This article examines the impact of artificial intelligence (AI) on changes in social research methods. The authors focus on the potential and limitations of AI use for describing and analysing sociological data. They also examine how experts perceive AI-generated texts compared to descriptions prepared by professional analysts. The purpose of this study is to empirically evaluate the effectiveness of artificial intelligence (a neural network language model) in automating the description and interpretation of tabular research data using social research as an example.

The article addresses aspects of AI application in social research, including issues of ethics, copyright, and plagiarism, as well as the need to develop a regulatory framework that facilitates the integration of AI into empirical research. The authors have developed an algorithmic model for integrating AI into tabular data analysis, representing a business process map in BPMN 2.0 notation. A concept for applying AI to the description of tabular data from social research results is presented.

The empirical basis of the study consists of an expert survey (n = 24) of representatives of the academic and professional sociological community. The criteria for participation in the survey included at least five years of experience in social research and the rank or position of professor/associate professor at higher education institutions. Data was collected through structured, in-person interviews with experts using a blind comparison format. Experts were asked to evaluate two versions of data table descriptions from real sociological studies. The first version contained a description prepared by analysts at a leading Russian sociological center. The second version was developed by the authors using a neural network language model. During the study, the experts evaluated the data table descriptions according to five criteria: structure, logical presentation, completeness, correctness of terminology, and validity of conclusions without identifying the source of the accompanying texts.

As a result, more than half of the experts preferred the AI-generated descriptions, noting their advantages in terms of logical presentation and correctness of terminology. In their conclusion, the authors formulated proposals and recommendations for expanding the use of AI in scientific practice.

social consequences of artificial intelligence, sociological research, artificial intelligence in science, automation of analysis, data verification

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