Artificial intelligence (AI) technologies are currently being applied in all areas of society, transforming industries and opening up new opportunities for innovation. Because AI systems are capable of recognizing, learning, planning, interacting with people and the environment, building models that synthesize data, and make predictions or decisions based on new data, the significance of their application in modern science and education is only growing. An analysis of open sources and case studies in foreign and domestic historical scholarship has demonstrated significant interest from both academic institutions and commercial organizations in integrating big data and deep learning technologies into historical document analysis projects. The results help identify new patterns in vast text collections. Although the Russian scientific community is cautious about the use of AI technologies and methods in the humanities, including Russian historical scholarship, researchers note the effectiveness of large language models for analyzing historical sources, combined with critical comparisons with traditional research methods. This article analyzes the use of generative neural networks in research projects at several universities and the integration of AI tools into the educational process at Moscow City Pedagogical University for the implementation of history programs. The risks and limitations of using AI services and generative neural networks in historical research and education are explored in detail.

Artificial intelligence (AI) technologies are currently being applied in all areas of society, transforming industries and opening up new opportunities for innovation. Because AI systems are capable of recognizing, learning, planning, interacting with people and the environment, building models that synthesize data, and make predictions or decisions based on new data, the significance of their application in modern science and education is only growing. An analysis of open sources and case studies in foreign and domestic historical scholarship has demonstrated significant interest from both academic institutions and commercial organizations in integrating big data and deep learning technologies into historical document analysis projects. The results help identify new patterns in vast text collections. Although the Russian scientific community is cautious about the use of AI technologies and methods in the humanities, including Russian historical scholarship, researchers note the effectiveness of large language models for analyzing historical sources, combined with critical comparisons with traditional research methods. This article analyzes the use of generative neural networks in research projects at several universities and the integration of AI tools into the educational process at Moscow City Pedagogical University for the implementation of history programs. The risks and limitations of using AI services and generative neural networks in historical research and education are explored in detail.