aiskillstore/assessment-builder

Purpose

The assessment-builder skill helps educators create comprehensive, balanced assessments that measure conceptual understanding (not just memorization). This skill generates varied question types, designs meaningful distractors for MCQs, aligns questions with Bloom's taxonomy levels, and provides rubrics for open-ended questions.

Constitution v4.0.1 Alignment: This skill implements evals-first assessment design (foundational to all pillars)—defining success criteria BEFORE creating assessments, and integrating Section IIb (AI Three Roles Framework) co-learning evaluation.

Evals-First Assessment Design (Constitution v4.0.1)

CRITICAL WORKFLOW:

1. Evals First: Define success criteria from chapter spec BEFORE designing questions
2. Objectives Second: Map questions to learning objectives (from spec)
3. Questions Third: Design assessment items that test evals
4. Validation Fourth: Verify questions actually measure what evals define

Template for Assessment Planning:

markdown
### Assessment Planning (Evals-First)

**Source**: Chapter spec at `specs/part-X/chapter-Y/spec.md`

**Success Evals from Spec**:
1. 75%+ students write valid specification (measured by exercise)
2. 80%+ identify vague requirements (measured by quiz)
3. Students demonstrate co-learning (measured by reflection)

**Learning Objectives** (from spec):
- LO-001: Write clear specifications
- LO-002: Identify ambiguous requirements
- LO-003: Work effectively with AI partner

**Assessment Design**:
- Q1-3: Test LO-001 (spec writing) → Eval #1
- Q4-5: Test LO-002 (identify vagueness) → Eval #2
- Q6: Test LO-003 (co-learning) → Eval #3

Do NOT create assessments without:

• ✅ Reference to approved spec with evals
• ✅ Explicit mapping: Question → Objective → Eval
• ✅ Validation that questions measure defined success criteria

When to Activate

Use this skill when:

• Educators need to create quizzes, tests, or exams
• Designing assessments aligned with learning objectives
• Creating varied question types (MCQ, code-writing, debugging, projects)
• Need meaningful distractors based on common student errors
• Want balanced cognitive distribution (not just recall questions)
• Generating rubrics for open-ended programming questions
• Evaluating existing assessments for quality and balance
• Ensuring 60%+ questions test application or higher-order thinking

Inputs

Required:

• Learning objectives: What learners should be able to demonstrate
• Concept/topic: Python concepts to assess

Optional:

• Number of questions: How many questions to generate
• Question types: Preferred formats (MCQ, code-writing, debugging, etc.)
• Target audience: beginner | intermediate | advanced
• Time limit: Available assessment time
• Bloom's distribution: Desired cognitive level mix

Process

Step 1: Clarify Assessment Goals

Understand:

• What specific skills/knowledge to assess
• Depth of understanding required
• Format constraints (time, delivery method)
• Stakes (formative check vs. high-stakes exam)

Step 2: Load Question Type Reference

Read question type patterns:

bash
Read reference/question-types.md

Available types:

• MCQ: Concept recognition, decision-making
• Code-completion: Apply syntax, fill strategic gaps
• Code-tracing: Test mental execution model
• Debugging: Error recognition and fixing
• Code-writing: Implementation from specification
• Explanation: Test conceptual understanding
• Code-review: Evaluate code quality
• Prediction: Anticipate behavior
• Comparison: Distinguish approaches
• Project: Integrate multiple concepts

Step 3: Load Bloom's Assessment Alignment

Read cognitive level guidelines:

bash
Read reference/blooms-assessment-alignment.md

Map question types to Bloom's levels:

• Remember (10-15%): MCQ recall, terminology
• Understand (15-20%): Tracing, prediction, explanation
• Apply (30-40%): Code-completion, simple code-writing
• Analyze (20-25%): Debugging, comparison, code-review
• Evaluate (10-15%): Best-approach selection, trade-offs
• Create (5-10%): Projects, design problems

Target: 60%+ non-recall (Apply and higher)

