Actionable companion · Staff capability and AI literacy
From evidence to district action: Generative Artificial Intelligence–Driven Clinical Case Simulation in Temporomandibular Disorder Education: ChatGPT Versus Real Patients
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:
ABSTRACT Background Temporomandibular disorders (TMDs) and orofacial pain (OFP) demand advanced diagnostic and clinical reasoning skills in dental education. Traditional simulations with real patients face limitations in availability and standardization. Generative artificial intelligence (GAI), such as ChatGPT‐3.5, has emerged as a potential tool for clinical training. Methods This blinded, cross‐sectional, crossover study involved 30 undergraduate dental students, each completing two simulated cases: one with ChatGPT‐3.5 and one with standardized real patients. Cases were developed and validated by TMD/OFP specialists via the Delphi method and incorporated into the AI through structured prompts. Quantitative parameters (such as responses, word count, follow‐up questions, reformulations, and diagnostic accuracy) and qualitative aspects (such as empathy, clarity, and relevant information) were analyzed. Results GAI simulations provided higher information density (231 vs.167 relevant units; p 0.05). Relevant information correlated positively with diagnostic accuracy (r = 0.484; p = 0.007), whereas total word count correlated negatively (r = −0.386; p = 0.035). Conclusions ChatGPT‐3.5 matched real patient simulations in diagnostic reasoning for TMD/OFP. Combining GAI's scalability and standardization with real patient variability may optimize clinical competency training.
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. Run a staff workshop using three authentic lesson-planning tasks. Ask participants to identify hallucinations, revise outputs and document when they reject the tool. 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. Measure error-detection accuracy before and after training, preparation time and the quality of the final lesson plans. 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
Paula Rodrigues‐Pereira; Maria Amália Pereira Dias‐Calças; Alex Moreira Mélo; Melissa de Oliveira Melchior; Lucas Gaspar Ribeiro; Antônio Pazin‐Filho; Jardel Francisco Mazzi‐Chaves; Laís Valencise Magri. Generative Artificial Intelligence–Driven Clinical Case Simulation in Temporomandibular Disorder Education: ChatGPT Versus Real Patients. Journal of Dental Education (2025-11-12). https://doi.org/10.1002/jdd.70104
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.