GigaChat AI in the Philosophy Educator's Work: The Dawn of the Algorithmic Future in Teaching and Methodology

Introduction. UN Secretary-General António Guterres called for greater integration of digital technologies into the educational process, which will enable the creation of fundamentally new educational platforms. Various software tools based on generative artificial intelligence are increasingly finding their way into the educational process. The purpose of this article is to comprehensively study the possibilities of using the domestic GigaChat neural network in the professional (educational and methodological) activities of philosophy teachers. Materials and Methods. The study is based on an interdisciplinary methodology. In particular, a set of analytical methods is used: quantitative, qualitative, content and SWOT analysis. In addition, the case method is used to test the effectiveness of GigaChat tools in solving specific educational tasks. Results. An experimental test of the educational and methodological capabilities of GigaChat was conducted in the context of a philosophy course within the following clusters: development of materials for lectures and seminars, creation of an assessment toolkit, compilation of a list of relevant educational literature, review of foreign literature, automation of student work assessment, creation of a model for consultative feedback with students, and development of educational programmes for computers. The results of the study demonstrated that the domestic neural network service GigaChat can be effectively used in the creation of all typical types of teaching materials for a philosophy course. The experiment showed that GigaChat is capable of creating resources for original educational projects – specialised computer games. At the same time, the use of GigaChat has limitations in terms of content assessment and personalised feedback on the discipline, and also requires mandatory verification of information. The results obtained underwent independent expert evaluation and testing at the relevant departments of three universities: LSPU named after P.P. Semenov-Tyan-Shansky, LKITiU (a branch of MGUTU named after K.G. Razumovsky), and DSPU named after V. Shatalov (Russian Federation). KEYWORDS Conclusion. It has been experimentally confirmed that neural network platforms are a promising tool for optimising teacher resource preparation and cognitive reorientation towards more creative forms of educational activity. GigaChat can help implement teachers' original educational and methodological ideas, which can significantly diversify the formats and informational content of educational practices in philosophy.