AI Literacy Curriculum Designer

Overview

Design AI education programs tailored to different audiences in the organization. Create learning paths that build appropriate AI literacy from executives to practitioners.

Core principle: Different roles need different AI knowledge. Tailor education to what each audience needs to know and do.

When to Use

•Launching AI education program
•Onboarding new employees
•Preparing for AI tool rollout
•Building internal AI capability
•Addressing knowledge gaps

Output Format

yaml

ai_curriculum:
  program_name: "[Name]"
  version: "[Version]"
  last_updated: "[YYYY-MM-DD]"
  
  learning_paths:
    - path_id: "[PATH-001]"
      name: "[Path name]"
      target_audience: "[Who this is for]"
      objective: "[What learners will achieve]"
      prerequisites: ["[Prerequisites]"]
      duration: "[Total hours]"
      
      modules:
        - module_id: "[MOD-001]"
          title: "[Module title]"
          format: "[e-learning | Workshop | Hands-on | etc.]"
          duration: "[Hours]"
          
          learning_objectives:
            - "[What learners will be able to do]"
          
          topics:
            - "[Topic 1]"
            - "[Topic 2]"
          
          activities:
            - type: "[Lecture | Exercise | Discussion | Project]"
              description: "[What they do]"
          
          assessment:
            type: "[Quiz | Project | Demo | None]"
            passing_criteria: "[What constitutes pass]"
          
          resources:
            - type: "[Video | Reading | Tool access]"
              name: "[Resource name]"
              location: "[Where to find]"
      
      certification:
        available: [true | false]
        requirements: ["[Completion requirement]"]
        validity: "[How long valid]"
  
  audience_matrix:
    - audience: "[Role/Level]"
      recommended_path: "[PATH-ID]"
      mandatory: [true | false]
      deadline: "[If applicable]"
  
  delivery:
    platforms: ["[LMS | Workshop | etc.]"]
    schedule: "[When offered]"
    support: "[How to get help]"
  
  metrics:
    completion_target: "[%]"
    satisfaction_target: "[Score]"
    competency_assessment: "[How measured]"

Audience Segmentation

Executive/Leadership

yaml

executive_path:
  name: "AI for Leaders"
  duration: "2-4 hours"
  format: "Workshop + reading"
  
  objectives:
    - "Understand AI capabilities and limitations"
    - "Identify strategic AI opportunities"
    - "Make informed AI investment decisions"
    - "Lead AI governance"
  
  topics:
    - "AI fundamentals (no-code, concept level)"
    - "Business impact and ROI"
    - "Risks and governance"
    - "Leading AI transformation"
    - "Competitive landscape"
  
  not_covered:
    - "Technical implementation"
    - "Hands-on tools"
    - "Algorithm details"

Managers/Business Users

yaml

manager_path:
  name: "AI for Business"
  duration: "8-12 hours"
  format: "e-learning + workshop"
  
  objectives:
    - "Identify AI opportunities in your domain"
    - "Work effectively with AI teams"
    - "Evaluate AI project proposals"
    - "Manage AI-augmented teams"
  
  topics:
    - "AI capabilities by type"
    - "Use case identification"
    - "Data requirements"
    - "Working with AI teams"
    - "Change management for AI"
    - "AI ethics and policies"

End Users

yaml

end_user_path:
  name: "Working with AI"
  duration: "2-4 hours"
  format: "e-learning + guided practice"
  
  objectives:
    - "Use approved AI tools effectively"
    - "Understand AI limitations"
    - "Follow AI policies"
    - "Report issues appropriately"
  
  topics:
    - "How AI works (conceptual)"
    - "Prompt engineering basics"
    - "Reviewing AI outputs"
    - "Do's and don'ts"
    - "Company AI policy"

AI Practitioners

yaml

practitioner_path:
  name: "AI Development"
  duration: "40+ hours"
  format: "Hands-on + projects"
  
  objectives:
    - "Build production AI systems"
    - "Follow development best practices"
    - "Implement responsible AI"
    - "Monitor and maintain AI"
  
  topics:
    - "ML fundamentals"
    - "LLM application development"
    - "Prompt engineering advanced"
    - "Evaluation and testing"
    - "MLOps and deployment"
    - "Responsible AI implementation"

Module Design Template

Module Structure

yaml

module_template:
  overview:
    - "Learning objectives (3-5)"
    - "Prerequisites"
    - "Time commitment"
  
  content:
    - type: "Concept introduction"
      method: "Video or reading"
      duration: "10-15 min"
    
    - type: "Examples/demos"
      method: "Walkthrough"
      duration: "15-20 min"
    
    - type: "Hands-on practice"
      method: "Guided exercise"
      duration: "20-30 min"
    
    - type: "Knowledge check"
      method: "Quiz or discussion"
      duration: "10 min"
  
  wrap_up:
    - "Key takeaways"
    - "Resources for deeper learning"
    - "Next module preview"

Learning Objective Format

code

Action verb + specific content + context

Examples:
- "Identify three AI use cases in your workflow"
- "Write effective prompts for document summarization"
- "Explain AI limitations to stakeholders"
- "Evaluate AI vendor proposals against requirements"

Delivery Methods

Method	Best For	Audience
e-Learning	Foundation knowledge, scale	All
Workshop	Discussion, application	Managers, execs
Hands-on lab	Skill building	Practitioners, users
Coaching	Deep skill development	Practitioners
Lunch & learn	Awareness, culture	All
Office hours	Q&A, support	All

Assessment Approaches

Knowledge (Know)

yaml

knowledge_assessment:
  - method: "Quiz"
    when: "End of module"
    passing: "80%"
  
  - method: "Discussion responses"
    when: "During workshop"
    rubric: "Quality of reasoning"

Skills (Do)

yaml

skills_assessment:
  - method: "Hands-on project"
    when: "End of path"
    rubric: "Working solution that meets criteria"
  
  - method: "Prompt portfolio"
    when: "End of user path"
    rubric: "5 effective prompts with rationale"

Application (Apply)

yaml

application_assessment:
  - method: "Use case proposal"
    when: "Post-training"
    rubric: "Viable AI opportunity identified"
  
  - method: "Manager observation"
    when: "On the job"
    rubric: "Using AI tools appropriately"

Program Metrics

yaml

program_metrics:
  reach:
    - "% of target audience enrolled"
    - "% of target audience completed"
  
  quality:
    - "Learner satisfaction (NPS or rating)"
    - "Assessment pass rates"
    - "Time to completion"
  
  impact:
    - "AI tool adoption rate"
    - "Use cases identified post-training"
    - "Reduction in AI support requests"

Checklist

• Audiences segmented with needs
• Learning paths designed per audience
• Modules have clear objectives
• Content appropriately detailed
• Hands-on practice included
• Assessments defined
• Delivery method selected
• Resources identified/created
• Success metrics defined