Why is almost everything about AI in English?
Historically, research, documentation, and the first courses on artificial intelligence are published in English. The terms appear there and do not always have an established translation. Because of this, a newcomer to Europe often faces the dual task of understanding the topic itself and at the same time understanding a foreign language. Even a confident level of English does not help: reading technical text in a non-native language requires more attention, you read more slowly and get tired faster. As a result, many people give up not because the topic is difficult, but because the barrier to entry seems higher than it actually is.
The native language releases strength for the essence
The brain processes its native language almost automatically, but a non-native language with effort. When you read an explanation in your language, all your attention goes to the idea, and not to deciphering the words. This is especially important in AI training, where there are many new concepts: model, prompt, context, token. If each of them comes through translation, the load adds up and makes it difficult to integrate the knowledge into the system. In your native language, you more easily connect new things with what you already know, ask more precise questions to yourself and your mentor, and move more quickly from theory to practice. Simply put, you learn to think about AI, not translate about AI.
Is it necessary to write prompts in English?
A common fear: since the model studied mainly in English, it means that requests must be written in English. Modern language models understand dozens of languages well and respond to the one you write in. For most everyday tasks—writing, getting ideas, parsing text, helping with spreadsheets—the native language works great and saves you time. English will come in handy specifically: if you are looking for fresh, narrow documentation or working with a team in English. But this is a choice for the task, and not a prerequisite. It’s easier to start and master a skill in your own language, and use a second language when you really need it.
What good learning looks like in your language
Translating the interface is not enough. Real learning in your native language means that the theory, assignments, tips, and mentor’s answers are written in it. It is important that the examples are close to your reality: work letters, documents, tables, and not abstractions from another culture. Even more important is practice with feedback: you try, get an analysis of the error in understandable language, and correct it immediately. When you can ask in simple words “why this and not otherwise” and receive a clear answer, knowledge is consolidated. This format turns the language from an obstacle into a support and significantly speeds up the path from the first lesson to confident work.
LearnAI: learn to work with AI in your language
LearnAI is built around this idea. The platform is available in 18 languages: theory, assignments and error analysis open in the language you choose. Aura's inner mentor answers your questions in simple words in your language - you can clarify and ask again until it becomes clear. The training is practical: you solve real problems - texts, ideas, working with data, first automations - and immediately see the result. You can start for free and at your own pace, while the language remains your ally and not a barrier. If you have any questions, write to support@learnaiskills.cloud - we’ll help you choose where to start.