Education & Learning / Quiz Generation
Tag and categorize questions by topic, difficulty, and cognitive level — content taxonomy for efficient quiz assembly.
Why This Prompt Exists
Disorganized question banks are unusable. Without tagging, teachers can’t find questions by topic, difficulty, or type. Most question collections are flat lists — no search, no filtering.
You get:
- questions buried in long lists (can’t find what you need)
- no way to filter by difficulty or topic
- duplicate questions across teachers (no sharing system)
- inconsistent quality and standards
- time wasted searching instead of teaching
But organized question banks have structure:
- topic tags: subject, unit, chapter, concept
- difficulty level: easy, medium, hard
- cognitive level: remember, understand, apply, analyze, evaluate, create
- question type: multiple choice, short answer, essay, matching
- usage tracking: used on which assessments, performance data
Without organization, question banks are unusable.
This prompt organizes questions into searchable, tagged databases.
The Prompt
Assume the role of a curriculum database designer who organizes question banks.
Your task is to tag and categorize questions for easy retrieval.
Generate:
1. QUESTION BANK STRUCTURE
**Topic Hierarchy**
- Domain: [broad subject area]
- Unit: [major topic]
- Chapter: [specific topic]
- Concept: [individual learning objective]
2. QUESTION METADATA TEMPLATE
{
"id": "QZ-001",
"topic_hierarchy": {
"domain": "",
"unit": "",
"chapter": "",
"concept": ""
},
"difficulty": "easy|medium|hard",
"cognitive_level": "remember|understand|apply|analyze|evaluate|create",
"question_type": "multiple_choice|short_answer|essay|matching|fill_in",
"estimated_time": "30s|60s|90s|2min|5min",
"tags": [],
"usage_count": 0,
"performance_data": {
"p_value": null,
"discrimination": null
}
}
3. TOPIC TAG INVENTORY
| Topic Level | Tag Name | Parent Tag | Sub-tags |
|-------------|----------|------------|----------|
| Domain | [name] | None | [units] |
| Unit | [name] | [domain] | [chapters] |
| Chapter | [name] | [unit] | [concepts] |
| Concept | [name] | [chapter] | None |
4. DIFFICULTY CALIBRATION
| Difficulty | Success Rate Target | Time Estimate | Question Features |
|------------|---------------------|---------------|-------------------|
| Easy | 80-90% | 30-60s | Direct recall, one step |
| Medium | 60-80% | 60-90s | Two steps, simple application |
| Hard | 40-60% | 90-120s | Multiple steps, analysis required |
| Challenge | 20-40% | 2-5min | Synthesis, evaluation, creation |
5. QUESTION BANK INVENTORY
| QID | Question | Topic | Difficulty | Cognitive Level | Type | Usage |
|-----|----------|-------|------------|----------------|------|-------|
| [ID] | [first 50 chars] | [tag] | [E/M/H] | [level] | [type] | [count] |
6. RETRIEVAL QUERY PATTERNS
| Query Type | Example | Use Case |
|------------|---------|----------|
| Topic filter | "topic:photosynthesis" | Find all photosynthesis questions |
| Difficulty filter | "difficulty:hard" | Challenge students |
| Cognitive level | "level:analyze" | Build higher-order thinking |
| Combined | "topic:algebra AND difficulty:hard AND level:apply" | Targeted assessment |
7. QUESTION BANK QUALITY METRICS
| Metric | Description | Target |
|--------|-------------|--------|
| P-value | Proportion of students answering correctly | 0.4-0.8 for medium questions |
| Discrimination | Difference between high and low performers | >0.3 |
| Distractor effectiveness | Each distractor chosen by some students | >5% per distractor |
| Usage frequency | How often question is used | Distributed evenly |
8. COMMON ORGANIZATION MISTAKES
| Mistake | Why It Fails | Correct Approach |
|---------|--------------|------------------|
| Flat list structure | Can't find questions | Hierarchical tagging |
| No difficulty calibration | Unbalanced assessments | Calibrate with student data |
| Inconsistent tagging | Can't filter reliably | Standardized tag list |
| No usage tracking | Unknown question quality | Log performance data |
| Duplicate questions | Wasted effort | Central question bank |
INPUTS:
Subject area:
[PASTE SUBJECT AREA]
Existing questions (if any):
[PASTE QUESTIONS OR "NEW BANK"]
Number of questions to organize:
[PASTE NUMBER]
Tagging preferences (optional):
[PASTE PREFERENCES]
RULES:
- Use hierarchical topic tags (domain → unit → chapter → concept)
- Tag every question with difficulty (easy/medium/hard) based on success rate targets
- Tag every question with cognitive level (Bloom's taxonomy)
- Track usage and performance data for quality improvement
- Avoid duplicate questions (check before adding)
- Calibrate difficulty with real student data when available
- Review question bank annually (retire poor questions, add new ones)
How To Use It
- Use hierarchical topic tags — domain → unit → chapter → concept for granular searching.
- Tag every question with difficulty (easy/medium/hard) — based on success rate targets, not intuition.
- Tag every question with cognitive level — Bloom’s taxonomy for balanced assessment.
- Track usage and performance data — P-values and discrimination indices reveal question quality.
- Avoid duplicate questions — check the question bank before adding new questions.
- Calibrate difficulty with real student data when available — adjust tags based on actual performance.
- Review the question bank annually — retire poor questions, add new ones, update tags.
Example Input
Subject area:
“High School Biology”
Existing questions:
“10 questions about photosynthesis, 5 about cellular respiration, 3 about DNA structure”
Number of questions to organize:
“18”
Tagging preferences:
“Add domain, unit, chapter, concept tags; include difficulty and cognitive level”
Why It Works
Flat question lists are unusable at scale. Without tagging, teachers waste time searching instead of teaching.
This framework improves outcomes by forcing: hierarchical topic tagging, difficulty calibration, cognitive level classification, usage tracking, and quality metrics.
Failure modes this prevents: Buried questions, no filtering, duplicates, inconsistent quality, time wasted searching.
This improves on: Flat question lists. Organized question banks enable efficient quiz assembly.
Related to: QZ-01 (Bloom’s) for cognitive tags; QZ-02 (Distractor) for multiple-choice quality.
This framework improves outcomes by forcing: hierarchical topic tagging, difficulty calibration, cognitive level classification, usage tracking, and quality metrics.
Failure modes this prevents: Buried questions, no filtering, duplicates, inconsistent quality, time wasted searching.
This improves on: Flat question lists. Organized question banks enable efficient quiz assembly.
Related to: QZ-01 (Bloom’s) for cognitive tags; QZ-02 (Distractor) for multiple-choice quality.
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