Actionable companion · Assessment and evidence of learning
From evidence to district action: ChatGPT to Enhance Learning in Dental Education at a Historically Black Medical College
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:
The recent rise of powerful large language model (LLM)-based AI tools, exemplified by ChatGPT and Bard, poses a great challenge to contemporary dental education. It simultaneously offers a unique resource that potentially complements today’s teaching and learning, where existing widely available learning resources have often fallen short. Although the LLM tools will shape both the clinical and educational aspects of dentistry profoundly, the didactic curricula, which primarily rely on lecture-based courses where instructors impart knowledge through presentations and discussions, need to be upgraded urgently. In this paper, we used dental course materials, syllabi, and textbooks adopted currently in the School of Dentistry (SOD) at Meharry Medical College to assess the potential utility and effectiveness of ChatGPT in dental education. We collected the responses of the chatbot to questions as well as students’ interactions with it for assessment. Our results showed that ChatGPT can assist in dental essay writing and generate relevant content for dental students, in addition to other benefits. The limitations of ChatGPT were also discussed in the paper.
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
Khandoker Rahad; Kianna Martin; Ihunna Amugo; Shania Ferguson; Angela Curtis; Anniya Davis; Pandu Gangula; Qingguo Wang. ChatGPT to Enhance Learning in Dental Education at a Historically Black Medical College. Dental research and oral health (2024-01-25). https://doi.org/10.26502/droh.0069
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.