CEFR Proficiency Integration (Constitution v3.1.2)

Map assessment difficulty to CEFR proficiency levels (aligned with skills-proficiency-mapper):

A1 (Beginner - Recognition):

• MCQs: Recognize syntax, identify basic concepts
• Code-tracing: Predict simple, linear code output
• No debugging or design questions yet

A2 (Elementary - Guided Application):

• MCQs: Choose correct approach with scaffolding
• Code-completion: Fill strategic blanks with hints
• Simple debugging: Identify syntax errors with guidance

B1 (Intermediate - Independent Application):

• Code-writing: Implement from clear specification
• Debugging: Find and fix logic errors independently
• Code-review: Evaluate simple code quality

B2 (Upper-Intermediate - Analysis):

• Design questions: Choose appropriate data structures/algorithms
• Optimization: Identify performance improvements
• Trade-off evaluation: Compare multiple valid approaches

C1 (Advanced - Synthesis):

• Architecture design: Plan system structure
• Complex debugging: Diagnose multi-layered issues
• Best practices justification: Defend design decisions

Assessment Design Rule: Questions must match lesson's target CEFR level (from spec).

Three-Role AI Partnership Assessment (Section IIb, Constitution v4.0.1)

CRITICAL: AI-native development requires assessing students' ability to work WITH AI in bidirectional co-learning partnership (per Section IIb forcing functions), not just independently.

AI's Three Roles - Assessment Types:

1. AI as Teacher (Does student learn from AI?)

• Reflection question: "What did AI suggest that you hadn't considered?"
• Evaluation: "Which of AI's three suggestions was most valuable? Why?"
• Transfer: "How would you apply AI's pattern to a new problem?"

2. AI as Student (Does student effectively teach AI?)

• Specification quality: "Write a spec that produces correct code on first try"
• Feedback quality: "Improve AI's output by providing clear corrections"
• Iteration: "Refine your prompt based on AI's initial response"

3. AI as Co-Worker (Does student collaborate effectively?)

• Convergence: "Show iteration where you and AI refined solution together"
• Decision-making: "Which strategic decisions did you make vs. AI's tactical suggestions?"
• Validation: "How did you verify AI's output was correct?"

Example Assessment Items:

markdown
### Q: Co-Learning Reflection (10 points)

In the previous exercise, you worked with AI to implement authentication.

1. What pattern or approach did AI suggest that you hadn't considered? (5 pts)
2. How did you validate that AI's suggestion was appropriate? (5 pts)

**Rubric**:
- Excellent (10): Specific AI suggestion identified, clear validation method
- Good (7): General AI contribution mentioned, basic validation
- Fair (4): Vague answer, no validation mentioned
- Poor (0): No evidence of learning from AI

Assessment Balance for AI-Native Content:

• 60-70%: Traditional skills (code-writing, debugging)
• 20-30%: Co-learning skills (working WITH AI)
• 10-20%: Validation/verification skills (checking AI outputs)

Step 4: Design Varied Question Set

Create 5-10 questions with variety:

Example Distribution:

Q1: MCQ (Understand) - Concept check
Q2: Code-tracing (Understand) - Predict output
Q3: Code-completion (Apply) - Fill strategic blank
Q4: Code-writing (Apply) - Implement function
Q5: Debugging (Analyze) - Find and fix errors
Q6: Code-review (Evaluate) - Assess quality
Q7: Project (Create) - Integrate concepts

Avoid: 10 identical MCQs (tests memorization, not understanding)

Step 5: Design MCQ Distractors

For multiple-choice questions, load distractor design guide:

bash
Read reference/distractor-design.md

Create meaningful distractors based on common misconceptions:

Process:

1. Identify correct answer
2. List common errors students make
3. Create distractor for each specific misconception
4. Ensure distractors are plausible (not obviously wrong)

Example:

Q: After this code, what does x contain?
x = [1, 2, 3]
y = x
y.append(4)

