Actionable companion · Feedback & scaffolding

Learning with agency: The combined impact of AI and VR on interdisciplinary learning and patient safety in healthcare education: a narrative review

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. Verify the participant population and task before transferring the findings.

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

Introduction The increasing integration of Artificial Intelligence (AI) and Virtual Reality (VR) in healthcare education offers innovative ways to enhance collaborative learning and improve patient safety. This narrative review examines the synergistic impact of AI-powered virtual reality (VR) simulations, such as those used in surgical training and patient communication, on knowledge acquisition, clinical skill development, and collaborative competencies among healthcare students. It also explores long-term knowledge retention, ethical considerations within virtual scenarios, and the psychological impact of high-stakes simulations on learner resilience. Methods This review is distinguished by its exhaustive literature search, which spanned PubMed, CINAHL, Scopus, Google Scholar, and other pertinent databases, to identify studies published between 2005 and 2024. The distinct focus on AI and VR interventions in healthcare education, particularly those with outcomes related to interdisciplinary learning or patient safety, distinguishes this review. Data were thematically analyzed across domains, including adaptive learning, technical skill development, teamwork, patient safety, and ethics. Results The findings of this review carry significant practical implications. Five key themes emerged: adaptive learning (n = 17), immersive skill development (n = 10), teamwork enhancement (n = 10), patient safety (n = 18), and ethical considerations (n = 21). These themes underscore the potential of AI and VR in healthcare education. AI-driven adaptive systems enabled personalized VR training, enhancing engagement and knowledge retention. Real-time AI feedback during simulations improved decision-making in safe, controlled environments. Interdisciplinary team simulations enhanced communication and collaboration, which are crucial for effective clinical care. Ethical modules embedded in VR scenarios promoted moral reasoning. Several studies also reported increased learner confidence in performing clinical procedures following VR training, suggesting enhanced preparedness for practice. Conclusion The integration of AI and VR holds the potential to revolutionize healthcare education, fostering personalized, immersive, and ethically informed learning. These technologies enhance technical proficiency and equip students with the complex demands of modern clinical practice. Strategic implementation can contribute to error reduction, improved patient outcomes, and a culture of safety. However, the journey is not over. Continued research is crucial for assessing the long-term outcomes and cost-effectiveness of these educational innovations, as well as for keeping pace with the rapidly evolving field of AI and VR in healthcare education. Supplementary Information The online version contains supplementary material available at 10.1186/s12909-025-07589-7.

What kind of evidence is this?

Intelligent tutoring & adaptive learning · Evidence synthesis

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 study follows a structured review approach adapted from Ferrari. (2015), covering the period from 2005 to 2024. Narrative reviews are valuable for identifying patterns, addressing specific questions, and organizing complex topics in fields of growing research interest. According to Jackson et al., an effective review requires a well-organized synthesis of previous research to provide a coherent analysis [22]. This narrative review aims to deepen understanding of the combined impacts of AI and VR on interdisciplinary learning and patient safety in healthcare, to bridge gaps in the existing literature, and to highlight areas for further exploration.

Limitations and uncertainty · source passage

While this narrative review synthesizes current insights into the combined impact of AI and VR on interdisciplinary learning and patient safety in healthcare education, it is essential to acknowledge certain limitations inherent to its methodology. As a narrative review, it does not employ the systematic search and rigorous appraisal protocols characteristic of systematic reviews or meta-analyses. Consequently, the synthesis of evidence presented here reflects a qualitative overview rather than a comprehensive, statistically pooled analysis, which may limit the generalizability and quantitative strength of the conclusions. Furthermore, the selection of studies might be subject to publication bias, where studies reporting positive or significant findings are more likely to be published and subsequently included, potentially skewing the perception of efficacy. The absence of meta-analytic data, in particular, means that specific effect sizes or aggregated statistical conclusions regarding the impact of these technologies cannot be provided. Therefore, while this review offers valuable conceptual insights and identifies key themes, readers should interpret the findings with an understanding of these methodological constraints.

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.

Give feedback on the learner’s reasoning, then fade hints. Record whether the learner can complete the next task without that scaffold.
Include a delayed assessment and an unfamiliar transfer problem without AI. Immediate supported performance cannot establish durable learning.
Measure engagement separately from achievement. Check whether novelty-driven participation persists and whether it translates to independent performance.
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

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

Emmanuel Aoudi Chance. The combined impact of AI and VR on interdisciplinary learning and patient safety in healthcare education: a narrative review. BMC Medical Education (2025-07-11). https://doi.org/10.1186/s12909-025-07589-7

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.