Software package for assessing user satisfaction with artificial intelligence responses in biological, psychological, and social level tests

DOI: 10.21293/1818-0442-2025-28-2-111-115

Download article in PDF format

JATS xml

Abstract: A software variant is presented that makes it possible to evaluate the level of user satisfaction with artificial intelligence responses to biological, psychological and social test questions. The level of satisfaction is assessed based on the similarity of perceived AI response to one of five statements. Initial test results have been obtained, demonstrating the prospects for using this software package.

Keywords: verification of neural networks, interaction between humans and artificial intelligence, psychological testing, satisfaction level

For citation:
Artemov I. L., Davydov A. A., Loboda Yu. O., Tamoshkin M. A., Shipulya A. D., Shipulya M. A. Software package for assessing user satisfaction with artificial intelligence responses in biological, psychological, and social level tests. Doklady Tomskogo gosudarstvennogo universiteta sistem upravleniya i radioelektroniki, 2025, vol. 28, no. 2, pp. 111–115. DOI: 10.21293/1818-0442-2025-28-2-111-115

Authors and copyright holders:

  • Artemov I. L. , Tomsk State University of Control Systems and Radioelectronics (Tomsk, Russia)
  • Davydov A. A. , Siberian State Medical University (Tomsk, Russia)
  • Loboda Yu. O. , Tomsk State University of Control Systems and Radioelectronics (Tomsk, Russia)
  • Tamoshkin M. A. , Tomsk Polytechnic University (Tomsk, Russia)
  • Shipulya A. D. , Siberian State Medical University (Tomsk, Russia)
  • Shipulya M. A. , Tomsk State University of Control Systems and Radioelectronics (Tomsk, Russia)

  • 1. Brown T.B., Mann B., Ryder N. Language Models are Few-Shot Learners. arXiv preprint, 2020, arXiv:2005.14165. Available at: https://arxiv.org/abs/2005.14165 (Аccessed: 01.06.2025).
  • 2. Vaswani A., Shazeer N., Parmar N. Attention is All You Need. Advances in Neural Information Processing Systems, 2017, vol. 30.
  • 3. Goertzel B. Artificial General Intelligence: Concept, State of the Art, and Future Prospects. Journal of Artificial General Intelligence, 2014, vol. 5, no. 1, pp. 1–48.
  • 4. Segal J.I., Smith M., Johnson K. [et al.] A multi-scale cognitive interaction model of instrument operations at the Linac Coherent Light Source. Rev. Sci. Instrum., 2025, vol. 96, pp. 013005.
  • 5. Mitchell M., Krakauer D.C. The Debate Over Understanding in AI’s Large Language Models. arXiv preprint, 2020, arXiv:2210.13966v3. Available at: https://arxiv.org/abs/2210.13966v3 (Аccessed: 01.06.2025).
  • 6. Karelov S.V. Lovushka Gudharta dlya AGI: problema sravnitel'nogo analiza iskusstvennogo intellekta i intellekta cheloveka [The Goodhart’s Trap for AGI: The Problem of Comparative Analysis of Artificial Intelligence and Human Intelligence]. Uchenye zapiski Instituta psikhologii Rossiyskoy akademii nauk, 2023, vol. 3, no. 3, pp. 5–22 (in Russ.).
  • 7. Legg S., Hutter M. A Collection of Definitions of Intelligence. Frontiers in Artificial Intelligence and Applications, 2007, vol. 157, pp. 17–24.
  • 8. Turing A.M. Computing Machinery and Intelligence. Mind, 1950, vol. 59, no. 236, pp. 433–460.
  • 9. Marcus G. The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence. arXiv preprint, 2020, arXiv:2002.06177. Available at: https://arxiv.org/abs/2002.06177 (Аccessed: 01.06.2025).
  • 10. Bostrom N. Superintelligence: Paths, Dangers, Strategies. Oxford, Oxford University Press, 2014.
  • 11. Chollet F. On the Measure of Intelligence. arXiv preprint, 2019, arXiv:1911.01547. Available at: https://arxiv.org/abs/1911.01547 (Аccessed: 01.06.2025).
  • 12. Kahneman D. Thinking, Fast and Slow. New York: Farrar, Straus and Giroux, 2011.
  • 13. Troelsen A., Japikse P. Pro C# 9 with .NET 5: Foundational Principles and Practices in Programming. New York, Apress, 2021.
  • 14. Richter J. CLR via C#. Redmond, Microsoft Press, 2010.
  • 15. .NET Documentation. Introduction to .NET. Available at: https://docs.microsoft.com/en-us/dotnet/core/introduction (Аccessed: 01.06.2025).
  • 16. Microsoft Docs. Windows Forms Overview. Available at: https://docs.microsoft.com/en-us/dotnet/desktop/winforms/overview/ (Аccessed: 01.06.2025).
  • 17. Gigachat – Russian-language neural network from Sberbank Available at: https://giga.chat (Аccessed: 01.06.2025) (in Russ.).
  • 18. ChatGPT|OpenAI Available at: https://openai.com/chatgpt/overview (Аccessed: 01.06.2025) (in Russ.).
  • 19. AI-assistant for solving any tasks Available at: https://alice.yandex.ru, (accessed 01.06.2025) (in Russ.).
  • 20. Eysenck Personality Inventory, EPI. Available at: https://psytests.org/eysenck/epiAS-run.html (Аccessed: 01.06.2025) (in Russ.).
  • 21. Optimism and Activity Scale, AOS. Available at: https://psytests.org/typo/aos.html (Аccessed: 01.06.2025) (in Russ.).
  • 22. Schmieschek Questionnaire (Classic). Available at: https://psytests.org/accent/shmi88a-run.html (Аccessed: 01.06.2025) (in Russ.).
  • 23. Personal Dynamism Scale. Available at: https://psytests.org/emvol/sld.html (Аccessed: 01.06.2025) (in Russ.).
  • 24. Toronto Alexithymia Scale, TAS-26. Available at: https://psytests.org/diag/tas26-run.html (Аccessed: 01.06.2025) (in Russ.).
Editorial office address

Executive Secretary of the Editor’s Office

 Editor’s Office: 40 Lenina Prospect, Tomsk, 634050, Russia

  Phone / Fax: + 7 (3822) 701-582

  journal@tusur.ru

 

Viktor N. Maslennikov

Executive Secretary of the Editor’s Office

 Editor’s Office: 40 Lenina Prospect, Tomsk, 634050, Russia

  Phone / Fax: + 7 (3822) 51-21-21 / 51-43-02

Subscription for updates