Use of generative artificial intelligence software in the process of introductory programming training at university
Michael Dolinsky
Ph.D, docent, docent of the chair “Department of Mathematical Problems of Control and Informatics” Gomel State University named after F. Skorina, Gomel, Belarus, Sovietskaya str. 104, 246023, Gomel
DOI: https://doi.org/ifr.v2i2.8881
Keywords: generative artificial intelligence; programming training for freshmen; DL.GSU.BY
Abstract
This paper provides a review of the literature on introductory programming training in universities and the use of generative artificial intelligence software (GAIS) for these purposes. During introductory programming training in universities, the tasks of developing five skills are highlighted: abstraction (determining what information about an entity/object needs to be preserved and what to ignore); generalization (formulating a solution in general terms so that it can be applied to solve various problems); decomposition (breaking complex problems into smaller subtasks that are easier to understand and solve); algorithmization (determining step by step a set of operations/actions in order to solve a problem); debugging (localization and elimination of errors). There are also many programming languages used in introductory education at universities, such as Pascal, C , Python, C#, Java. A review of the literature on the use of GAIS in teaching of programming at universities indicated the following main problems and tasks: to prevent the use of GAIS to obtain undeserved grades in the subject; develop techniques and examples of using GAIS in the educational process to personalize, increase intensity and quality; explore new areas of using GAIS, for example, developing skills in using GAIS to solve real production problems (GAIS operator). A description is given of the author's experience of using GAIS for introductory programming training for first-year students of the Faculty of Mathematics and Programming Technologies of Gomel State University named after F. Skorina. The main futures of authors approach are the following: automatic personalized training, low entry threshold, multiple entry points, personalization, intensification, freedom to choose a program development language (in current practice between five programming languages: Pascal, Python, Java, C#, C . If the need arises, new languages can be added).
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