Generative AI · Education leadership

Actionable companion · Staff capability and AI literacy

From evidence to district action: Integration of Artificial Intelligence Into a Medical Curriculum: Evolving Student Perceptions and Faculty Development Challenges

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:

Background and methods: The rapid integration of generative artificial intelligence (AI) into medical education presents both opportunities and continuing challenges for faculty and learners alike. This study provides an update on ongoing efforts to integrate AI into the curriculum at Quillen College of Medicine, East Tennessee State University in the United States, focusing on evolving student perceptions and a structured faculty development initiative. Using our previously published satisfaction instrument, we tracked shifting attitudes toward AI among our pre-clerkship and clerkship students. Results: Incoming students demonstrated increasing awareness of AI's impact on education and the profession, alongside a stronger commitment to developing core clinical competencies independent of technology. The response rate for AI use was higher in 2026 compared to 2025 (97%, n=68 vs. 89%, n=62) for rising third-year students (class sizes of 80). Those students showed a meaningful shift toward using AI for active learning, particularly practice question generation and self-testing (Mean 2.30, SD 1.10 for 2025; Mean 2.77, SD 1.14 for 2026), while upper-level clerkship students consistently prioritized the ability to function as competent clinicians without AI assistance. To address faculty readiness, we developed a multi-dimensional framework for responsible AI integration and implemented a series of faculty development workshops. The first workshop produced an increase in confidence (+0.75) and practical engagement (+0.43). A second workshop focused on ethical and policy considerations yielded greater critical awareness, though with a more cautious outlook. Feedback across both workshops consistently highlighted the need for hands-on training, prompt engineering instruction, and role-specific, tool-agnostic development pathways. Conclusions: Our findings underscore that integration of AI into medical education requires sustained, flexible, and mission-aligned faculty development. As AI becomes standard in clinical practice, equipping both educators and learners with the skills for responsible, thoughtful AI use is not optional; it is essential.

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. 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.
  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. 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

Kinner L Flaglor; Gabrielle Rueff; Paul Monaco; Antonio E Rusinol; Mark Hernandez. Integration of Artificial Intelligence Into a Medical Curriculum: Evolving Student Perceptions and Faculty Development Challenges. Cureus (2026-05-25). https://doi.org/10.7759/cureus.109621

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.