Russian Federation
Russian Federation
Purpose: To develop a methodology for using artificial intelligence to solve the problem of intensifying the process of developing students’ professional competencies. To show the need to improve teaching technologies due to a reduction in classroom time allocated to general education disciplines when reconfiguring the curricula for training specialists. To consider the issue of rethinking the effectiveness of the existing distance learning system (DLS) as a means of rationalizing educational procedures in connection with the continuing risk of students acquiring only a minimum level of knowledge due to the use of template electronic demonstration materials in the DLS. Methods: To achieve the stated goals, data from the analysis of the results of the implementation of the “Digital Teacher” (DT) product, developed on the basis of the domestic DeepTalk platform, into the educational process of the ICS Department are used. An algorithm for training a neural network as a mathematical core of the DT has been developed. When used at the first stages of loading the DT with text and presentation materials, the principle of randomization of logically related queries, answers and comments from teachers on lectures and practical assignments is implemented. Results: A technique for iterative adjustment of the DT has been developed and tested when increasing the array of situational data with the following elimination of the contradiction between the requirement for representativeness and the initially small volume of the generated training sample. Practical significance: The creation of a developed neural network product is the first step in the deployment of the University’s digital services system, the hierarchical structure of which according to areas of training, qualifications and specializations should include “Digital Teacher”, “Digital Applicant Curator” and other similar products. The introduction of DT into providing teaching in senior years will contribute to the formation of the student’s individual educational trajectory and the development of his cognitive abilities. Further, in the course of obtaining additional professional education, the use of DeepTalk will ensure the acquisition of skills in interacting with AI, used as a means of supporting decision-making under conditions of uncertainty. DeepTalk allows you to assess the predisposition of applicants to the main types of professional activities at JSCo “Russian Railways”.
System of digital services, generation of educational content, training sample, digital teacher, additional training of models, individualization of education
1. Burakov M. V. Sistemy iskusstvennogo intellekta: uchebnoe posobie / M. V. Burakov. - M.: Prospekt, 2017. - 440 s.
2. Nikolenko S. Glubokoe obuchenie i pogruzhenie v mir neyronnyh setey / S. Nikolenko, A. Kadurin, E. Arhangel'skaya. - SPb.: Piter, 2018. - 480 s.
3. Sirenko S. N. Primenenie informacionnyh tehnologiy kak sredstva intensifikacii processa obucheniya v vuze / S. N. Sirenko // Otkrytoe obrazovanie. - 2009. - № 3. - S. 20-29.
4. Rabochaya programma uchebnoy discipliny «Informatika» / Cost. N. A. Shed'ko. - SPb.: PGUPS, 2023. - 27 s.
5. Endovickiy D. A. Universitetskaya nauka i obrazovanie v kontekste iskusstvennogo intellekta / D. A. Endovickiy, K. M. Gaydar // Vysshee obrazovanie v Rossii. - 2021. - T. 30. - № 6. - S. 121-131.
6. Furs S. P. Iskusstvennyy intellekt v sfere obrazovaniya - pomoschnik pedagoga ili «podryvnaya» tehnologiya? / S. P. Furs // Prepodavatel' XXI vek. - 2023. - № 1. - Chast' 1. - S. 40-49.
7. Martyshenko N. S. Issledovanie processov, vliyayuschih na udovletvorennost' studentov vyborom special'nosti / N. S. Martyshenko // Sovremennoe obrazovanie. - 2017. - № 4. - S. 131.
8. Vasil'eva E. V. Ocenka ekonomicheskoy effektivnosti konkuriruyuschih IT-proektov: podhody i matematicheskiy instrumentariy / E. V. Vasil'eva, E. A. Deeva // Upravlenie. Vyzovy i ugrozy. - 2017. - № 4(18). - S. 40-46.
