AI is now a colleague.
Learn to work with it responsibly.
This course gives you the knowledge, judgement and practical habits to use AI tools ethically at work, in study, and in everyday life — through short units, hands-on labs and real-world scenarios.
By the end of this course you will be able to:
Beyond questions: put AI to work
Most people treat AI like a search engine: ask, read, leave. The real value starts when you delegate a task, not just a question.
❓ Asking (where most people stop)
"What makes a good CV?" · "What can I cook with chicken?" · "How do I run a meeting?"
You get generic information — then you still have to do all the work yourself.
🤝 Delegating (where the value is)
"Rewrite my CV for this job advert." · "Plan my meals for the week using what is in my fridge." · "Build me a 30-minute agenda for Monday's project meeting."
You get a finished draft of the actual task — which you then review, refine and own.
The task recipe: four ingredients
🎯 Goal
What do you want produced? An email, a plan, a table, a checklist, a rewrite. Name the deliverable, not the topic.
🧩 Context
What does the AI need to know? Your situation, audience, background facts. AI can't read your mind — context is where most prompts fail.
📐 Format
What should the output look like? Length, structure, tone, bullet points versus prose, table columns.
🚧 Constraints
The rules: word limits, budget, things to avoid, deadlines, dietary needs, level of formality. Constraints turn generic output into your output.
The prompt builder
Pick a task, then switch on each ingredient and watch the prompt — and its quality — transform.
Steal these ideas
💼 At work
- Turn messy meeting notes into actions with owners and deadlines
- Rewrite a difficult message in a calmer, clearer tone
- Summarise a long report into a one-page brief for your manager
- Draft job descriptions, rotas, checklists and handover notes
- Prepare interview questions — or practise answering them
🏡 In life
- Meal plans around your budget, diet and what is already in the fridge
- A step-by-step plan for a house move, event or family trip
- Explain a confusing letter, bill or contract in plain English
- A personalised study or fitness plan you can actually stick to
- Draft complaints, appeals and applications — then personalise them
🔁 One more habit: it's a conversation, not a slot machine
The first draft is a starting point. Reply and refine: "Make it shorter." · "More formal." · "Give me three options." · "You've assumed X — actually it's Y, redo it." Iterating is not failure — it's how skilled users work. And everything you produce this way still follows the rules of this course: verify it, disclose it where needed, and own it.
The first draft is never the last
Skilled users treat AI output as a first draft to direct, not a final answer to accept. You don't start over — you reply.
The refinement studio
The AI has drafted an email announcing a new booking system to your team. It's… not great. Apply refinements and watch it transform. Click a command again to undo it.
💡 What this lab teaches
- Refining beats regenerating. Small, specific instructions ("shorter", "warmer", "plainer") get you to a usable result far faster than re-rolling and hoping.
- You are the editor-in-chief. Direction, taste and judgement stay with you — the AI just types faster than you do.
- Name what's wrong. "I don't like it" gets you nowhere. "Too formal, cut the jargon, end with one clear action" gets you exactly what you want.
The prompt doctor
Diagnosing weak prompts is the fastest way to learn to write strong ones. Six patients are waiting. Which ingredient would most improve each prompt?
Consultation room · 0 correct · 6 remaining
🩺 The doctor's rule of thumb
When an AI answer disappoints you, don't blame the AI first — re-read your prompt and ask which ingredient you starved it of. No deliverable named? Goal. Generic answer that could apply to anyone? Context. Right content, wrong shape? Format. Technically fine but useless for your situation? Constraints.
How AI actually works — and why that matters ethically
Modern AI — including chat assistants and prediction systems — does not "understand" the world. It is a pattern machine: it learns statistical patterns from vast training data, then uses those patterns to generate outputs.
Three consequences you must internalise
🎯 Bias in, bias out
If the training data under-represents or misrepresents a group, the AI's outputs will too — systematically, and at scale.
🎲 Confident errors
AI generates plausible text, not verified fact. It can "hallucinate" — invent details, citations and figures that look real but aren't.
🔍 Opacity
Even developers can't fully explain a specific output. That's why human judgement must stay in the loop for decisions that matter.
The five principles of ethical AI use
Global frameworks — from the UK Government's AI regulation principles to the EU AI Act and UNESCO's recommendations — converge on five ideas. Click each card to reveal what it means in practice.
Fairness
CLICK TO REVEALAI must not create or amplify discrimination. In practice: question whether an AI decision would treat different groups differently, and never use AI outputs about people without checking for bias.
