What this course is
A survey of what AI actually is and how it fits into ordinary work. Each module pairs a short, jargon-free lecture with a much longer lab where you use the tools yourself (writing, research, data analysis, creative work) while learning to check what comes back for accuracy, bias, and ethical problems.
By the end of the course you will be able to:
- Use AI tools to complete real tasks in writing, research, data analysis, and creative expression.
- Evaluate AI outputs for accuracy, bias, and ethical implications, and justify your judgment.
- Own what you build with them: understand the parts, know your alternatives, and keep the result.
- Decide when not to use them at all, and spend the time you save on people rather than on more output.
How this course thinks
Most AI teaching stops at operating the software. Three commitments run underneath every module here, and they are the reason the labs are shaped the way they are.
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01
Agency
The first question is never how to use an AI tool. It is whether, and for what. You will practise saying no to these tools as deliberately as you practise using them, because a choice you cannot decline is not a choice.
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02
Ownership
The chip in a current tablet or laptop will already run a capable model without touching the internet, and that capability gets cheaper every year. Learn the parts (models, gateways, open ecosystems), run one yourself, and publish what you make somewhere you control.
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03
Humanization
Use the machine on the drudgery, so your attention goes to people. Send the one-page answer somebody actually wanted, not the thousand words a model will happily generate. And never let a system quietly turn a person into a number.
Within a few years, serious AI will be an ordinary household appliance. The people who understand what it is made of, and what it is for, will be the ones deciding how it gets used at home, at work, and in their communities. None of that requires code, a budget, or a computer science degree.
The eight modules
Each module page carries its objectives, a minute-by-minute agenda for a three-hour session, lab instructions, and homework. Enough for someone else to pick it up and teach it.
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Module 1
What is AI? Past, Present, and Future
Definitions, machine vs. human intelligence, and the key terms: LLMs, machine learning, neural nets.
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Module 2
Generative AI for Everyday Productivity
Text generation, summarization, and translation, including the discipline of sending less, not more.
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Module 3
Working with AI Images, Audio, and Video
Diffusion models, speech-to-text, text-to-speech, and how to spot a deepfake.
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Module 4
AI and Data: Spreadsheets & Visualization
Using AI in Excel and Google Sheets, basic data literacy, and chart generation.
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Mini-lesson Module 5
AI for Research and Information Gathering
How LLMs search, why they hallucinate, and how to fact-check everything they tell you.
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Module 6
Ethics, Bias, and Responsible AI
Bias, privacy, copyright, and environmental cost, plus the systems that quietly turn people into numbers.
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Module 7
Automation with AI Assistants
Workflow automation, Zapier, and AI copilots. Hand the drudgery to the machine and get your attention back.
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Module 8
Capstone Showcase
Design, build, and present your own AI-assisted project: a study aid, creative piece, or workflow.
Suggested pacing
The modules are written for three-hour sessions, because that is what the material wants: a short lecture and then a long stretch of actually doing the thing. Ten sessions is a comfortable term. Run it weekly, run it over a weekend, or work through it alone at whatever speed suits you.
Module 5 is highlighted because it is the source of the 15-minute mini-lesson, a standalone segment you can watch, or teach, on its own.
How to use this course
Everything here is free, and it is meant to be taken. Work through the modules on your own, or lift them wholesale and run the course for a club, a library, a classroom, or a group of friends. Attribution is welcome, a permission slip is not required.
- Learning alone? Follow the module pages in order and actually do the labs. The reading is short; the doing is the course.
- Teaching it? Each module page is a lesson plan, with a timed agenda you can teach straight from. Start with the mini-lesson to see the format.
- Materials. No textbook purchase. The recommended companion is Elements of AI (University of Helsinki and MinnaLearn; no affiliation, it just teaches well). Beginner-friendly, no math. Everything else is on the Resources page.
Ready when you are
Begin with Module 1
Free, self-paced, and yours to keep. Work through it alone or lift the whole thing and teach it.