Adaptive Intelligent Tutoring System

The goal of the work is to create a modern adaptive intelligent system using current machine learning technologies to automate a significant part of the teacher’s work. Existing intelligent systems, the purpose of which is to train students to work in various subject areas, currently have a set of various disadvantages, for example, the need to prepare educational material in a given format, which is sometimes a very labor-intensive task. In addition, in such systems there is a need to assess knowledge to correct the training plan for students, which requires various practical tasks for their formal presentation. In this case, practical assignments must be compiled by the course author, which can also be very labor-intensive. The novelty of the adaptive intelligent system presented in the work lies in the improvement of learning approaches using the latest machine learning methods. To help the teacher prepare educational material provides the ability to create video material automatically. This approach provides an opportunity for students to receive material not only in text form, but also in video format, without increasing the labor intensity on the part of the teacher. In addition, the teacher will be given the opportunity to manipulate versions of educational materials in accordance with the statistics provided by the system on student performance.

1. Avanskij S. M., Zatylkin A. V., Jurkov N. K. Predstavlenie modeli pol'zovatelja i predmetnoj sredy obuchenija //Trudy Mezhdunarodnogo simpoziuma «Nadezhnost' i kachestvo», 2007. Vol. 1, pp. 66-67.
2. Alesheva L. N. Intellektual'nye obuchajushhie sistemy. Vestnik universiteta, 2018.  No. 1, pp. 149-155.
3. Intellektual'noe upravlenie processom obuchenija. Analiz i proektirovanie sistem. Available at: https://habrahabr.ru/post/194240/ (Accessed: 20.11.2023).
4. Petrushin V. A. Jekspertno-obuchajushhie sistemy. K.: Nauk. Dumka,1992.
5. Jurkov N. K. Intellektual'nye komp'juternye obuchajushhie sistemy //Penza: Publ. PGU, 2010.
6. Alkhatlan A., Kalita J. Intelligent tutoring systems: A comprehensive historical survey with recent developments. arXiv preprint arXiv:1812.09628, 2018.
7. Geneva D. et al. Accentor: An Explicit Lexical Stress Model for TTS Systems.
8. How intelligent tutoring systems are changing education. Available at: https://medium.com/@roybirobot/how-intelligent-tutoring-systems-are-changing-education-d60327e54dfb (Accessed: 10.06.2023).
9. Keleş A. et al. ZOSMAT: Web-based intelligent tutoring system for teaching–learning process. Expert Systems with Applications, 2009. Vol. 36, no. 2, pp. 1229-1239.
10. Lecture Automator. Available at: https://github.com/CapBlood/lecture-automator (Accessed: 30.05.2023).
11. Pardos Z. A. et al. Oatutor: An open-source adaptive tutoring system and curated content library for learning sciences research //Proceedings of the 2023 chi conference on human factors in computing systems, 2023. pp. 1-17.
12. Silero Models. Available at: https://github.com/snakers4/silero-models (Accessed: 10.06.2023).
13. Shen J. et al. Natural tts synthesis by conditioning wavenet on mel spectrogram predictions //2018 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, 2018. pp. 4779-4783.
14. Thesis-ITS. Available at: https://github.com/robertoguazon/Thesis-ITS/wiki (Accessed: 10.06.2023).