Agentic education · Learning science

Actionable companion · Self-regulation & metacognition

Learning with agency: Effects of Generative AI–Supported Virtual Reality Dental Skill Training on Learning Performance, Visual Behavior, and Cortical Activation Among Dental Students: Stratified Randomized Controlled Trial

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): Higher education. Health-professions learning must be distinguished from K–12 instruction.

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

Abstract Background The rapid advancement of AI, particularly generative AI (GenAI), is reshaping educational paradigms. However, its utility in preclinical digital medical skills education remains insufficiently explored. Objective This study aims to investigate whether GenAI‑assisted training yields superior performance relative to traditional instruction in virtual reality (VR)–supported preclinical dental skill education. Methods A total of 123 eligible students from a top‑tier Chinese university completed the trial. Participants were stratified by baseline theoretical scores and randomly assigned within each stratum into two arms: the GenAI-assisted group (n=62) received ChatGPT guidance without human tutoring, with research assistants monitoring AI outputs solely for safety and error checking; the teacher-led control group (n=61) received on-site instructor guidance. Both groups underwent standardized VR-based dental skill training for 7 days. Assessments included dental operative skill examinations, functional near-infrared spectroscopy recording, continuous eye-tracking monitoring, visual-spatial ability tests, and questionnaires measuring self-regulated learning, learning emotions, and engagement. Results As the primary outcome, there was a statistically significant intergroup difference in operative test scores between the control group (mean 67.39, SD 16) and the GenAI‑assisted group (mean 77.25, SD 11.86), with the GenAI‑assisted group exhibiting higher operative test scores relative to the control group (t110.56=3.88, Cohen d=0.70; P<.001). For secondary outcomes, the GenAI‑assisted group also demonstrated significantly lower cognitive‑load indicators (mean 0.180, SD 0.136) than the control group (mean 0.346, SD 0.150; P<.001), accompanied by more efficient visual attention allocation patterns and stronger activation in prefrontal cortex, motor cortex, visual association cortex, and temporoparietal junction brain regions. The GenAI‑assisted group showed better performance across low (P=.003), medium (P=.004), and high (P=.004) level visual-spatial ability tests. Among low-achieving participants, the largest between-group difference in self-regulated learning was found in favor of the GenAI-assisted group (P=.02). Better scores were also observed for the GenAI‑assisted group in learning enjoyment, as well as behavioral, emotional, and cognitive engagement. Conclusions Students randomized to GenAI-assisted training had higher immediate operative scores than students receiving teacher-led instruction. Secondary cognitive, behavioral, and neurophysiological findings were exploratory and pointed toward a link between GenAI exposure and variations in skill‑acquisition metrics among dental students receiving VR-based training. The results offer preliminary insights regarding the potential integration of GenAI into preclinical dental skill education. Trial Registration Chinese Clinical Trial Registry ChiCTR2500110063; https://tinyurl.com/2hkv5wt9 Keywords: generative AI, medical education, clinical skills, visual-spatial ability, cognitive load, self-regulated learning

What kind of evidence is this?

Supporting AI learning science · Randomized study reported

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

The study recruited 129 dental students from a top-tier Chinese university. All were in their fourth or fifth year of a 5-year dental program and had acquired theoretical knowledge, with no preclinical skills training and VR simulator operation experience. Ages ranged from 17-23 (mean 21.86, SD 0.97) years. All had normal or corrected vision.

Limitations and uncertainty · source passage

This study has several limitations: only partial blinding was implemented and full double-blinding could not be achieved due to distinct training interaction modes, which may introduce subjective scoring bias; single-item scales were adopted to assess learning emotions, lacking the reliability and comprehensiveness of standardized multiitem psychological scales; merely 1-week short-term training performance was analyzed without testing long-term skill retention and measuring real clinical performance outcomes; training was restricted to basic tooth preparation, so the results cannot be generalized to complex dental operations or other training scenarios; multiple statistical comparisons across indicators were performed, and minor residual statistical inflation could not be fully eliminated even with Holm-Bonferroni correction; detailed ChatGPT usage details of participants were not recorded during the training process. Furthermore, learners may exhibit decision offloading and develop overreliance on AI feedback during practice. Although research assistants monitored AI outputs, systematic verification of every AI response at scale was not feasible within this trial.

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 plan–attempt–check–reflect prompts and compare confidence with actual performance. Keep learner choice and reflection visible.
  • Limit simultaneous instructions and unnecessary dialogue. Compare task performance and a suitable mental-effort measure; easier-feeling work is not automatically better 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

  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

Yuting Wen; Chang Wen; Siyu Huang; Yufei Chong; Dong Yang. Effects of Generative AI–Supported Virtual Reality Dental Skill Training on Learning Performance, Visual Behavior, and Cortical Activation Among Dental Students: Stratified Randomized Controlled Trial. Journal of Medical Internet Research (2026-10-01). https://doi.org/10.2196/90938

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.