Action Mapping meets AI: how Coursewright applies Cathy Moore's framework

Cathy Moore's Action Mapping is one of the most practical frameworks in instructional design. It's straightforward, evidence-based, and laser-focused on performance. So when we built Coursewright, we didn't just reference it — we embedded it into every AI prompt.

The four steps of Action Mapping

For those unfamiliar, Action Mapping follows four steps:

1. Identify the business goal — a measurable metric the organization already tracks
2. Identify what people need to DO — observable actions, not knowledge states
3. Design practice activities — realistic scenarios where learners make decisions
4. Identify minimum necessary information — only what directly supports practice

The brilliance of this approach is its ruthless prioritization. It forces you to cut everything that doesn't directly serve a performance outcome.

How most AI tools fail at this

Ask a typical AI tool to build training on "workplace safety" and you'll get:

• Module 1: Introduction to Workplace Safety
• Module 2: OSHA Regulations Overview
• Module 3: Hazard Identification
• Module 4: Personal Protective Equipment

That's a topic outline, not a course design. It starts with information, not actions. There's no business goal driving the structure, and there are no scenarios forcing learners to make decisions.

How Coursewright implements Action Mapping

Step 1: Business goal intake

During the Briefing conversation, Coursewright asks: "What measurable business outcome should this training improve?"

Not "What topic do you want to cover?" — but what metric will move. Reduce incident reports by 20%. Cut onboarding time from 6 weeks to 3. Decrease support escalations by 15%.

This goal becomes the North Star for every element the AI generates.

Step 2: Performance-first objectives

Every learning objective Coursewright generates describes something the learner will do, not something they'll know. The system validates that each MLO contains exactly one measurable Bloom's verb and rejects vague outcomes like "understand the importance of safety."

Step 3: Scenario-driven content

This is where most AI-generated content fails — and where Coursewright diverges most sharply. Instead of presenting information and then testing recall, Coursewright generates:

• Scenario openings that present realistic workplace situations
• Decision points where learners must choose an action
• Consequence-based feedback that explains WHY an answer matters

The AI is specifically instructed: "Activities come FIRST; information is embedded within them."

Step 4: Minimum necessary information

Coursewright's content generator is trained to ask: "Does the learner need this information to complete the practice activity?" If not, it becomes optional reference material — or gets cut entirely.

This is the hardest discipline for any content creator (human or AI) because our instinct is to be comprehensive. Coursewright's prompts include explicit anti-patterns to prevent information overload.

The anti-pattern list

Every generation prompt includes rules like:

• Don't begin with definitions or history
• Don't write comprehensive topic overviews
• Don't front-load information before practice
• Don't use academic third-person language
• Don't end with "In conclusion, it is important to consider..."

These aren't suggestions — they're hard constraints in the AI's instructions. Without them, even the best language model defaults to academic textbook patterns.

Results

The courses Coursewright generates don't read like AI content. They read like courses designed by an experienced instructional designer who's read Cathy Moore's book three times and has strong opinions about passive voice.

That's the point.

Want to see Action Mapping in action? [Create your first course free](/signup) and notice how the briefing conversation starts with business goals, not topics.