The background material compiles the knowledge base for AI recommendations and supports education providers and teachers in developing AI competence and utilizing AI locally.

The background material covers various themes and should be used as a whole.

Artificial intelligence (AI) is not a new invention in itself, but its large-scale utilisation in different sectors has only recently become possible as a result of the increase in the computational power of cloud servers and the amount of data available. The term artificial intelligence can be defined in different ways depending on context or branch of science. From the user's point of view, AI can include all applications that participate in intelligent activities typical of people, such as decision-making, text or speech recognition and production, or creating new images and compositions.    

In technological terms, AI can be defined as the ability of computer applications to generate new, non-preprogrammed solutions or reasoning rules. Whereas traditional computer software is based on rules and operating instructions programmed in advance by humans, AI applications, in contrast, form reasoning rules based on the data they are fed. In this sense, AI applications can be seen as learning from data, which is referred to as machine learning.  

The EU Artificial Intelligence Act (AI Act) defines AI systems as follows:  

AI system means a machine-based system that is designed to operate with varying levels of autonomy and that may exhibit adaptiveness after deployment, and that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments. 

Generative AI refers to AI applications that can generate new content, such as text, images and videos. Generative AI applications are often based on large amounts of training data that allow them to learn new models with which to generate new content. Generative AI utilises machine learning and so-called large language models (LLMs), which are used to generate and process human-like language.  

Reactive AI refers to AI in the context of applications that react to provided data or situations without actually learning from previous experiences or extensive teaching data. One example of reactive AI could be a computer application that plays chess.  

Predictive AI means AI applications that analyse historical data and past experiences to predict future events or future behaviour. These often include the recommendation algorithms of online services, which recommend different content to their users.  

Updated