

Pathology analyzes tissues, organs, body fluids, and autopsy findings to uncover disease mechanisms and inform clinical decisions and treatment strategies, making it a cornerstone of medical research and diagnosis (39). Traditional pathology teaching relies on glass slides and light microscopes, which pose problems such as limited learning resources and high time demands. Moreover, the lecture-dominated format overemphasizes memorization over clinical reasoning, and the use of real cases remains insufficient, hindering students' development of diagnostic thinking (40, 41). Virtual simulation offers a practical way forward. Cubillos P et al. (42) developed a virtual microscope—based online learning module for gynecologic cytopathology (VM-eLM). After training, average test scores rose by 16%, and the false-negative rate dropped by 88%. Among novices, false negatives decreased by 78% (p < 0.001); experienced users eliminated false negatives entirely. Recognition of low-grade and high-grade squamous intraepithelial lesions (LSIL, HSIL) improved by 31% and 50%, respectively (p < 0.001). Participants rated the module as novel, supportive of remote training, timely in feedback, and high in whole-slide image quality. Separately, Buma AIG et al. (43) designed and evaluated PathoDiscovery, an interactive online tool for teaching tumor-related pathophysiology and histopathology to medical students. Student ratings of the online module increased from 6.8 to 7.4 out of 10. In the improved academic year, 90% of students found the module structure logically clear, 57% considered the difficulty level appropriate, 76% agreed the modules matched the learning objectives, and 78% believed the modules supported knowledge development. The tool received positive feedback as a dynamic resource for blended learning (43).

Limitations and uncertainty · source passage

Abstract Medical education is fundamental to developing clinical competence, directly influencing care quality and patient safety. Traditional teaching models have long faced structural limitations, including scarce physical resources and restricted clinical exposure, which hinder the training of versatile professionals needed in modern health care. Virtual simulation (VS), by providing a high-fidelity, repeatable, and low-risk learning environment, offers a promising solution to these challenges. This narrative article reviews recent applications of VS in basic medicine, clinical medicine, and nursing education. Although existing evidence supports its educational benefits, most studies rely on short-term, small-sample designs and lack evaluation of long-term clinical transfer or cost-effectiveness. Persistent issues include high device heterogeneity and insufficient integration of nontechnical skills training. Future efforts should prioritize multicenter longitudinal research, platform standardization, and the incorporation of artificial intelligence to enable personalized adaptive learning—moving VS toward a core component of medical education. Keywords: basic medicine, clinical medicine, medical education, nursing medicine, virtual simulation

Design the system before the pilot

Document the learner model, mastery rule, hint sequence and adaptation signal. Compare adaptive routing with the same content in a non-adaptive sequence. Check whether early errors lead to persistent low-expectation pathways.

Include a delayed assessment and an unfamiliar transfer problem without AI. Immediate supported performance cannot establish durable learning.

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

Xiwang Jiang; Haichao Ge; Qianyong Wang. Research progress on the application of virtual simulation in medical education. Frontiers in Medicine (2026-07-17). https://doi.org/10.3389/fmed.2026.1864139

Companion prepared 2026-10-30. 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.