# BAIS Government AI Course and Video Blueprint ## Course 1: Government AI Readiness Foundations Audience: - Public-sector employees - Teachers and school administrators - Workforce program participants - Early-career government candidates Format: - 6 modules - 60 to 75 minutes each - Can be delivered live, recorded, or hybrid - Each module includes a short lecture, live example, practice task, and reflection/reporting question Final learner output: - One AI-assisted workflow or project brief - One responsible-use checklist - One plain-English explanation of how the learner used AI and how they checked the result ## Module 1: AI in plain English Learning goals: - Explain what generative AI does. - Understand that AI predicts and generates, but does not "know" truth by default. - Identify appropriate and inappropriate uses. Lesson outline: 1. What AI is 2. What large language models do 3. Why AI can sound confident and still be wrong 4. The difference between drafting, advising, deciding, and executing 5. Where public-sector employees should pause before using AI Activity: - Give AI a messy paragraph and ask it to summarize, rewrite, and produce questions. - Compare the output to the original and mark what must be verified. Slide/demo idea: - "AI is a very fast draft partner, not an accountable decision maker." Quiz questions: - What is one reason AI can produce incorrect answers? - What kinds of information should not be pasted into a public AI tool? - Who is responsible for checking the output? ## Module 2: Responsible AI use in public work Learning goals: - Recognize privacy, security, bias, records, and trust risks. - Use a safe decision tree before putting information into AI. - Separate low-risk drafting from high-risk decision support. Lesson outline: 1. Sensitive data and confidentiality 2. Public records and documentation 3. Bias and fairness 4. Hallucinations and overconfidence 5. Human review 6. Procurement and approved tools Activity: - Sort 12 example use cases into green, yellow, and red categories. Slide/demo idea: - "The first AI skill is knowing when not to use it." Checklist: - Is the data sensitive? - Is the output used for a decision about a person? - Is the source verifiable? - Does an approved human own the final answer? - Can this be documented? ## Module 3: Prompting and verification Learning goals: - Write clear prompts. - Ask AI for structured output. - Verify claims, sources, and assumptions. Lesson outline: 1. Role, task, context, constraints, and output format 2. Asking for clarifying questions 3. Asking for sources without blindly trusting them 4. Comparing outputs 5. Creating reusable prompt templates Activity: - Learners turn one real job task into a reusable prompt. Prompt formula: You are helping with [role/task]. Use this context: [context]. Do not invent facts. If information is missing, ask for it. Return [format]. Before finalizing, list what needs human review. ## Module 4: AI for daily government workflows Learning goals: - Apply AI to real work without making it the decision maker. - Identify which workflows are worth improving first. - Create a repeatable human-in-the-loop process. Workflow examples: - Meeting summaries - Policy comparison - Public communication drafts - Training materials - Intake triage - FAQ creation - Translation draft review - Grant narrative first drafts - Cyber incident note organization - Lesson planning Activity: - Learners choose one workflow and create a before/after map. ## Module 5: Build lab Learning goals: - Build a useful artifact. - Apply safety and verification rules. - Prepare the artifact for a supervisor, teacher, or program lead. Artifact options: - Prompt library - Assistant plan - Workflow automation map - AI lesson plan - CyberAI project brief - Policy comparison template - Public communication draft workflow Required components: - What problem it solves - Who uses it - What information it needs - What AI does - What humans review - What risks exist - How success is measured ## Module 6: Capstone and reporting Learning goals: - Present the project. - Explain AI use clearly. - Document risks and next steps. Final report template: 1. Project title 2. Problem 3. Audience or user 4. AI use 5. Human review process 6. Skills learned 7. Risks and controls 8. Benefit to government, school, workforce, or public mission 9. Next step ## Course 2: CyberAI and Agent Readiness Audience: - CyberCorps/SFS scholars - Cybersecurity students - Entry-level public-sector cyber staff - Government cyber teams Course promise: - Understand how AI agents affect cybersecurity work and build a documented CyberAI project with human review. Modules: 1. AI and the new cyber workflow 2. AI agents and autonomous systems 3. Defensive use cases: triage, summarization, playbooks, detection engineering support 4. Offensive and misuse risks 5. Prompt injection, tool misuse, data leakage, and model hallucination 6. Human-in-the-loop operations 7. Work-based CyberAI