Generative AI · Education leadership

Actionable companion · Assessment and evidence of learning

From evidence to district action: Evaluating artificial intelligence large language models in dental education: a cross-sectional survey on usage, perceptions, and integration at a U.S. dental school

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.

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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:

Introduction The adoption of artificial intelligence (AI) in higher education presents opportunities and challenges for dental education. This study explores the use of Large Language Model (LLM) based AI tools, including ChatGPT and Grammarly AI, among faculty and students at the UTHealth School of Dentistry in Houston (UTSDH). This research assessed usage patterns, perceived benefits and concerns, and AI training demand. Methods A piloted, cross-sectional survey was administered via email. The survey included Likert-scale, multiple-choice, and open-ended questions. Respondents provided demographics and rated their LLM-based AI tools for use and perceptions in educational, clinical, and research contexts. Data was analyzed using Kruskal–Wallis and Tukey–Kramer–Nemenyi tests. Qualitative responses were analyzed thematically. Results Among 243 respondents, 66% of faculty and 73% of students reported using LLM-based AI tools, primarily for writing and educational tasks. Students were more likely to perceive LLM-based AI tools as beneficial (p < 0.01), while faculty showed stronger demand for AI training (p < 0.05). Gender differences were significant, with males were more supportive of AI in research tasks (p < 0.05). User experience ratings differed, with students rating ChatGPT more favorably across all categories. Discussion LLM-based AI tools (e.g., ChatGPT and Grammarly AI) are becoming increasingly relevant in dental education, particularly in academic writing and concept learning. While students are leading early adoption, faculty expressed a strong need for structured AI training, highlighting fertile ground for development programs. Clinical and research applications remain underdeveloped but promising. Addressing these gaps through tailored education, ethical guidelines, and institutional support will be essential for optimizing AI's potential across dental education and practice.

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. 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.
  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, 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

Celine Sheng; Camie McFarland; Nikola Angelov; Sridhar V. K. Eswaran; Richard Halpin; Jennifer Chang. Evaluating artificial intelligence large language models in dental education: a cross-sectional survey on usage, perceptions, and integration at a U.S. dental school. Frontiers in Digital Health (2026-06-08). https://doi.org/10.3389/fdgth.2026.1786363

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.