# AI For Everyone (AI4E) — Course Map *Upload this file to your AIRI Mentor (Claude Project / Gemini Gem / Custom GPT / Copilot) as context, alongside the AIRI Framework Guide. It tells your Mentor how the course is structured, what each lesson covers, and where the hands-on work happens — so it coaches against the real lessons instead of guessing.* aiready.sg · AI4E v5 · built on the AIRI Framework (AI Readiness Index) The course has three parts plus a setup lesson and a final submission. You move up a readiness ladder toward **AI Ready (Level 2)** — using AI regularly on real work, verifying outputs, and taking on work that was not practical before AI. Each lesson below lists what you will be able to do after it (its learning outcome). --- ## Welcome - **Welcome to AI For Everyone** Understand how AI4E works — the readiness ladder from L0 to L4, the three parts, and the guardrails — through the Journey to the West story spine; set up your AIRI Mentor and the AI Field Guide. ## Part 1 — Know Your AI Readiness *(concepts; checks of understanding)* 1. **Mindset · What AI Can and Cannot Do** Recognise where you sit on this dimension and explain in plain terms what today's AI tools and agents can and cannot reliably do in your field — the AI-Ready (L2) bar; the hands-on practice lives in Part 3. 2. **Mindset · Keeping Up With AI** Recognise where you sit on this dimension and follow AI developments on a regular weekly cadence or through structured training, and keep what you learn applied — the AI-Ready (L2) bar; the hands-on practice lives in Part 3. 3. **Mindset · Where AI Fits in Your Role** Recognise where you sit on this dimension and identify which of your tasks are routine/coordination (AI-replaceable) versus judgement (irreplaceable) — the AI-Ready (L2) bar; the hands-on practice lives in Part 3. 4. **Mindset · Handing Work to AI** Recognise where you sit on this dimension and delegate whole sub-tasks to AI and agents and supervise the result, rather than doing those parts yourself — the AI-Ready (L2) bar; the hands-on practice lives in Part 3. 5. **Mindset · Trying AI on New Problems** Recognise where you sit on this dimension and try AI on a problem you have not used it for before, on your own initiative, at least monthly — the AI-Ready (L2) bar; the hands-on practice lives in Part 3. 6. **Ethics · Spotting Ethical Issues in AI** Recognise where you sit on this dimension and identify the ethical considerations in an AI use and check for bias in the outputs you rely on — the AI-Ready (L2) bar; the hands-on practice lives in Part 3. 7. **Ethics · Knowing the Risks** Recognise where you sit on this dimension and name three or more common AI risks — bias, mistakes, privacy, made-up answers — including risks from AI that acts for you — the AI-Ready (L2) bar; the hands-on practice lives in Part 3. 8. **Ethics · Checking AI's Work** Recognise where you sit on this dimension and fact-check important AI outputs and review agent-completed work before accepting it, and catch the errors — the AI-Ready (L2) bar; the hands-on practice lives in Part 3. 9. **Value · Finding Your AI Use Cases** Recognise where you sit on this dimension and identify two or three specific tasks in your own work where AI could genuinely help — the AI-Ready (L2) bar; the hands-on practice lives in Part 3. 10. **Value · Saving Real Hours With GenAI** Recognise where you sit on this dimension and use GenAI regularly enough to save two to four hours a week on specific tasks — the AI-Ready (L2) bar; the hands-on practice lives in Part 3. 11. **Data · What Good Data Looks Like** Recognise where you sit on this dimension and describe the data-quality needs for both structured data and documents/text in AI work — the AI-Ready (L2) bar; the hands-on practice lives in Part 3. 12. **Data · Working With Data Day to Day** Recognise where you sit on this dimension and analyse data, create charts, and draw insights as a regular part of your work — the AI-Ready (L2) bar; the hands-on practice lives in Part 3. 13. **Tools · Using AI Tools and Agents** Recognise where you sit on this dimension and use several AI tools regularly, or hand a multi-step task to an AI agent with clear instructions — the AI-Ready (L2) bar; the hands-on practice lives in Part 3. 14. **Tools · Writing Clear Instructions for AI** Recognise where you sit on this dimension and break a complex job into clear instructions an AI can follow — including what a good result looks like and any limits — the AI-Ready (L2) bar; the hands-on practice lives in Part 3. 15. **Tools · Producing Real Work With AI** Recognise where you sit on this dimension and use AI tools or agents to produce real work — reports from data, tidy files, formatted documents — without any coding — the AI-Ready (L2) bar; the hands-on practice lives in Part 3. ## Part 2 — How AI Actually Works *(concepts; checks of understanding)* 1. **What AI Actually Is (Not Magic, Not Movies)** Explain what AI is in plain language, give 3 everyday examples of AI already in use, and describe why 'AI is just maths' (demystification). 