Topic 2.1

You've Written Hundreds. It's Still Tedious.

AI doesn't make writing learning objectives fun. It makes it faster.

⏱️ 15 minutes 📋 Prompt Templates ✓ Quality Checklist

The Problem

You know the drill. Stare at the blank document. Write an objective. Revise it. Check Bloom's taxonomy. Make sure it's measurable. Distinct from the others. Actually achievable.

Repeat 10 times.

It's not hard. It's just tedious.

AI won't solve tedium by making it interesting. It solves it by doing the grunt work—generating 10-15 options in seconds. Your job: select the best, refine, ship.

The Basic Prompt Template

📋 Copy this template
Generate 10 learning objectives for a [duration] course on [topic] for [audience]. Requirements: - Use Bloom's taxonomy at the [cognitive level] level - Make objectives observable and measurable - Keep scope appropriate for [duration] - Focus on job-relevant skills Example format: "By the end of this course, learners will be able to [verb] [object] [context/condition]."

Fill in the brackets:

  • [duration]: 30-minute module, 2-hour workshop, 5-day course
  • [topic]: Customer service de-escalation, SQL basics, project management
  • [audience]: New customer service reps, junior developers, first-time managers
  • [cognitive level]: Remember, Understand, Apply, Analyze, Evaluate, Create

Context Makes Better Objectives

Generic prompts get generic results. Add context—you get relevance.

🎯 Skill level context ▼

Without: "...for sales professionals"

With: "...for junior sales reps in their first 90 days, working in B2B SaaS, handling inbound leads"

The more specific, the better the fit.

📐 Format and constraints ▼

Tell AI about delivery format and limitations:

  • "This is a self-paced eLearning module—no hands-on practice available"
  • "ILT workshop with role-play activities"
  • "Microlearning—learners have 5 minutes max"
🏢 Work environment ▼

Add details about where/how learners will apply skills:

Example: "Learners work in retail stores, face-to-face with customers, handling complaints in real-time without supervisor support."

AI can now generate objectives focused on independent decision-making, not escalation protocols.

Quality Check

Run every AI-generated objective through these five questions:

✓ Five-point quality checklist ▼
Check Question
Observable Can you see or measure this behavior?
Right verb Does it match the cognitive level you need?
Right scope Achievable in your timeframe?
Distinct Does it overlap with other objectives?
Relevant Will learners actually use this on the job?

🚩 Red Flag

"Understand the principles of..."

You can't observe "understand." Change to "Explain..." or "Apply..."

Before and After

❌ AI's first draft

  • "Understand effective communication techniques"
  • "Know how to handle difficult customers"
  • "Be familiar with company policies"

Vague verbs, not measurable

✅ After your refinement

  • "Demonstrate three de-escalation techniques in customer conflict scenarios"
  • "Respond to customer complaints using the LEARN model"
  • "Apply company refund policies to real-world customer situations"

Specific actions, concrete applications

The pattern: vague verbs → specific actions. Abstract concepts → concrete applications.

Four Traps to Avoid

1. Accepting the first draft ▼

AI's first attempt is rarely right. Generate options, then refine.

Better approach: Ask for 10-15 objectives, select the best 3-5, then refine those specifically.

2. Skipping the cognitive level ▼

Without guidance, AI defaults to Remember and Understand. Be explicit.

Add to prompt: "Focus on the Apply level—learners need to use these skills immediately on the job."

3. Vague audience descriptions ▼

Generic: "Healthcare professionals" → generic results

Specific: "First-year nursing students in clinical rotations" → relevant results

The more detail about your learners, the better AI can target the objectives.

4. Forgetting assessment alignment ▼

If the objective says "Evaluate," your assessment better require evaluation—not recall.

Pro tip: Generate objectives and assessments together. Ask AI to create aligned assessment items for each objective immediately.

The Refinement Loop

Don't start over. Revise in place.

💬 Refinement prompts
Too broad? "Objective #3 is too broad. Rewrite to focus specifically on [narrower scope]." Overlap? "Objectives #2 and #5 overlap. Combine into one objective covering both." Wrong level? "Rewrite objective #7 at a higher cognitive level—move from Apply to Evaluate."

This back-and-forth—AI drafts, you direct revisions—beats trying to nail it in one prompt.

Key Takeaways

  1. AI drafts, you decide. Generate 10-15 options. Select the best. Refine.
  2. Context improves output. Skill level, format, duration, environment—add it to the prompt.
  3. Five-point quality check. Observable, right verb, right scope, distinct, relevant.
  4. Iterate, don't restart. Use follow-up prompts to fix specific issues.

Try It Now

🎯 Your task:

Pick a topic from your current project. Use the basic prompt template to generate 10 objectives. Run each through the quality checklist. Refine the top 5.

Time yourself. Compare to your usual process.

📥 Download: Prompt templates and quality checklist (PDF)

Ready-to-use templates with Bloom's verb lists and quality checklists.

Download PDF