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Designing and Implementing Agentic Generative AI Professional Learning for the U.S. Federal Workforce: An Education-focused Framework for AI Literacy, Ethical Practice, and Transfer to Work
TOTAL VIEWS: 496
Abstract
Agentic generative AI systems extend large language models by enabling goal-setting, multi-step planning, tool use, and iterative self-correction—capabilities that can significantly enhance workplace productivity but also require new forms of professional learning. In workplace settings, these capabilities can accelerate drafting, synthesis, and analysis tasks; however, the educational challenge is to prepare adult learners to collaborate with AI responsibly while maintaining professional judgment, data stewardship, and accountability. For the U.S. federal workforce, AI capability is not only a technology issue but also a workforce education issue: agencies require scalable professional learning programs that build AI literacy, safe-use habits, and measurable transfer of learning to job performance. This paper reframes agentic generative AI adoption as an educational design problem and proposes an education-focused framework for training federal employees. Drawing on AI literacy research, adult learning theory, experiential and workplace learning, and evaluation science, we outline (a) a competency model for agentic AI collaboration, (b) a modular curriculum blueprint aligned with adult learners’ needs and federal operational constraints, (c) pedagogical strategies for secure, scenario-based practice in sandboxed environments, and (d) an assessment and program evaluation plan that emphasizes performance evidence, learning transfer, and ethical compliance. The framework supports differentiated pathways for non-technical staff, technical specialists, and leaders. By positioning agentic AI training as lifelong learning and professional development, the proposed approach helps educational leaders and public-sector training units design programs that improve learner confidence, reduce automation bias, strengthen in-formation governance, and build organizational capacity for trustworthy AI use.
Keywords
Agentic generative AI; AI literacy; adult education; professional learning; federal workforce development; instructional design; training evaluation
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How to cite this paper
Designing and Implementing Agentic Generative AI Professional Learning for the U.S. Federal Workforce: An Education-focused Framework for AI Literacy, Ethical Practice, and Transfer to Work
How to cite this paper: Satyadhar Joshi, Noor Zulfiqar, Muhammad Usman Asif, Sana Kazin. (2026). Designing and Implementing Agentic Generative AI Professional Learning for the U.S. Federal Workforce: An Education-focused Framework for AI Literacy, Ethical Practice, and Transfer to Work. The Educational Review, USA, 10(2), 103-110.
DOI: http://dx.doi.org/10.26855/er.2026.02.007
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