Actionable companion · Assessment & reasoning

Learning with agency: Beyond acceptance: bounded use of AI and VR in orthopedic 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. Verify the participant population and task before transferring the findings.

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

Background The educational roles of artificial intelligence (AI) and virtual reality (VR) in orthopedic training remain insufficiently defined. Rather than examining acceptance alone, we investigated how students delineate trust boundaries, role allocation, and implementation priorities for bounded, faculty-supervised use of AI/VR in orthopedic education. Methods We conducted an exploratory cross-sectional online survey among Years 4–8 students in the eight-year program of clinical medicine at Peking Union Medical College using a study-specific questionnaire informed by AI- and simulation-enhanced medical education. Approximately 300 students were invited; 56 valid responses were analyzed (response proportion, 18.7%). The questionnaire assessed prior exposure to AI/VR, acceptance, perceived value, trust-related attitudes, application preferences, substitution boundaries, and risk perceptions. Results Overall, 83.9% of respondents supported or strongly supported further AI/VR integration in orthopedic teaching. Acceptance showed the highest composite score (4.25 ± 0.73), followed by perceived value (4.22 ± 0.61), educational trust (4.02 ± 0.49), and risk perception (3.80 ± 0.53). Support for AI-assisted tools and VR-based simulation was similarly high (4.29 ± 0.73 vs. 4.32 ± 0.83, p = 0.694). The strongest endorsements were for continued in-person teacher guidance (4.59 ± 0.53) and teacher review of AI/VR content before formal use (4.52 ± 0.57), whereas trust in AI-generated recommendations for real patient decision-making was the lowest-rated item (3.11 ± 1.11). Students consistently favored augmentation over replacement: AI was preferred for knowledge summarization, reasoning support, and examination preparation, whereas VR was preferred for anatomy learning, fracture classification, surgical pathway visualization, and pre-training. Prior VR exposure was associated with greater implementation support (4.41 ± 0.51 vs. 3.85 ± 0.90, p = 0.014). Conclusion Students supported AI/VR in orthopedic education, but within clear pedagogical and governance boundaries. Their responses suggest a preliminary, role-specific implementation model in which AI primarily enhances cognitive learning and VR primarily supports spatial-procedural training, under sustained faculty oversight. These perception-based findings should be tested in longitudinal or interventional curriculum studies.

What kind of evidence is this?

Supporting AI learning science · 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 exploratory cross-sectional questionnaire study investigated perceptions of AI- and VR-assisted orthopedic education among students enrolled in the eight-year program of clinical medicine at Peking Union Medical College (PUMC), Beijing, China. The survey targeted students from Year 4 through 8, representing the main clinical-stage cohorts with potential exposure to orthopedic learning activities. Because the survey targeted Years 4–8 students, respondents were reported as Year 4, Years 5–6, and Years 7–8 in the final analysis. The questionnaire was distributed to approximately 300 eligible students across five grade levels through the Wenjuanxing online survey platform. We attempted to invite the full eligible cohort rather than sample a predefined subgroup; therefore, no a priori power calculation was performed. Participation was voluntary and anonymous. A total of 56 complete and valid responses were included in the final analysis, corresponding to a response proportion of 18.7%.

Limitations and uncertainty · source passage

This study has several strengths. It examined AI and VR together rather than in isolation, focused on a specialty in which both technologies are pedagogically salient, and used a multidimensional questionnaire that moved beyond acceptance alone to include perceived value, trust boundaries, substitution boundaries, application priorities, and risk perceptions. These features allowed us to identify a more nuanced pattern than simple technological optimism or pessimism. Nonetheless, several limitations should be acknowledged. First, the study was exploratory and single-center, with a relatively small sample size and a modest response proportion (56 of approximately 300 invited students, 18.7%). No a priori power calculation was performed because we attempted to invite the full eligible cohort rather than test a predefined effect size. The results therefore should be interpreted as exploratory and hypothesis-generating, not definitive estimates of student attitudes.

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

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

Xiaohong Lyu; Yiwei Zhao; Siyi Cai; Jianguo Zhang. Beyond acceptance: bounded use of AI and VR in orthopedic education. Frontiers in Medicine (2026-07-15). https://doi.org/10.3389/fmed.2026.1856528

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.