Artificial Intelligence as Tutoring Partner for Human Intellect

  • V. I. Gromyko
  • V. P. Kazaryan
  • N. S. Vasilyev
  • A. G. Simakin
  • S. S. Anosov
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 658)


System informational culture (SIC) has stated the next problem of post neo classical science man. How to educate trans disciplinary man of scienceful culture (universal professional) capable to interpret and model knowledge by means of perception of humanitarian (art) and rational science unity? Co evolution of human mind and anthropogenic environment of computer instrumental systems (IS) occurs as subjective objectization of natural science knowledge (NSK). Authors have constructed model of artificial intelligence system of universal usage (AIUU) which supports continuous tutoring with the aim to form subject of SIC. Traditional education and known e-learning approaches can’t cope with the task. Phenomenology of consciousness is grounded on development of rational part of mind. Classical universalities problem of philosophy has been resolved in the model with the help of human ability to glottogenes in semantic direction. In this way the language of categories (LC) has been discovered. AIUU functions on the principles of knowledge without premises and educated unknowledge in the form of mathesis universales of LC. It also uses the unity of human and NSK proper to inter discipline activity in the SIC. The system generates and maintains educational space of meanings in the form of personal ontological knowledge base (KBO). By means of KBO artificial intelligence adaptively helps student to investigate any field of knowledge. LC is applied to express semantic. Axiomatic method provides the unity of ideal (theory) and real (modeling in IS). Universal tutoring is stated to resolve the problem of any theory understanding by professional clearness reduction to its self evidence. By the example of the theory of algorithms study it is shown how the approach to education works.


System informational culture Super computer Third world Language of categories Functor Artificial intelligence Ontological knowledge base Cognogenes Glottogenes Universal tutoring Axiomatic method Rational objectization Self creation Semantic consciousness 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Gromyko, V.I. Kazaryan, V.P. Vasilyev, N.S. Simakin, A.G. Anosov, S.S.: Consciousness technology. J. Compl. Syst., 1(3), 46−61 (2016).Google Scholar
  2. 2.
    Vasilyev, N.S., Gromyko, V.I.: Propedevtic courses of mathematics under continuous teaching. J. Hum. Herald MGTU. 2, (28), 1-17 (2015). URL:
  3. 3.
    Vasilyev, N.S. Categorial model of probabilities theory for intellectual educational system. J. Scien. and Innov., 12, 1-18 (2013). URL:
  4. 4.
    Gromyko, V.I., Kazaryan, V.P., Vasilyev, N.S., Simakin, A.G., Anosov, S.S.: Rational education as technology of consciousness. J. Compl. Syst., 3(8), 87-108 (2013).Google Scholar
  5. 5.
    Walid Mestadi, Khalid Nafil, Raja Touahni, Rochdi Messoussi,”Knowledge Representation by Analogy for the Design of Learning and Assessment Strategies”, International Journal of Modern Education and Computer Science(IJMECS), Vol.9, No.6, pp.9-16, 2017.DOI:  10.5815/ijmecs.2017.06.02
  6. 6.
    Asoka S Karunananda, Philippe R Goldin, P D Talagala,”Examining Mindfulness in Education”, International Journal of Modern Education and Computer Science(IJMECS), Vol.8, No.12, pp.23-30, 2016.DOI:  10.5815/ijmecs.2016.12.04
  7. 7.
    Benyahia Kadda, Lehireche Ahmed,”Semantic Annotation of Pedagogic Documents”, International Journal of Modern Education and Computer Science(IJMECS), Vol.8, No.6, pp.13-19, 2016.DOI:  10.5815/ijmecs.2016.06.02
  8. 8.
    Maria Dominic,Sagayaraj Francis,”An Assessment of Popular e-Learning Systems via Felder-Silverman Model and a Comprehensivee-Learning System using the Tools on Web 2.0”, International Journal of Modern Education and Computer Science(IJMECS), vol.5, no.11, pp.1-10, 2013.DOI:  10.5815/ijmecs.2013.11.01
  9. 9.
    MacLane, S.: Categories for working mathematician. Phys. Math., Moscow (2004). 352 p.Google Scholar
  10. 10.
    Kapra, F.: Network of life. New scientific understanding of live systems. Gelios, Moscow (2002). 336p.Google Scholar
  11. 11.
    Kapra, F.: Hidden connections. Sofia, Moscow (2004). 326 p.Google Scholar
  12. 12.
    Kassirer, E.: Philosophy of symbolical forms. Language. Univ. book, Moscow, St.-Pet. 1 (2000). 272p.Google Scholar
  13. 13.
    Pinker, S.: Thinking substance. Language as window in human nature. Librokom, Moscow (2013). 560 p.Google Scholar
  14. 14.
    Pinker, S.: Language as instinct. Edit, Moscow (2004). 456 p.Google Scholar
  15. 15.
    Knjazeva, E.N., Kurdumov, S.P.: Synergy foundations. Synergic scope of life. KomKniga, Moscow (2005). 240 p.Google Scholar
  16. 16.
    Manin, Ju.I.: Mathematics as metaphor. MCNMO, Moscow (2008). 400 p.Google Scholar
  17. 17.
    Campbell, D.T.: Evolutional epistemology. In: Evolutional epistemology and logic of social sciences. Karl Popper and his critics. Moscow (2000). 464 p.Google Scholar
  18. 18.
    Popper, K.R.: Objective knowledge. Evolutional approach. URSS, Moscow (2002). 384 p.Google Scholar
  19. 19.
    Popper, K.R.: Suppositions and refutations. Scientific knowledge growth. Ermak, Moscow (2004). 638p.Google Scholar
  20. 20.
    Maltsev, A.I.: Algorithms and recursive functions. Nauka, Moscow (1986). 368p.Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Moscow Lomonosov State Univ.MoscowRussia
  2. 2.Moscow Bauman State Tech. Univ.MoscowRussia
  3. 3.Russia Peoples’ Friendship UniversityMoscowRussia
  4. 4.Public company Vozrozhdenie BankMoscowRussia

Personalised recommendations