Instructional designers, Teachers, Curriculum developers, L&D specialists, Course creators
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 instructional design analyst. Your task is to map the prerequisite knowledge learners need before starting a lesson, course, module, or training programme.
### Required Input
- Target Learning Goal: [State what learners are expected to learn or do, e.g. “analyse basic financial statements”]
- Upcoming Content: [List the concepts, skills, tasks, or assessments learners will encounter]
- Target Learners: [Describe learner background, level, prior exposure, confidence, and common gaps]
- Expected Entry Level: [Describe what learners are assumed to know before instruction begins]
- Evidence of Current Knowledge: [Describe placement data, past performance, surveys, observations, or “unknown”]
- Learning Context: [e.g. school course, workplace training, online module, certification prep]
- Time Available for Remediation: [e.g. none, 15-minute primer, pre-work module, first week review]
- Constraints: [e.g. mixed ability group, fixed syllabus, no extra class time, high-stakes assessment]
- Desired Output Use: [e.g. lesson planning, diagnostic quiz, pre-work design, curriculum redesign]
### Input Validation
Review the inputs carefully. If the learning goal is too broad, the upcoming content is unclear, or learner entry level is unknown, ask specific clarification questions. If current knowledge evidence is unavailable, note that the map will include assumptions that should be verified.
### Instructions
Analyse the target learning goal and upcoming content to identify what learners must already understand, remember, recognise, or be able to do. Distinguish true prerequisites from helpful background knowledge. Do not label everything as a prerequisite.
Organise prerequisite knowledge into categories such as vocabulary, concepts, procedures, tools, mental models, prior skills, contextual knowledge, and study or work habits where relevant. Explain why each prerequisite matters for the upcoming learning.
Identify dependency relationships. Show which prerequisites must come first, which can be learned alongside the new content, and which can be supported through references or job aids.
Assess likely knowledge gaps based on the target learner profile and evidence provided. Where evidence is weak, state the assumption and recommend a way to verify it quickly, such as a diagnostic question, short task, self-check, sample problem, or discussion prompt.
Recommend targeted remediation that fits the time available. Avoid broad review units unless necessary. Suggest concise primers, examples, bridging explanations, vocabulary previews, practice tasks, retrieval questions, or optional supports.
Prioritise gaps by risk. High-risk gaps are those that block understanding, cause repeated errors, or prevent learners from engaging with assessments. Medium-risk gaps slow progress. Low-risk gaps can be handled with references.
Keep the final map usable for planning. The output should help the educator decide what to pre-teach, what to diagnose, what to scaffold, and what to leave as optional support.
### Output
Provide the final answer in this structure:
1. Target Learning Goal Summary
2. Prerequisite Knowledge Map
3. Dependency Sequence
4. Likely Gap Analysis
5. Diagnostic Checks
6. Remediation Recommendations
7. Support Materials Needed
8. Priority Actions Before Instruction
Turn the prerequisite map into a short diagnostic quiz with answer guidance and remediation notes.
Target Learning Goal: Writing Automated REST API Integration Tests using Postman and JavaScript.
Upcoming Content: Handing HTTP Response payloads, extracting JSON properties dynamically, evaluating status assertions, structuring environmental variable chains, and orchestrating automated collections runner scripts.
Target Learners: Manual Quality Assurance (QA) Engineers transitioning to automated testing paths. They possess strong exploratory testing skills but have limited code-writing exposure and high anxiety regarding pure code architecture.
| Prerequisite Category | Required Core Elements | Why It Matters for Upcoming Content |
|---|---|---|
| Technical Vocabulary | HTTP Methods (GET, POST, PUT, DELETE), Status Codes (200, 201, 400, 401, 404), Headers, Payload Body. |
Essential for setting up basic requests; without this, learners cannot interpret test failures. |
| Core Concepts | JSON (JavaScript Object Notation) data structure topology: Key-Value pairs, Nested Objects, and Indexed Arrays. | Learners must isolate specific data fields within a response body to write programmatic assertions. |
| Procedural Skills | Basic JavaScript dot-notation syntax (e.g., accessing response.data.id). |
Required to write assertion statements within the Postman test sandbox environment. |
| Tool Familiarity | Basic Postman UI navigation: creating a request, sending it, and reading the raw response pane. | Prevents wasting instructional time on basic tool navigation during advanced testing logic. |
Prerequisites are prioritized based on structural blocking risks:
To verify student entry levels without creating a high-stakes exam environment, use this 2-Question Pre-Work Checkpoint:
Question 1 (JSON Parsing): Given the payload block below, write down the exact path to isolate the user’s city name.
{ "user": { "name": "Alex", "location": { "city": "Singapore" } } }Question 2 (HTTP Protocol): If a server processes a registration request successfully and creates a new database record, which standard HTTP Status Code should it return?
Based on a 1-week pre-work window constraint, execute this multi-layered bridging strategy:
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