AI Innovation, Workflow Optimization, and Team Enablement
From Content Mapping to Copilot: Reimagining Learning Workflows with AI
Purpose-built Microsoft Copilot agents that automate content analysis and mapping inside the instructional design workflow.
Project overview
Curriculum development depends on synthesizing large volumes of source material across disparate systems, a labor-intensive process that slows speed-to-competency for the learners it ultimately serves. This initiative explored how purpose-built Microsoft Copilot agents could automate content analysis and mapping within the instructional design workflow.
The challenge
AI delivers value only when it is anchored to a well-defined problem and built into existing workflows. The goal was to find a high-impact use case where agents could reduce manual effort without compromising accuracy, design judgment, or trust in the output.
My role
I facilitated a workshop on effective prompting and creating Microsoft Copilot Agents. I developed custom agent instructions, shared agents, explored AI-driven content mapping, and identified opportunities to integrate AI into existing instructional design workflows.
My approach
- Collaborated with a team of four Learning Experience Designers to host an AI workshop focused on effective prompting and creating Microsoft Copilot Agents.
- Explored how customized agents could organize and map learning content into a reusable content database.
- Made information easier to access and apply during course development.
Outcomes
- The team earned second place in an internal AI hackathon for demonstrating how agent-driven content mapping could speed up onboarding course development and reduce manual analysis.
- Produced a reusable content database and custom agent instructions the team could apply to future course development.
Effective AI implementation begins with problem definition, not tool selection. The most valuable solutions are useful, trustworthy, and designed around how people actually work.