# AI for Federal Employee Relations Course (Self-Paced) Canonical URL: ## Overview This self-paced course helps federal Employee Relations professionals use generative AI responsibly for ER-specific drafting, research, documentation, and review tasks. Participants will learn how AI can support proposal letters, decision letters, PIPs, leave and AWOL correspondence, reasonable accommodation notes, case research, and performance or conduct documentation. Through structured lessons, demonstrations, practice activities, and applied exercises, participants will learn how to protect PII, sanitize case facts, verify AI-generated research, and review AI outputs for accuracy, bias, tone, due-process gaps, and compliance risks. This course focuses on individual-employee ER work and intentionally excludes CBA, ULP, bargaining, and arbitration preparation topics. ## What you'll learn - Identify ER tasks that are appropriate for AI support and separate them from LR-related work - Sanitize ER case details before using AI tools - Draft ER letters, PIPs, leave correspondence, and reasonable accommodation notes with structured prompts - Use AI to summarize ER-relevant decisions, policy materials, and case issues - Improve performance and conduct documentation using behavior-based, review-ready language - Build a reusable prompt library for recurring federal ER workflows ## Curriculum #### Module 1: ER, Not LR: Setting the Frame - Distinguish Employee Relations work from Labor Relations work using an ER vs. LR decision aid - Review common ER work products, including proposal letters, decision letters, PIPs, leave letters, reasonable accommodation correspondence, and grievance responses - Identify where AI can support ER workflows and where it should not be used - Understand why bargaining, ULPs, CBA analysis, and arbitration preparation are outside the scope of this course - Review how this course pairs with the companion AI for Federal Labor Relations Specialists course - Complete an ER-or-LR sorting exercise using sample workplace scenarios #### Module 2: Guardrails First: Privacy, PII, and Records - Review Privacy Act and PII considerations related to ER case files - Identify sensitive case details that should not be entered into public AI tools - Understand agency AI-use policies, approved tools, and sanitized sandbox options - Explore recordkeeping considerations for AI-generated drafts and ER case documentation - Consider bias, fairness, and EEOC guidance related to AI in employment decisions - Sanitize a sample ER fact pattern by removing names, SSNs, medical information, dates, and other identifying details #### Module 3: AI-Assisted ER Letter Drafting - Review the core elements of defensible ER correspondence, including notice, specificity, due process, and Douglas factors where applicable - Build structured prompts using role, context, constraints, format, and a review checklist - Explore AI-assisted drafting for a proposed removal letter under 5 CFR 752 - Explore AI-assisted drafting for a performance improvement plan under 5 CFR 432 - Identify common AI drafting issues, including vague specifications, missing due-process elements, and tone mismatch - Draft a leave or AWOL letter from a provided fact pattern and self-check it against a defensible-letter rubric #### Module 4: Case Research with AI - Use AI to summarize MSPB decisions, EEOC decisions, and ER-relevant agency policy materials - Apply a verification workflow that requires every cited authority to be checked against an accessible source - Compare AI-generated research outputs to OPM Employee Relations reference materials and agency policy - Recognize hallucination patterns common in legal and HR research - Use AI to organize key issues from a Chapter 75 fact pattern - Draft a one-page issue memo and verify two cited authorities against the original source materials #### Module 5: Performance and Conduct Documentation - Turn vague supervisor notes into specific, behavior-based documentation - Use AI to support performance documentation while preserving accuracy and reviewability - Structure PIP content around measurable standards, reasonable opportunity, and contemporaneous documentation - Draft Douglas-factor-aware conduct narratives for disciplinary documentation - Organize reasonable accommodation interactive-process notes from an ER perspective - Convert a messy supervisor email into a structured performance memo and review it against a rubric #### Module 6: Capstone and Personal Prompt Library - Build a five-prompt ER starter library for recurring casework - Create reusable prompts for ER letters, PIPs, case research, documentation cleanup, and reasonable accommodation notes - Apply a final review checklist covering privacy, accuracy, due process, tone, and records - Choose a recurring sanitized ER task and build an AI prompt to support that workflow - Test and revise the prompt based on output quality and compliance considerations - Create a practical 30-60-90 day adoption plan for responsible AI use in ER work ## Pricing **Tuition:** $675