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Background <p>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<sup>®</sup>) content that they must master independently. Curriculum mapping institutional curriculum objectives and assessments to USMLE<sup>®</sup> content may improve the perception of this alignment by showing the scope and sequence of board-relevant topics through dashboards and the connected assessments.</p> Methods <p>To align USMLE<sup>®</sup> content to our curriculum, we developed and operationalized a granular, machine‑readable coding library derived from the 2025 USMLE<sup>®</sup> Content Outline and demonstrate its utility for session‑level mapping and assessment tagging.</p> Results <p>Using a top‑down, left‑to‑right parse of the USMLE<sup>®</sup> 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.</p> <p>We then used a structured prompt with a generative AI assistant to propose USMLE<sup>®</sup> Content Outline codes for preclerkship session-level learning objectives. The library spans 18 USMLE<sup>®</sup> 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<sup>®</sup> Content Outline domain.</p> Conclusions <p>These linked USMLE<sup>®</sup> 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<sup>®</sup> Content Outline. This coding library is generalizable across courses, phases, and external institutions and can be mapped by AI technologies.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月15日

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  • Robert M. Badeau,
  • Thea Mallini,
  • Jennifer Laury,
  • Vincent VanBuren,
  • Danielle Dickey

摘要

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.