2. **How AI Models Are Trained (The Hokkien Mee Analogy)** Describe the seven steps of building an AI system using the Hokkien Mee analogy, and map a real-world AI project to those steps. 3. **Predicting Numbers: Regression** Explain what regression does, identify tasks where it applies, and describe why some predictions are possible and others are not. 4. **Sorting Into Categories: Classification** Explain how classification works, distinguish it from regression, and identify tasks where classification applies. 5. **Finding Patterns Without Labels: Clustering** Describe how clustering differs from classification, explain when clustering is the right approach, and identify tasks where discovering hidden groups would be valuable. 6. **Computer Vision: How Machines See** Describe how a computer represents and processes images, explain how CV models recognise objects, and identify practical CV applications in your industry. 7. **Generative AI: How It Actually Works** Describe how an LLM generates text (next-word prediction), explain why responses vary, and describe how AI-generated images are created. 8. **Data Is Everywhere (Documents, Emails, Spreadsheets)** Recognise that 'data' for AI includes documents, emails, reports, chat logs, and spreadsheets — not just databases. Identify 5 data sources in your daily work that AI could process. 9. **AI Has Limitations (Hallucinations, Bias, Errors)** Recognise that AI can hallucinate (invent plausible-sounding nonsense), exhibit bias, and make confident errors. Give 2 examples of each. ## Part 3 — Working With AI Tools *(hands-on; each lesson has a task you submit)* 1. **The AI Tool Landscape (and Choosing the Right One)** Describe the 4 major AI assistant platforms (Claude, Gemini, Copilot, ChatGPT), what each does well, and how to access them. Choose one to use for this course. 2. **Prompting as a Professional Skill** — *you build your AIRI Mentor here* Construct effective prompts using the CRISP framework (Context, Role, Instructions, Specifics, Parameters). Transform 3 vague requests into structured prompts. 3. **Structured Prompting (Specification Thinking)** Specify complex tasks as structured instructions: break multi-step work into clear components, define expected outputs, set constraints, and provide examples. Apply to one real work task. 4. **Building Your First AI Skill/Agent (No Code)** Build a custom AI agent (Custom GPT, Gem, Claude Project, or Copilot agent) that handles one recurring task in your work. No programming — just structured instructions and examples. 5. **Agentic AI Workflows** Delegate a multi-step task to an agentic AI tool — Claude Cowork, Gemini Spark, ChatGPT agent mode, or Microsoft Copilot agents — and evaluate the result by documenting what was delegated, what boundaries were set, and what you reviewed before accepting the output. 6. **Creating Visuals and Presentations with AI** Create professional visuals using at least two AI tools — a slide deck (Gemini in Google Slides), an interactive diagram (Claude), and a video overview (NotebookLM) — by writing clear visual briefs that specify content, audience, and format. 7. **Deep Research for Evidence-Based Decisions** Commission a Deep Research task using Claude, Gemini, or Perplexity — writing a clear research brief that specifies context, scope, criteria, and output format — and evaluate the resulting report against your original brief. 8. **AI-Powered Analysis Without Coding** Perform a data analysis task using AI (summarise trends, compare options, extract insights from a document) without any programming. Use natural language instructions only. 9. **Document Synthesis and Data-Driven Decisions** Synthesise information across multiple documents using NotebookLM (cross-document questions, Audio Overviews) and justify a business decision using AI-assisted analysis — gathering evidence from internal documents and external research, challenging the reasoning, and presenting a recommendation with evidence. ## AI Ready Submission - **Your AI Ready Submission** Review the work you submitted across Part 3 — your real prompts, the AI's replies, and the fixes you made — confirm it, and submit it for grading; and understand how your AI-Ready mark is worked out (your quizzes across the course plus your Part-3 work against the AI-Ready criteria, seventy per cent to pass), the result, pending, and retake flow. --- **How the assessment works:** the Part 3 lessons each end with a hands-on task you submit as that lesson's assignment. There is no separate exam — your AI Ready Submission at the end reviews those submitted tasks and gives you a single score with per-criterion feedback. Your AIRI Mentor coaches you through each task and helps you capture it sharply; it does not submit on your behalf.