A) [1, 2, 3, 4]  ← Correct (understands references)
B) [1, 2, 3]     ← Thinks x is independent of y
C) [4]           ← Misunderstands append
D) Error         ← Thinks modification through y is invalid

Distractor Analysis:
B tests: Understanding of assignment vs. copy
C tests: Understanding of append operation
D tests: Understanding of reference semantics

Step 6: Create Rubrics for Open-Ended Questions

For code-writing, projects, and explanations, load rubric guidelines:

bash
Read reference/rubric-guidelines.md

Create analytic rubric with criteria:

• Correctness (40%): Produces correct output
• Code Quality (25%): Readable, well-structured
• Efficiency (20%): Appropriate algorithm
• Error Handling (10%): Edge cases
• Style (5%): Python best practices

Each criterion has 4 levels: excellent, good, fair, poor

Load templates:

bash
Read templates/rubric-template.yml

Step 7: Validate Cognitive Distribution

Check assessment balance using validation script:

bash
python .claude/skills/assessment-builder/scripts/validate-assessment.py assessment.yml

The script checks:

• Cognitive distribution: 60%+ non-recall?
• Question variety: Multiple question types?
• MCQ distractors: Analysis provided?
• Rubrics: Present for open-ended questions?
• Objective alignment: Questions map to objectives?

Review output:

• Validation score: >80 is good, <60 needs revision
• Issues: Critical problems to fix
• Warnings: Quality improvements
• Recommendations: Specific suggestions

Step 8: Refine Based on Validation

If validation identifies issues:

Issue: "Only 40% non-recall questions" Fix: Replace 2-3 MCQ recall questions with code-writing or debugging

Issue: "MCQs lack distractor analysis" Fix: Document what misconception each distractor tests

Issue: "Missing rubrics for code-writing" Fix: Add rubric with correctness, quality, efficiency criteria

Step 9: Add Answer Key and Explanations

For each question:

• Correct answer: What is right
• Explanation: Why it's correct
• Common errors: Why distractors are wrong
• Grading guidance: How to score

Example:

Q3: Write function to sum even numbers

Answer:
def sum_evens(numbers):
    return sum(num for num in numbers if num % 2 == 0)

Explanation:
- Iterates through numbers
- Filters even (num % 2 == 0)
- Sums filtered values
- Handles empty list (sum returns 0)

Common Errors:
- Forgetting to check if num % 2 == 0
- Using == instead of %= for modulo
- Not handling empty list

Step 10: Package Complete Assessment

Structure assessment following template:

bash
Read templates/assessment-template.yml

Include:

• Metadata: Title, audience, time limit, points
• Learning objectives: What's being assessed
• Questions: All with type, points, Bloom's level
• Answer key: Solutions and explanations
• Rubrics: For open-ended questions
• Cognitive analysis: Distribution breakdown

Output Format

Provide assessment as structured markdown:

markdown
# Assessment: [Title]

**Target Audience**: [beginner/intermediate/advanced]
**Time Limit**: [X minutes]
**Total Points**: [X]
**Passing Score**: [X]

## Learning Objectives Assessed

- [Objective 1]
- [Objective 2]
- [...]

## Cognitive Distribution

- Remember/Understand: X%
- Apply: X%
- Analyze/Evaluate: X%
- Create: X%

---

## Question 1 (X points) [Type: MCQ] [Bloom: Understand]

[Question text]

A) [Option]
B) [Option]
C) [Option]
D) [Option]

---

## Question 2 (X points) [Type: Code-Writing] [Bloom: Apply]

Write a function that [specification].

**Requirements**:
- [Requirement 1]
- [Requirement 2]

**Test Cases**:
1. Input: [x], Expected: [y]
2. Input: [x], Expected: [y]

---

[Continue for all questions...]