Transparency
CLICK TO REVEALPeople deserve to know when AI is involved. In practice: disclose AI use in your work where it materially shaped the output, and never pass off AI-generated work as purely your own where honesty is expected.
Accountability
CLICK TO REVEAL"The AI did it" is never a defence. You remain responsible for anything you submit, publish or act on. In practice: verify before you rely, and own the outcome.
Privacy
CLICK TO REVEALData you paste into an AI tool may leave your control. In practice: never enter personal data, client information or confidential material into tools that aren't approved for it.
Human oversight
CLICK TO REVEALAI advises; humans decide. In practice: keep a competent human in the loop for any decision affecting people's rights, money, health, safety or opportunities.
0 / 5 principles explored
The bias simulator
You are training a fictional hiring AI. The dots below are the historic applications in your training data — Group A and Group B. Historically, the company mostly hired from Group A. Move the slider and watch what the model learns.
Meridian Logistics · hiring model trainer
💡 What this lab teaches
- Bias is structural, not intentional. No one programmed the model to discriminate — it inherited the pattern from history.
- Balanced data helps but isn't a cure. Real fairness work also needs testing, monitoring and human review of outcomes.
- This is why UK equality law still applies. Using a biased AI in hiring may result in unlawful discrimination under the Equality Act 2010. An employer cannot avoid its responsibilities simply by relying on an AI tool or vendor.
What you type in may not stay yours
Every prompt is data leaving your device. It may be stored, reviewed, or used to train future models — depending on the tool and its settings.
Your legal footing: UK GDPR in one minute
👤 Personal data
Information relating to an identified or identifiable living person, such as names, contact details, photos, IDs or opinions about them. When handled for work, sharing it with an AI tool is processing and must comply with data-protection law.
🚨 Special category data
Health, ethnicity, religion, sexuality, political opinions, trade union membership, genetic and biometric data. Extra protection applies. Pasting this into an unapproved AI tool is a serious breach risk.
🏢 Confidential business data
Client lists, contracts, unreleased plans, credentials, safeguarding records. Even if not "personal data", contracts and duties of confidence still apply.
✅ The golden rule
Before pasting, ask: "Would I be comfortable if this appeared publicly with my name attached?" If not — anonymise it, or use an approved tool.
Safer habits
- Strip names, addresses and identifiers before using AI on real documents.
- Check your organisation's AI policy and the tool's data settings before first use.
- Prefer enterprise or approved tools for work data — consumer tools for generic questions only.
- If you cause or spot a data slip, report it early — under UK GDPR, organisations may have as little as 72 hours to report a notifiable breach to the ICO.
Prompt triage
A colleague is about to paste each of these into a public, general-purpose chatbot. Click an item, then click the correct bin.
Triage queue · 0 correct · 8 remaining
✅ Safe to paste
⛔ Stop — don't paste
Use AI without losing your integrity
Ethical use comes down to honesty in three directions:
🗣️ With others
Disclose AI use where it materially shaped the work — assignments, reports, published content. Follow your institution's or employer's rules; when unclear, ask.
📚 About facts
Verify before you rely. Check names, numbers, quotes, citations, and any legal, medical or financial claims against trustworthy sources.
🪞 With yourself
Learn, don't outsource. If you couldn't explain or defend the output in your own words, you're not ready to submit it.
The verification habit
Before acting on any AI output, run the 4-check:
- Source check — can I find this claim in a reliable, independent source?
- Stakes check — what happens if this is wrong? Higher stakes = deeper checking.
- Freshness check — could this have changed recently? AI outputs may be outdated or lack current information.
- Sense check — does this actually fit my context, or is it generic advice dressed up?
Scenario studio
Scenario 1 · The deadline
Scenario 2 · The shortcut
Show what you know
Eight scenario questions. Pass mark: 6/8 (75%). Feedback is shown after each answer.
Congratulations — course complete
This is a certificate of completion for a non-regulated awareness course. It does not award an Ofqual-regulated qualification or professional accreditation.
Certificate of Completion
Using Artificial Intelligence Ethically
including all interactive labs and the final scenario assessment
Your ethical AI toolkit — the one-screen summary
- Remember the pipeline: data in, patterns learned, predictions out — flaws included.
- Run the five principles: fairness · transparency · accountability · privacy · human oversight.
- Apply the golden rule before pasting anything: "comfortable if public with my name on it?"
- Verify with the 4-check: source, stakes, freshness, sense.
- Own the outcome: AI drafts — you decide, you disclose, you're accountable.