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From parallel to aligned: a United States Medical Licensing Examination (USMLE®) mapping and assessment-tagging framework for medical education.

Primary research

#382

T1digested
Topic
AI in Medical Education
First seen
2026-07-16 23:33:02
Last seen
2026-07-16 23:33:02

Source raw items (1)

  • Semantic Scholar2026-07-16 23:31:52
    From parallel to aligned: a United States Medical Licensing Examination (USMLE®) mapping and assessment-tagging framework for medical education.

    BACKGROUND Medical students often experience a "parallel curriculum," which is perceived as a real misalignment between what is taught in the formal institutional curriculum and, for licensing, the United States Medical Licensing Examination (USMLE®) content that they must master independently. Curriculum mapping institutional curriculum objectives and assessments to USMLE® content may improve the perception of this alignment by showing the scope and sequence of board-relevant topics through dashboards and the connected assessments. METHODS To align USMLE® content to our curriculum, we developed and operationalized a granular, machine‑readable coding library derived from the 2025 USMLE® Content Outline and demonstrate its utility for session‑level mapping and assessment tagging. RESULTS Using a top‑down, left‑to‑right parse of the USMLE® Content Outline, we constructed a hierarchical code library with the parent-child relationships preserved. "Normal processes" were assigned 1.0 codes within each system to distinguish physiology/anatomy from pathology. We then used a structured prompt with a generative AI assistant to propose USMLE® Content Outline codes for preclerkship session-level learning objectives. The library spans 18 USMLE® Content Outline domains and supports alignment at multiple levels of granularity. Early use cases include (1) session‑level mapping to surface gaps and redundancies and (2) assessment tagging to enable exam blueprinting by USMLE® Content Outline domain. CONCLUSIONS These linked USMLE® Content Outline codes can, in the future, be visualized at a course- or phase-level to provide faculty and students linkages of session-level course objectives and assessments to the USMLE® Content Outline. This coding library is generalizable across courses, phases, and external institutions and can be mapped by AI technologies.