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Learning Objectives Skill

Purpose

Enable educators to create measurable, actionable learning objectives aligned with Bloom's taxonomy and CEFR proficiency levels. This skill helps:

• Define what students will achieve (not just what topics they'll cover)
• Ensure objectives are specific and testable (not vague)
• Identify prerequisites and scaffold learning progressively
• Plan appropriate assessment methods
• Sequence learning from basic recall to creative synthesis
• Map to international proficiency standards (CEFR A1-C2) for portability
• Include AI co-learning outcomes (working WITH AI, not just independently)

Constitution v4.0.1 Alignment: This skill implements evals-first objective design—defining success criteria BEFORE creating learning objectives, integrating CEFR proficiency levels (Principle 5: Progressive Complexity), and incorporating Section IIb (AI Three Roles Framework) co-learning outcomes.

When to Activate

Use this skill when:

• Planning curriculum or lesson design and need to define learning outcomes
• Creating assessments and want to align them with clear objectives
• Designing a course and need measurable outcomes for accreditation
• Educators ask to "define objectives", "create learning goals", "set outcomes", or "what should students achieve?"
• Reviewing existing objectives and wondering if they're specific enough
• Designing a lesson and unsure what students should be able to do by the end

Evals-First Objective Design (Constitution v4.0.1)

CRITICAL WORKFLOW:

1. Evals First: Review success criteria from chapter spec BEFORE writing objectives
2. Objectives Second: Design learning objectives that lead to eval success
3. Alignment Third: Ensure each objective maps to at least one success eval
4. Validation Fourth: Verify objectives are measurable and aligned

Template:

### Objective Design (Evals-First)

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

**Success Evals from Spec**:
1. 75%+ write valid specification (business goal: reduce iteration cycles)
2. 80%+ identify vague requirements (business goal: prevent scope creep)

**Learning Objectives Designed to Achieve Evals**:
- LO-001: Write clear specifications → Eval #1
- LO-002: Identify ambiguous requirements → Eval #2

Do NOT create objectives without:

• ✅ Reference to approved spec with success evals
• ✅ Explicit mapping: Objective → Eval → Business Goal
• ✅ Measurability aligned with eval criteria

Process

Step 1: Understand the Context

When a request comes in to generate learning objectives, first understand:

• What topic or concept? (Python decorators, OOP, async/await, etc.)
• Who are the learners? (Beginners, intermediate, advanced)
• How long to teach? (30 minutes, 2 hours, full unit)
• What's the end goal? (Can they build something? Understand theory? Debug code?)
• What are the success evals? (From chapter spec—what defines success?)

Step 2: Review Bloom's Taxonomy (If Needed)

If you're not familiar with the specific topic's cognitive levels, read: 📖 [reference/blooms-taxonomy-programming.md](reference/blooms-taxonomy-programming.md)

This document maps Bloom's 6 levels to programming contexts with:

• Action verbs for each level (Remember, Understand, Apply, Analyze, Evaluate, Create)
• Programming examples
• Assessment methods for each level

Step 3: Identify Prerequisites

Read the guidance on prerequisite analysis: 📖 [reference/prerequisite-analysis.md](reference/prerequisite-analysis.md)

For your objectives, determine:

• What must learners know BEFORE tackling the main concept?
• List prerequisites at Remember/Understand level (not deep mastery)
• Trace dependency chains to foundational knowledge

Step 4: Choose Assessment Methods

Based on the Bloom's level of each objective, review appropriate assessment methods: 📖 [reference/assessment-methods.md](reference/assessment-methods.md)

This guides you to pair objectives with realistic assessment approaches (code exercises for Apply level, code reviews for Evaluate, etc.).

Step 5: Generate Objectives with CEFR Proficiency Levels

For each topic, create 3-5 objectives (typically):

• At least one from each level needed for the topic (Remember through Create)
• Progressively building in complexity
• Each with clear statement, context, prerequisites, assessment method, and success criteria
• Map to CEFR proficiency level (A1/A2/B1/B2/C1)

Use the template as guidance: 📄 [templates/learning-objective-template.yml](templates/learning-objective-template.yml)

Key principle: Each objective should answer:

• What will learners DO? (verb from Bloom's level)
• In what context? (the specific situation or problem)
• How will we know they succeeded? (measurable criteria)
• What proficiency level? (CEFR A1-C2)

CEFR Proficiency Mapping (Constitution v3.1.2)

Align objectives with international proficiency standards (from skills-proficiency-mapper v2.0):

A1 (Beginner - Recognition):

• Bloom's: Remember/Understand only
• Example: "Identify Python syntax for defining a function"
• Measurable: Recognition, not production

A2 (Elementary - Guided Application):

• Bloom's: Understand/Apply with scaffolding
• Example: "Complete a function definition with provided hints"
• Measurable: Application with support

B1 (Intermediate - Independent Application):

• Bloom's: Apply independently
• Example: "Implement a function from clear specification without assistance"
• Measurable: Real-world application without scaffolding

B2 (Upper-Intermediate - Analysis):

• Bloom's: Analyze/Evaluate
• Example: "Compare two implementations and justify which is more maintainable"
• Measurable: Evaluation with justification

C1 (Advanced - Creation/Synthesis):

• Bloom's: Evaluate/Create
• Example: "Design a system architecture for scalable deployment"
• Measurable: Original design with trade-off analysis

Proficiency Progression Rule: Lessons should progress A1→A2→B1 within a chapter (not jump from A1 to C1).

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

CRITICAL: AI-native learning objectives must include ability to work WITH AI in bidirectional co-learning partnership (per Section IIb forcing functions), not just independently.

Traditional Objective Format:

LO-001: Implement user authentication (independent skill)

AI-Native Objective Format:

LO-001: Implement user authentication working with AI as co-learning partner
  - Use AI as Teacher: Learn security patterns from AI suggestions
  - Use AI as Student: Refine AI's output through clear specifications
  - Use AI as Co-Worker: Iterate toward optimal solution collaboratively
  - Validate: Verify AI-generated code meets security requirements

Three-Role Objective Types:

1. AI as Teacher Objectives (Student learns from AI):

• "Identify pattern suggested by AI that improves code quality"
• "Explain trade-offs in AI's proposed approaches"
• "Apply AI-suggested pattern to new context"

2. AI as Student Objectives (Student teaches AI):

• "Write specification that produces correct code on first try"
• "Provide feedback that improves AI's next iteration"
• "Clarify requirements when AI asks for disambiguation"

3. AI as Co-Worker Objectives (Collaborative iteration):

• "Iterate with AI to converge on optimal solution"
• "Make strategic decisions while AI handles tactical implementation"
• "Validate AI outputs for correctness and appropriateness"

Example AI-Native Objective Set:

- id: "LO-AUTH-001"
  statement: "Implement OAuth authentication working with AI as co-learning partner"
  blooms_level: "Apply"
  cefr_level: "B1"
  three_role_integration: