Topic 6.1

Project: 5-Minute Microlearning Module

Complete module in 2 hours (vs. 20-30 hours traditional)

⏱️ 12 minutes 📋 Prompt Templates ✓ Quality Checklist

The Challenge

You need a 5-minute learning module on a new procedure.
Budget: $0.
Timeline: This week.

Traditional approach: 2-3 weeks.
With AI: 2 hours.

This is where everything you've learned comes together.

What You're Building

Project: Complete microlearning module on "Handling Customer Data Requests"

✓ Components

  • 1 learning objective
  • 1 realistic scenario
  • 3 knowledge checks
  • 1 job aid (PDF checklist)
  • Complete instructional content

⏱️ Time Comparison

  • Traditional: 20-30 hours
  • AI-assisted: 2 hours
  • Savings: 90%

The 2-Hour Workflow

🔄 Six phases (120 minutes total) ▼
Phase What you build Time
1. Define Learning objective 10 min
2. Build Scenario + decision framework 30 min
3. Assess 3 knowledge check questions 20 min
4. Support Job aid checklist 30 min
5. Explain Why-it-matters content 20 min
6. Polish Assembly and review 10 min

Phase 1: Define (10 min)

📋 Learning objective prompt
I need to create a 5-minute microlearning module on handling customer data requests under GDPR. Context: - Audience: Customer service representatives - Current problem: Reps don't know what's urgent vs. routine - Setting: They receive requests via email - Desired outcome: Reps correctly classify and route requests within 24 hours Create a single, measurable learning objective. Use a Bloom's taxonomy action verb. Focus on performance, not just knowledge. One sentence only.

AI Output:
"By the end of this module, customer service representatives will classify incoming customer data requests as urgent or routine and route them to the correct department within 24 hours using the GDPR Data Request Decision Tree."

Your decision: Does this match what learners actually need to do? Is the performance outcome realistic and measurable?

Phase 2: Build Core Content (30 min)

📋 Scenario prompt
Create a realistic scenario for customer service training on GDPR data requests. Context: - Customer service rep receives an email - Customer is requesting their personal data - The rep must classify it as urgent or routine Requirements: - Include specific details (customer name, email excerpt, timestamp) - Make the situation require judgment (not obviously urgent or routine) - Keep to 3-4 sentences - End with a decision point Create one scenario that's moderately ambiguous.

AI Output:
"Sarah Chen opens an email Monday morning from customer James Rodriguez. He writes: 'I closed my account last month but I'm concerned about what data you still have. Can you send me everything you have on file? I need this for my records before I file my taxes next week.' How should Sarah classify and route this request?"

Your decision: Does this feel authentic? Would your reps actually encounter this?

📋 Decision framework prompt
Create a simple decision tree for classifying GDPR data requests as urgent vs. routine. Categories: - URGENT: Legal deadline, active investigation, data breach concern - ROUTINE: General inquiry, closed account data request, preference update Format as a 3-4 question decision tree. Keep language simple. Each question should have clear yes/no paths.

Your decision: Does this match your organization's actual policies? Adjust categories and timelines to fit reality.

Phase 3: Create Assessments (20 min)

📋 Assessment questions prompt
Create 3 multiple choice questions to assess whether customer service reps can correctly classify GDPR data requests. Use the decision tree I'll provide below. Each question should: - Present a realistic email scenario - Have one clearly correct answer - Include plausible wrong answers based on common mistakes - Test application, not just recall [Paste your decision tree here] Format: Question: [scenario] A) [option] B) [option] C) [option] Correct answer: [letter] Rationale: [why]

Your decision: Do these questions test what matters? Do wrong answers reflect actual mistakes your reps make?

Phase 4: Build Job Aid (30 min)

📋 Job aid prompt
Create a one-page job aid for customer service reps handling GDPR data requests. Format as a checklist they can print and keep at their desk. Include: - Quick classification criteria (urgent vs. routine) - Response timelines for each category - Who to escalate to - What NOT to do (common mistakes) Keep it to bullet points. Maximum 1 page when printed. Use clear headers.
📄 Example job aid output ▼

GDPR Data Request Quick Reference

CLASSIFY THE REQUEST

□ Legal deadline mentioned? → URGENT
□ Investigation/court order? → URGENT
□ Data breach concern? → URGENT
□ General data request? → ROUTINE

RESPONSE TIMELINES

- Urgent: Escalate within 2 hours
- Routine: Process within 30 days

ESCALATION CONTACTS

- Legal issues → Legal team
- Security concerns → Security team
- Routine requests → Data Privacy team

COMMON MISTAKES TO AVOID

✗ Don't promise specific timelines without checking category
✗ Don't ask customer to "follow up if urgent"—you classify it
✗ Don't forward to wrong team

Your decision: Does this match your team's actual workflow? Add your organization's specific contacts and policies.

Phase 5: Write Instructional Content (20 min)

📋 Instructional content prompt
Write 2-3 short paragraphs explaining why GDPR data request classification matters. Audience: Customer service reps who handle these daily but may not understand the stakes. Cover: - Why accurate classification matters (legal/business consequences) - What happens if requests are misclassified - How this protects both customers and the company Keep it conversational and practical. No legal jargon. 150-200 words total.

Your decision: Does this tone match your company culture? Does it motivate without creating anxiety?

Phase 6: Polish and Assemble (10 min)

Assemble in sequence:

  1. Learning objective (what they'll do)
  2. Why this matters (context)
  3. Decision tree (how to classify)
  4. Practice scenarios (knowledge checks)
  5. Job aid (reference tool)

Final checks:

  • Does the flow make sense?
  • Are examples realistic enough?
  • Is the job aid actually useful?
  • Could a new rep use this independently?

Time check: 120 minutes (2 hours)

What You've Built

✅ Complete deliverables checklist ▼
Component Status
Learning objective ✓ Clear and measurable
Scenario + decision framework ✓ Realistic and applicable
3 assessment questions ✓ Tests application, not recall
Job aid ✓ Printable and useful
Instructional content ✓ Motivating context

Traditional timeline: 20-30 hours
AI-assisted timeline: 2 hours
Savings: 90%

Where Your Expertise Mattered

🎯 AI handled vs. You decided ▼
AI handled You decided
First drafts of scenarios Whether objectives matched real performance needs
Question formatting If scenarios felt authentic to your organization
Job aid structure Which questions actually tested understanding
Explanation prose Whether decision tree matched your policies
If job aid would be useful on the job

💡 The pattern

AI gave you 80% of the content in 20% of the time. You used the saved time to make it excellent instead of adequate.

Key Takeaways

  1. Complete module in 2 hours. All components—objective, scenario, assessments, job aid, content—done in 120 minutes.
  2. 90% time savings. Traditional 20-30 hours reduced to 2 hours with AI assistance.
  3. Your expertise makes it work. AI drafts, you decide what's realistic, authentic, and useful.
  4. Quality checks matter. Verify accuracy, test realism, ensure job aid matches actual workflow.

Try It Now

🎯 Your task:

Pick a procedure or policy at your organization. Build a 5-minute microlearning module using the 6-phase workflow. Time yourself. Can you complete it in 2 hours?

The test: Would a new employee be able to perform the procedure after completing your module?

📥 Download: Microlearning module template pack (PDF)

All 6 prompts ready to customize for your topic, plus assembly checklist.

Download PDF