Learning path
AI literacy basics
The way into the technical foundations: step by step you sort out what an AI model is made of, how language becomes numbers, how a model decides, and why training costs so much more than using it.
For anyone who uses AI at work or in everyday life and wants to understand what happens technically. No prior knowledge needed.
- 6 lessons
- ≈ 84 min reading time (about 1.5 h)
- 38 questions in recall moments
Reading time is estimated from the text length (200 words per minute). Recall moments and interactive demos come on top.
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Learning goals
After this learning path you can …
- Tell program, algorithm and AI model apart, and explain why a model is not made of written-down rules but of numbers that are set during training.
- Describe what an AI model receives as input, why its output is usually a list of ratings, and how an answer is built from it piece by piece.
- Explain how a tokenizer splits text into reusable pieces, numbers them through a fixed vocabulary, and so produces the numerical input of a language model.
- Place scalar, vector, matrix and tensor, and explain why a chat message or a photo is a block of numbers to a model.
- Explain how a language model turns scores into probabilities, why it does not always pick the obvious option, and what temperature changes about that.
- Describe what is inside a finished model, what is fixed before training, why training costs more than inference, and what hardware a model needs.
Order
The lessons on this path
Every lesson also stands on its own. In this order, each builds on terms the previous one explained. A lesson counts as completed once you have read it to the end and answered every question of its recall moment once. Whether right or wrong does not matter for that.
Program, Algorithm, Model Compared
Telling program, algorithm and AI model apart: why a model is not made of written-down rules but of numbers that are set during training.
CompletedReadInput and Output: What a Function Does
What does an AI model receive, and what does it give back? From the spam filter to image recognition to the chatbot: why the output is usually a list of ratings, and how an answer is built from it piece by piece.
CompletedReadTokenizers: How Language Becomes Numbers
Shows how a tokenizer splits text into reusable pieces, numbers them through a fixed vocabulary, and turns them into the numerical input of a language model.
CompletedReadScalar, Vector, Matrix, Tensor: the Building Blocks of Numbers
From the weather app to the chatbot: how a number, a list, a table, and a stack of tables relate to each other, and why your chat message and your photo are exactly such blocks of numbers for a model.
CompletedReadProbability and Softmax: How a Model Decides
How a language model turns its scores into percentages, why it sometimes picks the obvious choice and sometimes something else, and what temperature has to do with it.
CompletedReadParameters, Training vs. Inference, Hardware: How a Model Runs
What is inside a finished AI model, what people decide before training, why learning costs so much more than using, and what hardware a model needs.
CompletedRead
To be honest
What this learning path does not cover
The path explains how AI models work technically. Using AI safely at work takes more, and this is not covered here:
- legal questions such as obligations under the EU AI Act, data protection, or copyright
- risks and limits of specific AI systems in your own work context
- how to operate particular tools or write prompts
At the end
Your learning record
Once you have read every lesson and done its recall moment, you can create a learning record: with your name, the date, the learning goals and the scope, to print or save as a PDF. It is a self-declaration, not verified, and deliberately carries no score. It can be part of your documentation of AI literacy measures (Art. 4 EU AI Act).
Want to use the learning path in teaching or training? Notes for educators & trainers