Overview

Federal labor relations work requires careful attention to contract language, case precedent, management rights, bargaining obligations, and employee protections. This live online course teaches experienced federal LR and ER professionals how to use generative AI as a responsible support tool for analyzing collective bargaining agreements, researching grievance and arbitration issues, and comparing ULP fact patterns to FLRA case law.

Through guided instructor demos, hands-on practice, and applied exercises, participants will learn how to build sanitized prompts, avoid confidentiality and privilege risks, evaluate AI-generated outputs, and verify every citation or legal reference against authoritative sources. The course concludes with a capstone exercise where participants apply AI to a realistic labor relations scenario and build a reusable prompt library for future LR work.

Syllabus

Module 1: AI Foundations and Compliance for Labor Relations

Understand what generative AI is and how it can support federal labor relations work
Review responsible AI use considerations based on OPM and OMB guidance
Identify labor relations-specific guardrails for confidentiality, privilege, and pre-decisional content
Learn what information should not be entered into AI tools, including employee names, case numbers, and draft management positions
Recognize hallucinated citations and other common AI risks in legal-adjacent work
Evaluate sample LR scenarios to determine whether AI use is appropriate, appropriate with caution, or not appropriate

Module 2: CBA Analysis with AI

Build effective prompts for contract analysis using role, context, constraints, and output format
Practice sanitizing collective bargaining agreement language before using AI tools
Use AI to extract specific clauses, such as official-time provisions, from a sample CBA
Compare grievance procedure language across two sample CBAs
Review AI-generated summaries for accuracy against the original contract language
Identify risks such as missed cross-references, inaccurate paraphrasing, and misread terms like “shall” and “may”

Module 3: Grievance Research with AI

Explore how AI can support grievance research, arbitration summaries, and fact-pattern timelines
Use AI to summarize an arbitration decision excerpt and identify the controlling rationale
Draft a Step 2 management response outline using a sanitized grievance scenario
Apply AI to information request analysis, including particularized-need considerations under §7114(b)(4)
Identify AI risks such as fabricated FLRA decisions, misstated outcomes, and oversimplified procedural history
Verify every cited case, quoted statute, and referenced source against authoritative materials

Module 4: ULP Comparison and Capstone

Use AI to compare sanitized fact patterns to existing unfair labor practice case law
Draft fact-pattern comparisons, position statement outlines, and witness-prep question lists
Review AI outputs for tone, bias, accuracy, and unintended admissions
Build a personal prompt library of vetted prompts for recurring labor relations tasks
Complete a capstone exercise using a realistic LR scenario, such as refusal to bargain, interference, formal discussion, or Weingarten allegations
Verify all AI-generated citations and self-assess the final output using a review checklist