2024, no. 10
DIGITALIZATION OF EDUCATION

Perspectives for the Use of AI Systems in Programming Teaching

Maksim V. SergievskyORCID, Aleksey I. VinokurORCID

DOI 10.20339/am.10-24.069pp. 69–75UDC 37.09+004.880 RUB

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Abstract

It is safe to say that, whatever the attitude to artificial intelligence (AI) technology, it will be in demand in the learning process in the near future. This article evaluates the potential of using AI systems, in particular neural networks of the GPT class, in teaching programming in algorithmic languages. The authors conducted a study in which three AI systems — ChatGPT-4, Gemini Ultra and GigaChat — were used to solve a number of model tasks in order to generate code in Python and Scala. The results of the AI systems were evaluated according to criteria such as running time, code efficiency, and comment quality. The study has shown that, despite the generally optimistic attitude towards the use of AI in programming education, it is necessary to maintain caution and a reasonable balance between classical teaching methods and artificial intelligence systems.

References

  1. Elsen-Rooney, M. NYC education department blocks ChatGPT on school devices, networks. Chalkbeat. 2023, Jan. 4. URL: https://ny.chalkbeat.org/2023/1/3/23537987/nyc-schools-ban-chatgpt-writing-artificial-intelligence

    ny.chalkbeat.org
  2. Sazonov, A.P. Use of AI in programming. Universum: Technical Sciences. 2024. V. 3 (120) [Electronic resource]. DOI: 10.32743/UniTech.2024.120.3.17010, URL: https://7universum.com/ru/tech/archive/item/17010

    DOI 10.32743/unitech.2024.120.3.170107universum.com
  3. Marcus, G., Davis, E. Artificial Intelligence: Rebooting. How to create a machine intelligence that can really be trusted (Biblioteka Sbera: Artificial Intelligence). Moscow: Publ. House Intellectual Literature, 2021. 328 p. URL: https://sberuniversity.ru/research/biblio/10432/ (accessed on: 18.06.2024).

    sberuniversity.ru
  4. Lapan, М. Deep Reinforcement Learning Hands-On. eBook. Published by Packt, 2018.

  5. Sergievsky, G., Sergievsky, M. Conception and Linguistic Means of Representation and Knowledge Processing at the Semantic Level. Automatic Documentation and Mathematical Linguistics. 2023. V. 57. Nо. 2. P. 127–133.

  6. Zaytsev K.S., Sergievsky, M.V. Using foreign experience in the preparation of master’s degree programs in IT. Alma Mater (Vestnik vysshey shkoly). 2014. No. 5. P. 62–67.

  7. Farley, D. Modern Software Engineering: Doing What Works to Build Better Software Faster. Addison-Wesley, 2021. 256 p. ISBN 978-0137314911 Open Library OL34779880M

  8. Eltarenko, E.A.; Sergievsky, M.V. Evaluation of hardware and pro-software by a multilevel system of criteria. Computer Press. 1998. No. 8. P. 268–272.

  9. The future of AI-assisted programming — first examples. 2023. [Electronic resource]. URL: https://habr.com/ru/companies/timeweb/articles/745442/

    habr.com
  10. Writing Code with AI. [Электронный ресурс]. URL: https://docs.superblocks.com/generative-ai/writing-code-with-ai

    docs.superblocks.com