AI in Government

A key component of the 2025 Presidential Management Agenda (PMA), AI is critical for leveraging data and technology to improve government efficiency, eliminate waste, and strengthen American competitiveness.

1. AI Foundations and Fundamentals
   • Introduction to Artificial Intelligence (AI)
   • Evolution of AI technologies
   • Open generative AI vs. closed AI systems
   • AI capabilities and limitations
   • AI in the federal workforce
   • Human-AI collaboration
2. AI for Decision-Making and Analytics
   • AI-enhanced decision-making
   • AI-based Decision Support Systems (DSS)
   • AI techniques for analytics
   • Using AI to analyze federal datasets
   • AI-supported operational workflows
   • Simulation modeling with AI
3. Machine Learning Concepts
   • Supervised learning
   • Unsupervised learning
   • Decision trees
   • Random forests
   • Expert systems
   • Simulation modeling
   • Federated learning
4. AI Ethics, Governance, and Regulation
   • AI ethics and governance
   • Federal AI guidance
   • Responsible AI use
   • AI transparency
   • AI risk mitigation
   • AI oversight and guardrails
   • Ethical considerations in AI implementation
5. AI Risk Management and Safe Use
   • Identifying AI-related risks
   • Context and oversight risks
   • Safe AI practices
   • Supervising AI-supported work
   • AI boundaries and review processes
   • Reassessing AI use over time
6. Prompt Engineering and AI Interaction
   • Building effective prompts
   • Baseline prompting
   • Advanced prompting techniques
   • Structured iteration with AI
   • Prompt-writing exercises
   • Improving AI-generated outputs
7. Data Privacy and Security in AI
   • Protecting proprietary and sensitive information
   • Data protection measures for AI systems
   • AI data privacy concerns
   • Differential privacy
   • Blockchain for data privacy
   • Data integrity and compliance
8. AI Adoption and Organizational Integration
   • AI implementation planning
   • Stakeholder engagement for AI initiatives
   • AI workforce integration
   • Change management for AI adoption
   • Building AI-enabled teams
   • Establishing organizational guardrails
9. AI for Workplace Productivity
   • AI for writing and editing
   • AI for research and summarization
   • AI for low-risk/high-value tasks
   • AI-assisted collaboration
   • AI-supported documentation practices

AI Course Skills Summary

1. AI Fundamentals
2. Understand AI concepts and terminology
3. Recognize AI capabilities and limitations
4. Understand generative AI and AI systems
5. Apply AI concepts in workplace environments

AI-Assisted Decision-Making

1. Use AI to support decision-making
2. Analyze data with AI-supported tools
3. Evaluate AI-generated recommendations
4. Apply AI in decision support systems (DSS)
5. Combine AI insights with human judgment

Prompt Engineering

1. Write effective AI prompts
2. Improve AI responses through iterative prompting
3. Use structured prompting techniques
4. Refine prompts for research, writing, and analysis

AI Productivity Skills

1. Use AI for writing and editing
2. Summarize and research documents with AI
3. Automate repetitive tasks
4. Improve workflow efficiency using AI tools
5. Create AI-supported documentation

AI Risk Management

1. Identify AI-related risks
2. Establish safe AI usage boundaries
3. Monitor and review AI outputs
4. Assess AI workflow risks
5. Apply oversight and governance practices

AI Ethics and Governance

1. Apply ethical AI principles
2. Understand AI governance and transparency
3. Recognize AI bias and fairness concerns
4. Support responsible AI implementation
5. Align AI use with policies and regulations
6. AI Data Privacy and Security
7. Protect sensitive and proprietary data

Apply AI data privacy practices

1. Understand compliance and consent requirements
2. Maintain secure AI-enabled workflows
3. Identify data integrity risks

AI Leadership and Organizational Skills

1. Lead AI adoption initiatives
2. Establish AI guardrails and standards
3. Support organizational AI integration
4. Address workforce concerns about AI
5. Manage AI-related change initiatives

Machine Learning Awareness

1. Understand supervised and unsupervised learning
2. Recognize decision trees and random forests
3. Understand simulation modeling and expert systems
4. Identify emerging AI technologies and trends

AI Ethics and Governance Courses: 4611, 4614, 4691, 4692

1. Apply ethical AI principles
2. Understand AI governance and transparency
3. Recognize AI bias and fairness concerns
4. Support responsible AI implementation
5. Align AI use with policies and regulations

AI Data Privacy and Security

1. Protect sensitive and proprietary data
2. Apply AI data privacy practices
3. Understand compliance and consent requirements
4. Maintain secure AI-enabled workflows
5. Identify data integrity risks
6. Courses: 4614, 4690