Actionable companion · Assessment and evidence of learning

From evidence to district action: Governing Generative Artificial Intelligence in Anatomy Education: A Proposed Risk-Tiered Framework for Donor-Derived Material, Anatomical Fidelity, and Assessment

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:

Generative artificial intelligence (GenAI) is now routinely used in anatomy education to draft explanations, generate and interpret images, produce assessment items, and support feedback. Broad responsible-AI principles are well established, but they do not resolve the questions anatomy educators actually face: whether donor-derived material may be entered into an external system, what counts as adequate verification of a generated anatomical structure, and which assessment decisions must remain human. Anatomy faculty themselves report unclear governance in these areas and ask for discipline-specific policy. This article proposes an operational governance framework for GenAI in anatomy education, developed from a structured synthesis of 71 sources spanning direct anatomy studies, transferable health professions education (HPE) evidence, professional guidance, and ethical scholarship. Five discipline-specific pressure points motivate the proposal: donor-derived inputs retain stewardship obligations after digitization; anatomical fidelity is relational and structural rather than stylistic, so fluency and realism are not evidence of correctness; representation and clinically relevant variation form part of anatomical truth; assessment converts content error into consequential judgment about learners; and provenance, intellectual property, and disclosure support traceability and accountability. The framework governs the defined use case rather than the AI product. Purpose, audience, input and data class, consequence, scale, and degree of AI autonomy determine one of four proposed tiers (Low, Moderate, High, and Restricted/Avoid) under a conservative rule in which the highest triggered criterion sets the tier. Permitted uses then pass five control gates covering inputs and provenance, anatomical fidelity, variation and representation, assessment validity where applicable, and disclosure and accountability. Four decisions remain non-delegable to a machine, and approval is treated as a revisable state maintained through a monitoring, incident-response, and revalidation loop rather than a one-time authorization. The framework is an evidence-informed proposal for local adaptation, not a validated instrument; it has not been prospectively evaluated, its tier thresholds are not empirically calibrated, and local law, donor-program policy, and institutional governance remain controlling.

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

Ahmed Mahgoub; Anaida Singh; Ayman Mahgoub. Governing Generative Artificial Intelligence in Anatomy Education: A Proposed Risk-Tiered Framework for Donor-Derived Material, Anatomical Fidelity, and Assessment. Cureus (2026-09-02). https://doi.org/10.7759/cureus.115656

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.