The U.S. Army emphasizes feedback as foundational for leader development, with ADP 6-22 and AAR processes mandating routine assessments to foster disciplined thinking and accountability. Professional Military Education (PME) aims to produce adaptive officers capable of disciplined initiative in volatile environments, requiring targeted feedback to shape judgment and ethical reasoning.
However, PME feedback often suffers from delays, generic content, and inconsistency due to curriculum demands, administrative tasks, and heavy instructor workloads, particularly with thousands of analytical writing assignments annually. This degradation hinders reflective practice and core competencies like intellectual agility. Instructor–AI teaming, exemplified by the Guided Analytical Recommended Feedback (GARF) system developed by the U.S. Army Command and General Staff College (CGSC) and the Army Software Innovation Center (ASIC), offers a promising solution. GARF analyzes student essays to generate potential developmental feedback, allowing instructors to focus on higher-order reasoning, operational judgment, and ethical considerations. This model enhances feedback depth, consistency, and equity, aligning with Army learning concepts to scale quality leader development without diminishing human mentorship.
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