Classification and Сapabilities of AI Tools in Web Technologies Education

Abstract

Introduction. The relevance of this study stems from the large-scale digitalization and import substitution currently underway in the domestic economy, which is simultaneously confronting a number of socio-economic challenges facing the Russian Federation. These challenges intensify the demand for highly qualified specialists, exacerbated by an acute personnel shortage estimated at 30-35% (between 700,000 and 1 million individuals). Moreover, advances in generative artificial intelligence models have significantly impacted numerous sectors of society, particularly education. This has created a need not only to explore the potential applications of AI tools in web development but also to identify the methodological specifics of training future IT specialists in web technologies to directly integrate such AI tools into their professional practice. Materials and methods. The study is based on an analysis of scientific research on the characteristics of large language models (LLMs), scholarly works on the theory and methodology of teaching computer science, and professional community recommendations regarding the use of AI tools in web development (e.g., Habr, Stack Overflow). The research primarily employed descriptive, comparative, generalizing, and bibliographic analytical methods. Results. The article presents an overview of the historical development and capabilities of LLMs. It identifies the advantages and disadvantages of using AI tools based on these models in educational contexts. In particular, the paper proposes a framework for classifying AI tools according to multiple criteria. With regard to web development education, a set of AI tools has been selected and their functionalities examined. Discussion and conclusion. The findings lay the groundwork for further empirical research into the integration of AI tools in the training of future IT specialists in web technologies. The study demonstrates that, in the context of web development, IT professionals can leverage AI tools to accomplish the following tasks: explaining how code works; generating documentation for existing code; performing code reviews; and creating verification tests for developed code. Furthermore, the agent-based operational mode of certain AI tools enables the automation of various routine tasks - particularly the deployment and configuration of dependencies in framework-based projects. These capabilities can be effectively utilized by learners at different stages of their education: during theoretical instruction, practical assignments, and project-based learning.