Information and Analytical Competencies of Librarians in the Field of Artificial Intelligence: Review of Studies

The rapid development of artificial intelligence (AI) technologies is challenging the library community to fundamentally rethink professional competencies. This review aims to analyze publications devoted to the transformation of library information and analytical work in the era of generative neural networks. Particular attention is paid to identifying contradictions between traditional library practices, educational standards, and new labor market demands. The analysis reveals a growing demand for a transition from information to analytical functions. Russian researchers view the integration of AI not only as a modernization of library facilities but also as a necessary condition for maintaining the library’s social significance. In the face of exponential growth in unreliable information, the key mission of librarians is to combat information noise and provide expert data validation. However, existing educational models often remain in the pre-neural network paradigm and fail to address the technological skills gap. International studies emphasize the concepts of “algorithmic literacy” and “hybrid professionalism”, where technical skills are combined with ethical expertise. Theoretical models describing the structure of this new literacy are examined, combining the ability to use AI tools with an understanding of the influence of algorithms on cognitive processes. Modern librarians should act as facilitators, teaching users how to correctly and safely interact with neural networks. Empirical data on the demand for data mining and prompt engineering skills is analyzed. A conclusion is drawn regarding the need to develop a comprehensive competency model, within which librarians will act as hybrid professionals, combining traditional methods of information and library work with mastery of advanced AI technologies.

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