Artificial Intelligence and Historical Science: From Interpretation to Modeling
Artificial Intelligence and Historical Science: From Interpretation to Modeling
SDG 4: Quality Education
On April 9, 2026, a webinar titled “Artificial Intelligence and Historical Science: From Interpretation to Modeling” was held at Kazakh National University named after al-Farabi, at the Faculty of History, under the Department of History of Kazakhstan.
The speaker of the webinar was G.I. Mensitova, Coordinator of Distance Learning Technologies (DLT) at the Faculty of History and Senior Lecturer. The event was organized by the faculty members of the Department of History of Kazakhstan: G.B. Khabizhanova, A.O. Koshymova, and A.N. Saginayeva.
The presentation highlighted the key directions of transformation in historical science in the context of digitalization, ranging from the traditional interpretation of historical sources to the application of big data analysis, machine learning, and digital modeling of historical processes. Particular attention was given to the capabilities of artificial intelligence in working with historical sources, including text recognition, automated translation, entity extraction, and the analysis of large-scale archival data.
Students, master’s students, and faculty members of the Faculty of History actively participated in the event, contributing to a productive multi-level academic dialogue and exchange of scholarly ideas. The discussion addressed the use of digital tools in the study of migration processes, the reconstruction of ethnohistorical structures, and the analysis of political systems of early medieval societies in Central Asia.
Special emphasis was placed on the integration of digital humanities methods, including GIS modeling, network analysis, and the visualization of historical data, which opens up new opportunities for reconstructing historical reality and advancing interdisciplinary research.
The event served as an important academic platform for discussing current trends in the development of historical science and for shaping new research strategies in the era of digital technologies.
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