Keyword: «artificial intelligence in education»

The article discusses the didactic potential and methodological aspects of using the DeepSeek neural network in the process of developing the foreign language communicative competence of undergraduate students (using the German language as an example). The article analyzes the platform's functionality, including the generation of authentic dialogues, the creation of multi-level exercises, the simulation of communicative situations, instant feedback, the ability to work with text files, and the search for relevant information. The article concludes that the use of DeepSeek in a methodologically sound manner enhances motivation, improves the effectiveness of speech practice, and promotes the development of students' autonomy.

The article addresses the problem of using generative neural networks (using DeepSeek as an example) for solving school problems in thermodynamics. Based on the analysis of four classical problems on the heat balance equation, stable markers are identified that distinguish an AI-generated solution from a student's work: technical artifacts in LaTeX, transliteration of Russian words, excessive precision of calculations, presence of «verification» blocks, step-by-step numbering of processes, and the use of non-standard constant values. It is shown that problems containing a graph require visual data analysis and therefore fundamentally cannot be solved by a neural network without a textual description, which makes them an effective tool for preventing cheating. Specific methodological techniques for adapting tasks for grades 8 and 10 are proposed, reducing the effectiveness of using AI without changing the physical complexity of the problems.