---

## Answer Key

### Question 1
**Answer**: B

**Explanation**: [Why B is correct and why A, C, D are wrong]

**Distractor Analysis**:
- A tests: [misconception]
- C tests: [misconception]
- D tests: [misconception]

### Question 2
**Solution**:

Complete correct solution]

## Acceptance Checks

- [ ] SpecRef present and each question maps to at least one objective ID
- [ ] Objective Coverage Matrix included (question → objective IDs, Bloom level)
- [ ] ≥ 60% non‑recall; variety of question types present
- [ ] Validation Pack attached: answer key, explanations, distractor analyses, and rubric(s)

### Objective Coverage Matrix (example)

Q1 → LO-001] (Understand) Q2 → LO-002] (Apply) Q3 → LO-002, LO-003] (Analyze) ...

**Rubric**:

**Correctness (40 pts)**:
- Excellent (40): Works for all test cases
- Good (30): Works for standard cases
- Fair (20): Partial functionality
- Poor (10): Significant errors

**Code Quality (25 pts)**:
- Excellent (25): Clear names, good structure
- Good (19): Mostly readable
- Fair (13): Some confusion
- Poor (6): Poor quality

[Continue for all criteria...]

---

[Continue for all questions...]

Examples

Example 1: Create Balanced Assessment for Functions

Input: "Create 6-question assessment for Python functions (beginner level)"

Process:

1. Identify objectives: Define functions, use parameters, return values
2. Design variety:

• Q1: MCQ on function syntax (Understand)
• Q2: Trace execution (Understand)
• Q3: Complete function (Apply)
• Q4: Write function from spec (Apply)
• Q5: Debug function (Analyze)
• Q6: Mini-project combining functions (Create)

1. Create MCQ distractors based on common errors
2. Add rubric for Q4, Q6
3. Validate (check 60%+ non-recall)

Output: Complete 6-question assessment with answer key and rubrics

Example 2: Add Distractors to Existing MCQ

Input: "Improve distractors for this MCQ" provides question]

Process:

1. Load distractor design reference
2. Identify concept being tested
3. List common student misconceptions
4. Create diagnostic distractor for each misconception
5. Document what each distractor tests

Output: Improved MCQ with meaningful, plausible distractors and analysis

Example 3: Validate Assessment Balance

Input: "Is this assessment balanced?" provides 10-question exam]

Process:

1. Analyze cognitive distribution
2. Check for question variety
3. Validate MCQ distractors
4. Check for rubrics on open-ended
5. Calculate non-recall percentage
6. Provide specific recommendations

Output: Validation report with issues and recommendations

Common Patterns

Pattern 1: Formative Assessment (Quick Check)

3-5 questions
30% Understand (quick concept check)
50% Apply (demonstrate skill)
20% Analyze (identify errors)
15-20 minutes

Pattern 2: Summative Assessment (Unit Exam)

10-15 questions
15% Understand
40% Apply
30% Analyze/Evaluate
15% Create (project)
60-90 minutes

Pattern 3: Skills Demonstration

5-7 questions, mostly code-writing and projects
20% Apply (basic implementations)
40% Analyze (debugging, optimization)
40% Create (design and build)

Validation Checklist

Before finalizing assessment:

• ] 60%+ questions test application or higher-order thinking
• ] Multiple question types used (not all MCQ)
• ] MCQs have meaningful, diagnostic distractors
• ] Open-ended questions have rubrics
• ] Questions align with learning objectives
• ] Clear instructions for each question
• ] Answer key and explanations provided
• ] Reasonable time allocation
• ] Validation score >80

References

Supporting documentation (loaded as needed):

• reference/question-types.md - MCQ, code-writing, debugging, projects, etc.
• reference/distractor-design.md - Misconception-based distractor creation
• reference/blooms-assessment-alignment.md - Cognitive levels and question types
• reference/rubric-guidelines.md - Analytic rubric creation

Error Handling

If validation fails:

1. Report specific issues (e.g., "Only 30% non-recall questions", "Missing rubrics")
2. Suggest remediation (e.g., "Replace 3 MCQs with code-writing questions")
3. Halt and require user intervention (hard failure mode)

Assessments must meet quality standards: balanced cognitive distribution, varied types, meaningful distractors, clear rubrics.