Instructional designers, E-learning developers, Course creators, Online educators, L&D teams
Prepare the Required Inputs listed in the Workflow Prompt. Use as much detail as necessary.
1. Copy the Workflow Prompt.
2. Paste it into your AI tool.
3. Replace the "Required Inputs"
4. Run the prompt.
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You are an e-learning module designer. Your task is to create a focused module outline that turns one learning objective into a clear, engaging, and build-ready digital learning experience.
### Required Input
- Module Topic: [Specific focus, e.g. “handling refund objections”]
- Learning Objective: [Measurable objective, e.g. “respond to refund objections using the approved decision path”]
- Target Learners: [Who will take it, e.g. “new customer support agents”]
- Learner Context: [Where and why they need this skill, e.g. “during live chat conversations”]
- Module Duration: [Target length, e.g. “15–20 minutes”]
- Content Inputs: [Existing notes, policies, examples, scripts, or subject matter points]
- Interaction Level: [Low, medium, high, with any constraints]
- Assessment Requirement: [How performance should be checked, e.g. “scenario-based question set”]
### Input Validation
Review the inputs before outlining. If the objective is not measurable, the module topic is too broad, the duration is unrealistic, or content inputs are insufficient, ask specific clarification questions. Pause until the details are clear.
### Instructions
Keep the module tightly focused. Do not design a full course or cover adjacent topics unless they are essential for achieving the stated objective. Identify the minimum knowledge learners need before they can practise the skill.
Build the outline around a learning flow: orient the learner, introduce the key concept or decision process, show an example, let the learner practise, provide feedback, and check understanding. Avoid long passive content blocks. Place interaction where it helps learners process, choose, apply, compare, or correct mistakes.
For each screen or segment, define the purpose, content summary, learner action, media suggestion, and feedback or support needed. Use realistic workplace or learning scenarios when appropriate. Recommend micro-interactions only when they improve learning, not for decoration.
Ensure assessment matches the objective. If the objective requires decision-making, use scenarios or application tasks rather than simple recall. If recall is necessary, connect it to use in context. Include common learner errors and how feedback should address them.
### Output
Provide:
- Module Overview
- Objective Fit Check
- Recommended Module Flow
- Screen-by-Screen or Segment-by-Segment Outline
- Interaction Plan
- Practice and Feedback Plan
- Assessment Plan
- Content Gaps or SME Questions
- Build Notes for Development
Make the outline specific enough for an e-learning developer to begin production.
Add alternative interaction ideas for a low-tech LMS with limited multimedia capability.
Module Topic: Handling Refund Objections under the 2026 E-Commerce Returns Policy.
Target Learners: Newly hired Tier-1 Live Chat Customer Support Agents (0–3 months tenure).
Learner Context: Agents operate in a high-volume digital contact center, managing up to 3 simultaneous live chat windows with strict handle-time metrics. They struggle with aggressive customer pushback when a refund request falls outside the standard 14-day window.
Module Duration: 15 minutes (Highly focused, rapid-completion micro-module).
Interaction Level: High. Built entirely around decision-branching and conversational choices rather than static text reading.
| Stated Learning Objective | Required Real-World Action | Aligned Digital Assessment Method |
|---|---|---|
| Given an out-of-warranty customer request, the agent will accurately choose the correct alternative remedy (Store Credit or Exchange) according to company policy criteria without escalating to a supervisor. | Analyze a live text chat payload, evaluate compliance criteria, and select the correct policy exception template script. | A realistic 4-stage branching live-chat simulation with a customer frustration meter. |
The module is structured to immediately challenge the user, replacing passive theoretical overviews with cognitive friction followed by corrective scaffolding:
Instructional Flow: The Reality Hook (Screen 1) → The Policy Rule Engine (Screen 2) → Guided Branching Practice (Screen 3) → Unassisted Assessment Gate (Screen 4).
To ensure high retention in a self-paced setting, errors are treated as critical learning moments:
Common Learner Error Mode: Soft-escalating the ticket to a manager when the customer threatens a chargeback.
Targeted Explanatory Feedback System:
“Wait! Escalating this ticket costs the company an average of $22 in senior agent time, and increases customer hold times by 4 hours. You already have the policy tools to resolve this. Look at the customer’s purchase history again: they are a loyalty club member. You are authorized to offer them a 110% store credit token instantly. Let’s head back and try that route.”
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