TMY-AILMW: AI Literacy for the Modern Workplace

Module 1: Introduction to AI Literacy & Why It Matters

• Why AI literacy is now a workplace necessity
• AI as a productivity co-pilot, not a replacement
• Real-world examples of AI in organisations
• AI myths vs reality

Outcome:
Participants understand why AI literacy matters to everyone.
Module 2: AI Fundamentals: From AI to AGI & ASI

• Artificial Intelligence (AI): definition and scope
• Machine Learning (ML): learning from data
• Deep Learning (DL): neural networks and representation learning
• Generative AI and Large Language Models (LLMs)
• Conceptual understanding of:
  • Artificial General Intelligence (AGI)
  • Artificial Superintelligence (ASI)

Focus: Conceptual understanding (no coding)
Module 3: How Generative AI Works: Strengths & Limitations

• How LLMs generate text, images, and insights
• What GenAI does well (speed, summarisation, drafting)
• What GenAI does poorly (facts, reasoning, context gaps)
• Hallucinations and overconfidence
• Why AI outputs must always be reviewed

Hands-On:
Compare human vs AI-generated responses
Module 4: AI Risks, Responsibilities & Organisational Impact

• Privacy and sensitive data risks
• Bias, fairness, and ethical concerns
• Reliability and decision-making risks
• Intellectual property and copyright
• Compliance, governance, and internal policies

Case Discussion:
AI misuse scenarios in the workplace
Module 5: Prompt Engineering & Context Engineering Fundamentals

• What is a prompt and why it matters
• Prompt structure:
  • Role
  • Context
  • Task
  • Output format
• Context engineering for better results
• Common prompting mistakes
• Refining AI responses safely

Hands-On:
Write and improve prompts for workplace tasks
Module 6: Practical Workplace Applications of Generative AI

• Email drafting, rewriting, and tone adjustment
• Meeting notes and summarisation
• Research and information synthesis
• Brainstorming, ideation, and planning
• Comparing tools:
  • ChatGPT vs Gemini vs Copilot
  • Perplexity for research
  • NotebookLM for document-based analysis

Hands-On:
Apply AI to real daily work scenarios
Module 7: Using AI Safely & Responsibly at Work

• What data should never be shared with AI
• Verifying AI outputs
• Responsible AI usage checklist
• Human-in-the-loop best practices
• Aligning AI usage with organisational values

Outcome:
Participants gain confidence using AI without increasing risk
Module 8: Measuring Productivity & Building an AI-Ready Culture

• Identifying productivity gains
• Measuring time saved and quality improvement
• Embedding AI responsibly into workflows
• Continuous learning as AI evolves
• Next steps: from AI literacy to AI capability