AI Literacy Education

A capability-building program that helps users make effective use of AI while understanding its limitations

Defining AI literacy levels

    flowchart LR
    A["Level 1<br/>AI Awareness"] --> B["Level 2<br/>AI Usage"]
    B --> C["Level 3<br/>AI Collaboration"]
    C --> D["Level 4<br/>AI Design"]

    style A fill:#EFF6FF,stroke:#2563EB,color:#1E40AF
    style B fill:#2563EB,stroke:#1D4ED8,color:#fff
    style C fill:#7C3AED,stroke:#6D28D9,color:#fff
    style D fill:#16A34A,stroke:#15803D,color:#fff

Level	Capability	Audience
Level 1	Knows what AI is	All employees
Level 2	Uses AI tools at a basic level	All employees
Level 3	Collaborates with AI to produce results	Key job functions
Level 4	Designs AI systems	Developers, AI leads

Core curriculum content

For all employees (Levels 1-2)

Understanding the nature of AI:

AI is a probabilistic, pattern-matching system (not 100% accurate)
Hallucinations can occur, so important information needs to be verified
AI is a tool that helps users — the final judgment call must be made by a human

Writing effective prompts:

The more specific and clear the instructions, the better the results
Include role, context, and the desired format
Providing examples leads to more accurate results

For developers and AI leads (Levels 3-4)

Designing and evaluating RAG pipelines
Advanced prompt engineering
Criteria for choosing AI models and optimizing cost
Principles of ethical AI development

Designing the training program

Onboarding track

Week 1: AI fundamentals + tool introduction (4 hours)
Week 2: Hands-on workshop + Q&A (4 hours)
Week 3: Applying AI to real work (self-directed)
Week 4: Results sharing + feedback (2 hours)

Ongoing learning system

Monthly AI newsletter: latest AI trends and use cases
Quarterly workshops: hands-on practice with new AI tools
Internal AI champions: department-level AI advocates, selected and run per team
Results-sharing sessions: presentations of successful AI use cases

Example AI usage guidelines

Recommended uses:

Drafting and editing assistance
Data analysis and summarization
Idea brainstorming
Code writing and debugging assistance

Use with caution:

Legal or financial decision-making (must be reviewed by an expert)
Work involving personal information (mask it before use)
Content delivered directly to end customers (must be reviewed)

Multimodal Interface Feedback Loop