project lab 8. Final report and career translation CyberAI project examples: - AI-assisted phishing report triage workflow - Cyber policy summarizer with verification checklist - Incident report drafting assistant - Threat intelligence summary workflow - Security awareness lesson builder - SOC analyst prompt library - AI-agent risk register ## Video scripts ### Video 1: Why government AI education matters now Length: - 4 to 6 minutes Script: AI is no longer a side conversation. The federal government is pushing schools, workforce programs, and agencies to prepare people for a world where AI is part of daily work. That does not mean every employee needs to become a machine learning engineer. It means people need practical AI literacy: what AI can do, where it fails, how to verify it, how to protect sensitive information, and how to apply it to real workflows. BAIS built its government AI education program around one idea: leave with capability, not just awareness. A training session should produce something useful, such as a prompt library, an improved workflow, a lesson plan, a CyberAI project brief, or a responsible-use checklist. For leaders, the question is not whether AI will affect your workforce. It already is. The question is whether your team will learn it safely, with structure, documentation, and practical judgment. BAIS helps public-sector teams turn federal AI education priorities into real training programs, live workshops, course materials, videos, and measurable outcomes. Call to action: - Schedule a BAIS government AI readiness call. ### Video 2: AI in plain English for public employees Length: - 8 to 10 minutes Script: Generative AI is best understood as a system that can draft, summarize, organize, classify, and reason over text or other information. It can be extremely helpful, but it can also be wrong, incomplete, biased, or overconfident. For public-sector employees, the safest mental model is this: AI can help prepare work, but it should not be treated as the accountable decision maker. You can use it to draft an email, compare policy language, summarize a meeting, organize notes, or create training materials. But you still need to verify facts, protect sensitive information, and make sure the final work follows your agency's rules. The first skill is not prompting. The first skill is judgment. Should this information go into AI? Is this an approved tool? Could this affect a person? Does the output need a source? Who reviews it? In the BAIS course, employees learn a simple framework: define the task, protect the data, prompt clearly, verify the output, and document the human review. ### Video 3: AI fluency for teachers and students Length: - 6 to 8 minutes Script: Students are already meeting AI. The question is whether they meet it with fear and shortcuts, or with skill, responsibility, and career awareness. AI education should not teach students to let a tool think for them. It should teach them how to ask better questions, verify claims, build useful projects, understand limitations, and explain their own thinking. For teachers, AI can help with planning, differentiation, examples, feedback drafts, and administrative work. But it also raises serious questions around integrity, privacy, fairness, and assessment. BAIS helps schools build AI education that is practical and responsible. Students leave with projects. Teachers leave with lesson plans, policies, and classroom examples. Leaders leave with a clearer path for implementation. ### Video 4: CyberAI and the future government workforce Length: - 8 to 10 minutes Script: Cybersecurity is one of the clearest places where AI will change work. Future cyber professionals will need to understand AI agents, automated workflows, defensive assistants, prompt and tool risk, and human review. That does not mean replacing analysts. It means preparing analysts to work with systems that can operate quickly, summarize large amounts of information, support triage, draft reports, and help organize incident response. But AI also creates new risks: prompt injection, data leakage, hallucinated analysis, over-automation, and adversarial use. CyberAI education must teach both capability and control. BAIS can support CyberAI project-based learning by helping students or teams design a work-based AI project, document the skills used, explain government relevance, and produce a final report. ## Instructor delivery checklist Before a live session: - Confirm audience and approved tool environment. - Confirm data rules. - Choose examples relevant to that audience. - Prepare slides, demo prompts, handouts, and final project template. - Set up pre-assessment. During session: - Teach with plain English. - Keep examples job-specific. - Separate AI assistance from accountable decision-making. - Make learners practice. - Collect questions and risk concerns. After session: - Collect artifacts. - Run post-assessment. - Send summary report. - Recommend next training or pilot.