ARTIFICIAL INTELLIGENCE IN PERSONALITY PSYCHODIAGNOSTICS

Abstract

The article provides a comprehensive analysis of the integration of artificial intelligence (AI) into the field of personality psychodiagnostics, considered as one of the key directions in the development of modern psychological science and practice. The potential of machine learning and deep learning algorithms in the early detection of mental disorders, automated diagnostics, and treatment outcome prediction is revealed. It is emphasized that due to the ability to process large-scale datasets, AI improves the accuracy of identifying depression, anxiety, and post-traumatic stress disorders, while also ensuring objectivity often lacking in traditional methodologies. Significant attention is devoted to digital phenotyping as an innovative tool based on tracking activity through smartphones, social networks, and daily communications. This approach is shown to have notable advantages over classical questionnaires, as it allows for the evaluation of individual differences and the dynamics of psycho-emotional states in real time. The article also examines the use of AI in mental health monitoring: mobile applications and sensor devices with AI algorithms provide continuous supervision, helping to prevent crises such as suicidal ideation. Furthermore, the article outlines opportunities for automating routine tasks – from processing psychodiagnostic methods to generating clinical reports and managing databases – which reduces the workload of professionals and enhances the standardization of procedures. An important direction of development is virtual therapeutic systems and chatbots that, applying cognitive-behavioral therapy (CBT) principles, can provide users with basic support at any time. At the same time, ethical challenges are highlighted, including risks of depersonalization of care, issues of confidentiality and algorithmic transparency, as well as potential threats of algorithmic bias.