AI & Data Literacy for the Federal Workforce

Module 1: AI Foundations & Why Verification Comes First

• Core AI and data concepts: algorithms, models, structured vs. unstructured data
• Where AI training data comes from — and why it matters for output quality
• The AI Validation Checklist: a seven-point verification framework
• What hallucinations look like in practice — and why they're dangerous
• The DIG framework: Describe, Introspect, Goal-Set before every AI analysis
• The AI Decision Log: documenting prompts, output, verification, and decisions

Module 2: AI in Government & Interpreting Data

• AI in government operations: fraud detection, document automation, chatbots, predictive analytics
• Human-AI partnership: why human oversight isn't optional
• The ACHIEVE framework: deciding when to use AI
• Common chart types and how to read them critically
• Common pitfalls in data interpretation: correlation vs. causation, misleading averages, and more
• Interpreting AI output: prediction scores, category labels, and generated insights

Module 3: Responsible AI & Ethics

• Current federal AI policy direction and the March 2026 national framework
• Bias and fairness: real examples from TSA, healthcare, and criminal justice
• Deepfakes and synthetic media: risks and protective practices for federal employees
• Data privacy and PII awareness: what to share and what to protect
• How the AI Decision Log connects to accountability, transparency, and FOIA readiness

Module 4: Practical Skills & Your Action Plan

• Prompt engineering: weak vs. strong prompts with federal examples
• The RACE prompt framework: Role, Action, Context, Expectation
• Generative AI in your federal workflow: where AI helps vs. where humans must decide
• Hands-on lab: Use AI to analyze a federal operations dataset using DIG, RACE, and the Validation Checklist
• Know your agency's AI landscape: Chief AI Officer, approved tools, governance structure
• AI and data literacy by role: how today's skills apply to your specific job function