ISSN 2071-8594

Russian academy of sciences


Gennady Osipov

R. V. Dushkin Development of adaptive teaching methods using intelligent agents


The paper describes the options for the use of intelligent agents as teacher assistants in the organization of adaptive learning processes in online messengers. This allows to reduce the burden on the teacher in a dramatic way when communicating with students and answering typical and sometimes very unusual questions from course participants. A brief description of the educational process model, including the student model, is provided. It also describes the generalized architecture of an intelligent agent along with a brief presentation of the subprocess of its interaction with various actors in the framework of the educational process. The conclusion provides a brief statistical summary of the training conducted using the described technology, as well as a list of next steps to improve the technology presented in the paper.


adaptive learning, education intellectual system, intellectual agent, cognitive technology, natural language processing.

PP. 87-96.

DOI 10.14357/20718594190108


1. Durnev A. Kak iskusstvenny intellekt pronikayet v fintech: ot chat-botov k personal’nym assisentam [How artificial intelligence penetrates fintech: from chat bots to personal assistants] // Forbes — Technologies / Fintech, 14 of May 2017 — Online resource (accessed: 19.02.2019).
2. Kretsu K. Iskusstvenny intellekt v bisnese — opyt rossiyskikh brendov [Artificial intelligence in business — the experience of Russian brands]//, 16 of August 2017 — Online resource (accessed: 19.02.2019).
3. Dushkin R. V., Zharkov A. D., Ivanov D. A. K sovremennomu ponimaniyu ITS [To modern understanding of ITS] // Nizhny Novgorod: IT Forum 2020. «The development of a digital state. Creation of systems of weight and dimensional control, intelligent transport systems». — 12-14 of April 2017.
4. Kausar M. Sustainability and Industry 4.0. — Logictics and inventory control, November 2018.
5. Aldewereld H., Boissier O., Dignum V., Noriega P., Padget J. A. Social Coordination Frameworks for Social Technical Systems. — Springer. — August, 2016.
6. Dushkin R. V. Mesto ITS v infrastructure Umnogo goroda [ITS place in the infrastructure of the Smart City] // Mir dorog, № 110, August 2018.
7. Raskin J. The human interface: new directions for designing interactive systems (1. printing. ed.). Reading, Mass. [u.a.]: Addison Wesley.
8. Udell J. Interfaces are habit-forming. — Infoworld. Archived from the original on 4 April 2017.
9. Dushkin R. V. 5 professiy, kotorye zamenyat chat-boty I iskusstvenny intellekt v blizhayshiye 5 — 10 let [5 professions that will be replaced by chat bots and artificial intelligence in the next 5 — 10 years] //, 27 of August 2016. — — Online resource (accessed: 19.02.2019).
10. Mauldin M. ChatterBots, TinyMuds, and the Turing Test: Entering the Loebner Prize Competition // Proceedings of the Eleventh National Conference on Artificial Intelligence, AAAI Press.
11. Wolpaw J. R., Birbaumer N., Heetderks W. J., McFarland D. J., Peckham P. H., Schalk G., Donchin E., Quatrano L.A., Robinson C. J., Vaughan T. M. Brain-Computer Interface Technology: A Review of the First International Meeting, IEEE Transactions on Rehabilitation Engineering, Vol. 8, No. 2, June 2000, 164-173.
12. Yemelyanov V. V., Kureichik V. V., Kureichik V. M. Teoriya I praktika evolutsionnogo modelerovaniya [Theory and practice of evolutionary modeling]. — Moscow: Physmatlit, 2003. — 432 p.
13. Kasabov N. Introduction: Hybrid intelligent adaptive systems // International Journal of Intelligent Systems, Vol. 6 (1998), 453-454.
14. Krafzig D., Banke K., Slama D. Enterprise SOA. Prentice Hall, 2005.
15. Teleschool: — retrieved in February 2019.
16. Morifuji D. Connectionist Approach to Stage-Like Syntactic Development / Daichi Morifuji, Toshio Innui // Word processing and cognitive technology: Digest of articles. № 11 / Eds: V. Solovyov, V. Goldberg, V. Polyakov — Kazan: Kazan State University, 2006. — p. 41-50.
17. Dushkin R. V. Metody polucheniya, predstavleniya I obrabotki znaniy s NE-faktorami [Methods for obtaining, representing and processing knowledge with NONfactors]. — 2011. — 115 p.
18. Blasiak S., Rangwala H. A Hidden Markov Model Variant for Sequence Classification. IJCAI Proceedings-International Joint Conference on Artificial Intelligence, 2001. 22:1192.
19. Aurélien G. Prikladnoye mashinnoye obucheniye s pomoshchyu Scikit-Learn I TensorFlow. Kontseptsii, instrumenty I tekhniki dlya sozdaniya intellektual’nykh system [Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques for Building Intelligent Systems]. — Williams, 2018. — 688 p.
20. Wierema S. Build your own Siri: offers voice integration for all. The Next Web.
21. Dushkin R. V. Pochemu za gibridnymi II-sistemami budushcheye [Why the hybrid AI-systems will take the future] // Economic strategies, № 6 (156), 2018. — p. 84-93.