Better Briefing Notes, or Better Briefing Note Writers?

A Teaching Innovation Project Using Generative AI (GenAI) in Public Administration Education

Project Overview

This project explores an innovative approach to teaching briefing note writing — a crucial skill for public servants — by integrating generative AI as a learning tool in graduate public administration courses. The research addresses a fundamental question:

  • Do students using GenAI genuinely improve their independent writing skills, or are they simply producing better documents while their core competencies remain undeveloped?

The Challenge

Briefing notes are essential decision-support documents in government, requiring policy analysts to synthesize complex information into clear, actionable recommendations. With the rapid advancement of generative AI tools that can perform similar synthesis tasks, educators must determine how to prepare students for an AI-enabled professional environment while ensuring they develop strong independent analytical and writing capabilities.

The Innovation

The project compares two approaches to teaching briefing note writing:

  • Traditional Approach: Students independently draft briefing notes using standard templates and established methods, focusing on issue identification, background analysis, options assessment, and recommendations.

  • AI-Augmented Approach: Students begin with an AI-generated draft, then engage in critical evaluation and enhancement. They must contextualize content for specific jurisdictions, verify facts, assess political nuances, and refine language to meet professional standards—all while documenting their process and justifying improvements.

Research Goals

  • Evaluate whether AI-supported learning enhances or hinders long-term skill development

  • Measure students' ability to critically engage with AI-generated content

  • Establish evidence-based practices for responsible AI integration in education

  • Develop assessment methods that capture both AI-assisted and independent capabilities

Impact & Significance

This research contributes to broader discussions about AI in education and professional training. By systematically evaluating how generative AI affects skill development, the project aims to inform teaching practices across public administration programs and beyond. The findings will help educators navigate the balance between leveraging AI's capabilities and ensuring students develop essential professional competencies.

Timeline

2025: Refine methodology, finalize research design, and implement in-class experiments with approximately 25 graduate students.

2026: Analyze results, evaluate impact on student learning outcomes, and disseminate findings through academic publications and conference presentations.

Knowledge Sharing

Research findings will be shared through:

  • Publications in the Journal of Public Affairs Education and Teaching Public Administration

  • Presentations at the Canadian Association of Programs in Public Administration conference

  • Workshops for the Canada School of Public Service and the University of Regina Centre for Teaching and Learning

  • Open-access resources for educators integrating AI into their teaching practice

Principal Investigator

Dr. Justin Longo
Associate Professor
Johnson Shoyama Graduate School of Public Policy
University of Regina

This project is supported by the University of Regina President's Teaching and Learning Scholars Program, which advances innovative, evidence-based approaches to integrating generative AI in higher education.