Reskilling the U.S. Military Workforce for the Agentic AI Era: A Framework for Educational Transformation

Abstract

The rapid emergence of agentic artificial intelligence (AI) systems represents a paradigm shift in military operations, demanding fundamental transformation of US military education. This paper presents a comprehensive framework for reskilling and redesigning military education to address critical workforce readiness gaps in the era of autonomous AI systems. Utilizing a mixed-methods review of defense reports, case studies, and quantitative workforce data, this paper develops a comprehensive framework for reskilling the defense force to address critical readiness gaps in the era of autonomous AI. Through analysis of current AI adoption trends, quantitative workforce assessments, and educational limitations, we identify that only 10-15% of military personnel feel adequately trained for agentic AI integration despite significant investments exceeding $600-900 million in next-generation AI capabilities. Our proposed solution features a multi-tiered educational architecture with progressive competency levels, a continuous curriculum development pipeline, and layered technology integration. The framework addresses identified skills gaps through foundational AI literacy for all personnel, operational competence for mid-career leaders, and strategic AI leadership development. Implementation strategies include phased rollout over 24-36 months, multi-stakeholder engagement models, and comprehensive assessment mechanisms. Findings demonstrate that successful agentic AI integration requires not only technical upskilling but also fundamental changes in pedagogical approaches, institutional culture, and resource allocation—with optimal distribution of 30-40% to technology infrastructure, 20-25% to faculty development, 15-20% to curriculum design, and program evaluation. This research provides actionable recommendations for military education institutions to prepare personnel for human-AI teaming, autonomous system oversight, and ethical AI application in complex operational environments. decrease medical as well as financial burden, hence improving the management of cirrhotic patients. These predictors, however, need further work to validate reliability. All results and proposals are from cited literature.

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