# AI & Data Literacy for the Government Workforce Course (Self-Paced)

Canonical URL: <https://www.graduateschool.edu/courses/ai-data-literacy-for-the-government-workforce-course-self-paced>

## Overview

Artificial intelligence is already embedded in government operations. This four-hour, hands-on training equips government employees with the practical skills to use AI tools effectively and the critical thinking skills to verify AI output before it reaches a decision-maker's desk.

What sets this course apart: verification comes first. Before participants touch an AI tool, they learn the DIG framework for setting up analysis, a seven-point AI Validation Checklist, and an AI Decision Log for documenting their process. These tools become the through-line for the entire course, culminating in a hands-on lab where participants analyze a real federal operations dataset using AI and discover planted data quality issues that AI handles silently and incorrectly.

Participants will also explore real-world government AI applications, current federal AI policy direction, ethical risks including bias and deepfakes, and a prompt framework for writing effective AI prompts. No technical background required.

## What you'll learn

- Define basic AI and data concepts including algorithms, models, structured vs. unstructured data, and data visualization
- Recognize AI applications already in use across government — fraud detection, document automation, chatbots, and predictive analytics
- Interpret data visualizations and AI-generated output critically, including prediction scores, category labels, and generated insights
- Identify ethical risks in AI use including bias at every pipeline stage, hallucinations, and deepfake threats
- Apply responsible AI practices using the DIG framework, the ACHIEVE decision model, and the RACE prompt framework
- Verify AI output using a structured seven-point validation checklist and document decisions in an AI Decision Log

## Curriculum

#### 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

## Instructors

### Steve Pesklo — Instructor

Steve is an energetic trainer who focuses on applying technical concepts to everyday work practices. He is the founder and president of SoftLake Solutions, a company that specializes in providing data and AI applications to identify fraud for Internal Audit, Criminal Investigations, Forensic Accounting, Privacy, and Compliance.

Steve brings a large amount of experience across multiple industries and government agencies. He is an expert in implementing large data analysis projects across the world, including Inland Revenue in the UK and Argentina, New Zealand, Africa and across Europe. Previously, he was the manager of Data Architecture and Data Services for a large mortgage company. He is a frequent speaker on data analytics and project management topics and speaks fluent German. He has been teaching at the Graduate School for over 10 years.

Steve has an M.B.A. from the University of St. Thomas and a B.S. in Computer Science from California Lutheran University and the Universität Salzburg in Austria. He is certified as a Certified Fraud Examiner (CFE), Project Management Professional (PMP), and a Certified ScrumMaster (CSM).

## Pricing

**Tuition:** $675