AI & Data Literacy for the Federal Workforce

Artificial intelligence (AI) and data are reshaping how organizations work and make decisions. This interactive training introduces participants to the essentials of AI and data literacy, helping them understand, interpret, and use data-driven technologies more confidently in their daily work.

Through hands-on activities and practical examples, participants will learn key concepts, common terminology, and best practices for working with AI tools and interpreting data. The course focuses on real-world applications and offers actionable strategies to build digital confidence, support informed decision-making, and encourage a more data-savvy workplace.

What You Will Learn

• Define basic concepts and terminology related to artificial intelligence and data.
• Recognize how AI and data are used in workplace operations and decision-making.
• Interpret simple data visualizations and AI outputs relevant to everyday work.
• Identify common risks and ethical considerations related to AI and data use.
• Apply foundational practices to improve data literacy and support responsible AI use.

Course Outline

Module 1: AI Foundations & Responsible AI Data Workflow

• 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
• Prompt engineering: weak vs. strong prompts with federal examples
• The RACE prompt framework: Role, Action, Context, Examples

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

• Generative AI in your federal workflow: where AI helps vs. where humans must decide
• Hands-on dataset 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
• Build your personal action plan