Micro-Course/Lead Magnet

Case Study

Tools: Storyline, Illustrator, Canva, ChatGPT
Timeline: 2 months
Client: IDOL Academy
Collaborators: IDOL Staff
Focus: AI integration, workflow mapping, performance support, narrative microlearning

Project Overview

This micro-course helps instructional designers confidently integrate AI into every stage of the ID process—from analysis to evaluation. Instead of tool lists or abstract theory, the experience gives designers clear, workflow-based applications supported by hands-on practice. Learners are guided by Addie, an AI “co-pilot” character who demystifies tools and models real-world use. The centerpiece is the AI Tools by Stage job aid, a workflow-aligned resource that shows where AI can save time, improve quality, and spark creativity.

The Challenge

Instructional designers felt overwhelmed by the explosion of AI tools, unsure where to start, and skeptical of AI output. Many used AI only for writing tasks, missing opportunities to streamline analysis, prototyping, asset creation, QA, and evaluation. Key problems included:

  • Scattered tools and unclear use cases

  • High cognitive load and low confidence

  • Lack of workflow clarity

  • Hesitation and distrust in AI-generated outputs

The course needed to build confidence, reduce overwhelm, and show exactly how AI fits into real ID work.

Design Approach

Solution Strategy

I created a story-driven micro-course paired with a comprehensive job aid organized by ID workflow stage. This structure gave learners both engagement (through narrative) and long-term utility (through performance support).

Design Priorities

  • Practical, task-based examples

  • Tool-agnostic recommendations (evergreen)

  • Lightweight modules with quick wins

  • Beginner-friendly with optional deep dives

  • Clear mapping of AI to authentic ID tasks

A long-form course or tool directory was rejected because neither solved the core problem: lack of workflow clarity.

Design & Development Process

Using Merrill’s First Principles:

  1. Analyze learner gaps through surveys & interviews

  2. Map ID workflow stages and identify AI touchpoints

  3. Prototype narrative, structure, and Addie’s role

  4. Build activities, prompts, and interactive practice

  5. Develop the AI Tools by Stage job aid

  6. Pilot, test, and refine for clarity and usability

Implementation Challenges

  • Rapidly evolving tools → Designed tasks, not tool-specific advice

  • Mixed-skill learners → Layered content for varied depth

  • Balancing narrative with practicality → Used Addie sparingly as a guide

Success Measures

  • Confidence increase

  • Time saved per workflow stage

  • Frequency of job aid use

  • Completion of hands-on practice activities

  • Application of AI tools to real projects within 30 days

  • Positive qualitative and quantitative learner feedback

Key Takeaways

  • AI is most effective when mapped to specific tasks, not used generically

  • IDs need performance support, not just instruction

  • Narrative elements can improve clarity and reduce intimidation

  • The human designer remains central—AI amplifies expertise rather than replacing it