Think Smarter About AI: Critical Thinking Skills for Government Employees Course
IDTECH7250

Module 1: AI and Critical Thinking in the Federal Workplace

• Introduction to AI: what AI is, how generative AI works, and common federal workplace applications.
• AI in the federal workplace: current and emerging use cases, opportunities for efficiency, and risks of over-reliance.
• Critical, mechanical, and emotional thinking: how different thinking styles impact decisions, automation bias, and the role of human judgment.
• Human-in-the-loop decision-making: why AI should support rather than replace human judgment, and accountability in AI-assisted work.

Module 2: Evaluating AI Outputs and Information Credibility

• Fact vs. opinion in AI responses: how AI blends facts, assumptions, and probability, and how to recognize unsupported claims.
• Evaluating information credibility: applying CRAAP-style evaluation principles, including currency, relevance, authority, accuracy, and purpose.
• Common AI risks: hallucinations, bias, overconfidence, incomplete context, and false certainty.
• Verifying AI outputs: cross-checking information, evaluating sources and evidence, and recognizing gaps in reasoning.

Module 3: Logical Reasoning and Prompting for Better AI Results

• Logical reasoning fundamentals: premises, inferences, and conclusions in AI-supported analysis.
• Recognizing faulty logic: hasty generalizations, false dichotomies, unsupported assumptions, and non sequiturs.
• Asking better questions: Socratic questioning techniques and critical analysis prompts for deeper AI-supported inquiry.
• Prompt engineering fundamentals: writing clear prompts, providing context and constraints, and refining outputs iteratively.
• AI as a thinking partner: using AI for brainstorming, drafting, analysis support, and scenario evaluation.

Module 4: Applying AI and Critical Thinking to Federal Work Scenarios

• Practical federal use cases: research and analysis, drafting communications, meeting summaries, and policy and program support.
• Responsible AI use: security and privacy considerations, ethical obligations, and human oversight practices.
• Decision-making with AI: evaluating recommendations, considering stakeholder impacts, and balancing efficiency, accuracy, and risk.
• Building an AI-ready mindset: adaptability, continuous learning, and critical evaluation habits for responsible long-term AI use.