Curriculum Design Guide
SkillMediaSystematic approaches to curriculum design using backward design and alignment
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.
Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
Then ask your AI: use the Curriculum Design Guide skill
What this skill tells your AI
The instructions your AI receives, as published by brycewang-stanford/auto-empirical-research-skills in skills/43-wentorai-research-plugins/skills/domains/education/curriculum-design-guide/SKILL.md and read by ahel’s review.
A structured skill for designing research-informed curricula using backward design, constructive alignment, and competency-based frameworks. Applicable to higher education course design, training program development, and educational research.
Backward Design Framework
Understanding by Design (Wiggins & McTighe, 2005) reverses the traditional content-first approach:
Stage 1: Identify Desired Results
Define what students should know, understand, and be able to do:
course: "Introduction to Research Methods"
big_ideas:
- "Research is a systematic process of inquiry"
- "Methodology must align with research questions"
essential_questions:
- "How do we know what we know?"
- "What makes evidence credible?"
- "When should we use qualitative vs. quantitative methods?"
learning_outcomes:
- "Formulate testable research questions (Apply)"
- "Select appropriate research designs for given questions (Evaluate)"
- "Critically appraise published research methodology (Analyze)"
- "Design and defend a research proposal (Create)"
Stage 2: Determine Acceptable Evidence
Design assessments before planning instruction:
# Assessment blueprint generator
def create_assessment_blueprint(outcomes: list[str], bloom_levels: list[str],
weights: list[float]) -> dict:
"""
Generate an assessment blueprint mapping outcomes to
assessment types and weights.
"""
assessment_types = {
'Remember': 'quiz',
'Understand': 'reflection_paper',
'Apply': 'problem_set',
'Analyze': 'case_study',
'Evaluate': 'peer_review',
'Create': 'research_proposal'
}
blueprint = []
for outcome, level, weight in zip(outcomes, bloom_levels, weights):
blueprint.append({
'outcome': outcome,
'bloom_level': level,
'assessment_type': assessment_types.get(level, 'portfolio'),
'weight_pct': weight * 100
})
return {'blueprint': blueprint, 'total_weight': sum(weights) * 100}
outcomes = [
"Formulate research questions",
"Select research designs",
"Appraise methodology",
"Design research proposal"
]
levels = ['Apply', 'Evaluate', 'Analyze', 'Create']
weights = [0.15, 0.20, 0.25, 0.40]
print(create_assessment_blueprint(outcomes, levels, weights))
Stage 3: Plan Learning Experiences
Sequence activities that build toward assessment readiness. Use the WHERETO framework:
- W -- Where are we going? Why?
- H -- Hook and hold interest
- E -- Equip with experience, tools, knowledge
- R -- Rethink, reflect, revise
- E -- Evaluate understanding
- T -- Tailor to individual needs
- O -- Organize for maximum engagement
Constructive Alignment
Biggs' Constructive Alignment (1996) ensures coherence between intended learning outcomes (ILOs), teaching/learning activities (TLAs), and assessment tasks (ATs):
ILO: "Students will analyze case studies using SWOT framework"
|
+--> TLA: Workshop where students collaboratively analyze
| a real company case in small groups
|
+--> AT: Individual case analysis report (1500 words)
assessed with rubric mapping to ILO verbs
Misalignment is the most common curriculum design failure. Audit each ILO to verify it has at least one matching TLA and one matching AT.
Competency-Based Curriculum Mapping
For programs with multiple courses, create a curriculum map:
Competency | Course 1 | Course 2 | Course 3 | Course 4
------------------------|----------|----------|----------|--------
Research question design| I | D | M | A
Literature review | I | D | D | M
Data collection | - | I | D | M
Statistical analysis | - | I | D | A
Academic writing | I | D | D | A
Legend: I = Introduced, D = Developed, M = Mastered, A = Applied
Ensure every competency reaches at least "Mastered" level by program completion, and identify gaps where competencies are introduced but never developed further.
Quality Assurance
Validate curriculum designs through:
- External review by discipline experts and industry advisors
- Student feedback surveys (mid-semester and end-of-semester)
- Learning analytics -- track completion rates, grade distributions, and DFW rates
- Periodic program-level assessment with rubric-scored capstone artifacts
Document all revisions in a curriculum changelog to maintain institutional memory and support accreditation reporting.
References
- Wiggins, G., & McTighe, J. (2005). Understanding by Design (2nd ed.). ASCD.
- Biggs, J. (1996). Enhancing teaching through constructive alignment. Higher Education, 32(3), 347-364.
Signals
- GitHub stars
- 4k
- Forks
- 531
- Last commit
- Sep 2026
Advanced
- Item type
- skill
- Key
curriculum-design-guide- Source
- github.com/brycewang-stanford/auto-empirical-research-skills
github.com/brycewang-stanford/auto-empirical-research-skills
Related picks
Skill · wshobson
The pick for Pythonpython-pro
Skill · jeffallan
The pick for Pythonacademic-paper-composer
Skill · brycewang-stanford
The pick for Academic03-academic-writing
Skill · 24kchengye
The pick for Academicprototype
Skill · mattpocock
More in Mediabrand-guidelines
Skill · anthropics
More in Media