• Julia V. Borisenko, Kemerovo State University (Kemerovo, Russia)
• Vladislav I. Borisenko, ITMO University (Saint-Petersburg, Russia)

The issues of mental and psychological health of adults, children and adolescents are becoming particularly relevant in the modern world. Urbanization, acceleration of the pace of life, digitalization of communication, mediatization of the educational environment and increasing demands on the volume of information processed by students of modern educational organizations can affect their emotional state, which can lead to various emotional disorders, the absence of which is one of the criteria for mental health. In the paper we present the results of the developing and programming of a chatbot for diagnosing the parameters of the respondent's mental health. The neural network was trained to recognize signs of depressive, anxious or aggressive tendencies of the respondent in the dialogue. In each case, neural network offers its own version of diagnostic instruments and recommendations for a respondent. We developed telegram chatbot to identify signs of depression, auto-aggression, or anxiety in the respondent's statements during communication. When identifying signs of risk, the chatbot offers a number of questions and, with informed consent, to take a psychological test. Several psychological tests were uploaded: the A. Beck Depression Scale; the A. Beck Anxiety Scale; the Auto– and Heteroaggression Questionnaire by E. P. Ilyin. The GigaChat neural network from the Sber company was used to communicate with the user. The programs were written in Python. This program can be used by educational organizations for monitoring the parameters of the emotional state of students.

neural network, neural network training, chatbot, mental health, depression, anxiety, aggression

2025-12-01