Historical Science in the Era of Generative Artificial Intelligence: The Transformation of Historical Methodology

Recent advancements in generative artificial intelligence are profoundly reshaping the discipline of history, altering its methodologies and research practices. Traditional methods of historical data analysis face challenges in processing large volumes of digitized sources, making the application of artificial intelligence particularly relevant. The purpose of this study is to analyze the influence of generative artificial intelligence on historical methodology, identifying its capabilities and limitations in working with historical sources, text analysis, and the creation of historical narratives. The methodology includes a critical review of academic publications, a comparative analysis of traditional and modern historical research methods, and an empirical assessment of the use of generative models in studying the past. The results demonstrate that generative artificial intelligence can automate routine research processes, enhance text analysis, and help uncover hidden relationships. However, its application presents methodological and ethical challenges, such as the issue of data reliability and the necessity of critical verification. The scientific novelty of this study lies in the comprehensive analysis of the impact of generative artificial intelligence on historical research and the proposal of principles for its thoughtful and responsible use.