Actionable companion · Assessment and evidence of learning
From evidence to district action: Revolutionizing clinical skills training with Generative AI: the Intelligent Inquiry Training System framework
AI-assisted editorial implementation guide. The district actions are proposed applications, not findings or recommendations attributed to the original authors. No additional peer review is asserted.
Evidence and context
Health-professions education context: test transfer to K–12 rather than assuming it.
Original authors’ abstract, reproduced under the linked source license:
Background Practical training bridges theoretical knowledge and clinical skills in medical education but faces challenges such as resource scarcity, complex patient–physician relationship, and limited interactivity in traditional systems. Generative artificial intelligence (LLMs) offers innovative solutions for simulating virtual patients and enhancing communication training. Method An Intelligent Inquiry Training System (IITS) was developed using Baidu’s Wenxin LLM, featuring dynamic case generation, multimodal examination simulation, and emotional feedback modules. Integrated into a blended surgical curriculum, IITS employed pre-class problem-based learning (PBL) and post-class extended training. System efficacy was evaluated via student/instructor questionnaires. Results IITS received software copyright certification and demonstrated cross-platform compatibility. Student feedback highlighted its auxiliary role, prompt responses, and need for improved diagnostic accuracy. Teachers praised its case generation efficiency and recommended AI-powered evaluation. Conclusion IITS enhances clinical reasoning and communication skills through realistic virtual training, reducing teacher workload. Future iterations will integrate AI scoring and simulator mannequins to validate long-term impacts on clinical competency.
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. Redesign one low-stakes assessment: require a first attempt, an AI-assisted revision, a source-check log and a short oral explanation. Use the same rubric for the comparison task. 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. Blind-score reasoning, factual accuracy and unaided transfer on a fresh task. Record suspected misconduct separately from demonstrated evidence. 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
Hongtao Liu; Huihui Cheng; Yun Zhang; Ying Huang; Danting Tan; Zheng Wang; Fan Zhang; Shaopeng Ming. Revolutionizing clinical skills training with Generative AI: the Intelligent Inquiry Training System framework. Frontiers in Medicine (2026-04-24). https://doi.org/10.3389/fmed.2026.1781314
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.