Abstract
The article offers an analytical overview of the use of artificial intelligence (AI) in the education system, with a primary focus on the teacher’s work with neural networks. From a historical and methodological perspective, it traces the evolution of AI from expert systems to generative models and demonstrates how shifts in technical paradigms have influenced didactic practices and the culture of academic communication. The section on the contemporary Russian context outlines the institutional and regulatory frameworks for AI implementation, the development of domestic platforms, and issues of localizing language models for humanities-oriented tasks. It is shown that AI in educational technologies reorients the focus from knowledge transmission to the orchestration of learning activities, enabling individualized learning pathways, adaptive diagnostics, and scalable feedback.
Particular attention is paid to multimedia interactive online courses (MIOC) as a format that integrates generative and analytic tools. The section on neural networks and the teaching of literature presents the concept of an expanded pedagogical ecosystem – “teacher – student – tool – task – data.” It is demonstrated that generative models and specialized AI tools can consistently and safely assume the roles of tutor (adaptive support for reading and writing), co-author (collaborative design of interpretations), assessment instrument (formative diagnostics with transparent criteria), and trainer (scalable practice in rhetoric and analysis). The conclusion delineates the prospects and challenges of employing artificial intelligence (particularly generative neural networks) in humanities education.
Keywords
Artificial intelligence; education; humanities education; generative neural networks; teaching literature.