The U.S. National Institute of Standards and Technology (NIST) Artificial Intelligence Risk Management Framework (AI RMF 1.0; NIST AI 100-1) is voluntary, sector-agnostic, and non-prescriptive. Its companion AI RMF Playbook supplies 72 actionable subcategories across four functions - Govern, Map, Measure, and Manage - but leaves to each organization the work of selecting, scoping, and operationalizing them. This paper develops a use-case AI RMF Profile with a temporal-profile template for a class of organizations that the framework does not address by name: federal instructional-design, training, and academic units. These organizations are AI deployers and users, not frontier-model developers: they author courseware, run learning-management and adaptive-learning systems, field AI tutoring, and use AI in assessment and AI-text detection. The Profile applies an explicit deployer lens to all 72 subcategories, retaining 71 as in-scope (Govern 19, Map 18, Measure 21, Manage 13) and marking 1 Not Applicable with a stated reason, reconciling to the full set with no silent omissions. For each in-scope subcategory it pairs the verbatim NIST control language - description, suggested actions, and documentation questions - with a generic applicability analysis for training and academic deployment and blank current-state/target-state fields the adopter completes. A reproducible build script renders the Profile directly from the pinned machine-readable Playbook used for this version, so the artifact cannot drift from that public-domain source through transcription error. The contribution is the selection logic, the deployer-oriented applicability analysis, the reusable temporal-profile scaffold, and the synthesis; the NIST control text is quoted and attributed as a U.S. Government work in the public domain. The Profile is offered as a voluntary template, not a completed Current Profile, completed Target Profile, certification, or compliance mandate.
Ruchir Bakshi (Tue,) studied this question.