Neural Networks and Artificial Intelligence in Foreign Language Teaching: A Practice-Oriented Approach

In the modern world, characterized by international cooperation, the spread of globalization, and expanding communication between representatives of different language groups, learning a foreign language has become an integral part of an individual's professional development. The growth of export-import operations and the intensive development of international relations generate a pressing need for proficiency in foreign languages, and the adaptation of Artificial Intelligence (AI) serves as a means that significantly transforms the learning process in this field. In this context, the use of AI and neural networks allows for the personalization of language learning, making it audibly and visually adaptable to the student's various proficiency levels and their psychophysiological characteristics. The relevance of this study is explained by the comprehensive application of AI in the context of acquiring various types of language competencies, which allows for a change in teaching methods within the system of educational institutions, making them more effective, accessible, and suitable for a diverse range of students. As a field focused on creating systems with the ability to perform creative tasks and automate routine processes, AI is capable of understanding natural language, transcribing it, and solving the problem of foreign language perception. This necessitates a thorough analysis of the specifics of applying neural networks and artificial intelligence in foreign language teaching. The subject of this research is the use of neural networks and artificial intelligence in the practice of foreign language teaching. The primary methods used in the research process were the dialectical method, which helped identify the features of using modern neural networks; and a systems approach, based on which the structure of AI was revealed as a set of nodes (neurons) trained on large volumes of data and predicting patterns and solutions to the most typical tasks. General methods of cognition such as analysis, synthesis, induction, and deduction were also applied, which allowed for the identification, generalization, and formulation of the main provisions of the research. Relying on specific scientific methods of cognition helped to uncover aspects related to the practice of using neural networks and artificial intelligence in foreign language teaching. Based on the results of the conducted research, we can identify the following tasks that AI can solve in the context of teaching and learning a foreign language: creating assignments based on automatically selected educational materials, and organizing and checking exams. The most in-demand tools, which are gaining particular relevance, are AI services capable of recognizing and analyzing text (chatbots, voice assistants, online translators, services for checking grammar, punctuation, and spelling). It has been proven that AIs such as Duolingo, ChatGPT, Babbel, Rosetta Stone, BERT, and Google Translate allow for the creation of a dynamic, interactive, and student-ability-adapted educational environment for the learner. Staying in this environment allows for achieving faster, more tangible results in learning a foreign language. ChatGPT stands out in this lineup of neural networks; thanks to its updates, it promotes the improvement of speaking skills not only through its built-in text format but also through the ability to act out scenarios and role-plays, simulating conversations in a restaurant, at an airport, or during a job interview. Additionally, ChatGPT, on par with a teacher, is capable of explaining grammatical rules, and the nuances of using tenses and declensions. Primarily, the application of AI is suitable for developing educational materials and workbooks, and generating tasks tailored to the learner's needs and individual characteristics. AI helps to increase motivation for learning a language, especially in the context of expanding intercultural interaction. At the same time, it is worth highlighting the disadvantages of AI, the main ones being limited contextual awareness, which prevents neural networks from fully "feeling" the communication style, the interlocutor's tone, and the authenticity inherent to native speakers. Along with this, AI lacks emotional intelligence, which in some cases makes the use of AI inappropriate, as it is unable to correctly recognize the emotions of learners.

neural networks, artificial intelligence, machine translation, digital competencies, voice assistant, speaking practice, personalized learning experience, individualization of language learning, data volume analysis, language communication