The relevance of using neural networks in learning foreign languages

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Relevance. In recent years, technologies related to the introduction of artificial intelligence in various spheres of human life have been actively developing. The learning process, in particular, the study of foreign languages, was no exception. Neural networks are very popular among students, which is why it is important to evaluate their effectiveness and prospects for use in the educational process.
Introduction. Learning foreign languages has always been a difficult and time-consuming process, requiring considerable effort and time. Traditional methods, including textbooks, courses, and communication with native speakers, are effective, but they have their limitations. In recent years, artificial intelligence (AI) has become an integral part of the educational process, especially in the context of higher education institutions. This is due to the need to adapt educational methods to modern requirements and interests of students, including the practice of learning foreign languages [1].
Goal. To analyze the relevance of using neural networks and the possibility of using artificial intelligence in learning foreign languages in higher educational institutions.
Materials and methods. In the course of this work, research methods were used: analysis and synthesis, comparison and generalization of scientific literature on the research topic.
Results. First of all, it is necessary to consider what is meant by artificial intelligence. Artificial intelligence (AI) is a field of computer science that focuses on creating systems and programs capable of performing tasks that normally require human intelligence. These tasks include perception, natural language understanding, learning, decision making, problem solving, and pattern recognition. AI can be classified into narrow (or specialized) intelligence, which performs specific tasks, and general intelligence, which seeks to mimic human abilities in various fields. The main goal of AI is to create machines capable of self—learning and adaptation, which allows them to improve their functions and conclusions based on the experience gained [2].
The topic of the use of artificial intelligence in education has become an object of research and discussion in the early stages of the development of AI technologies. The first steps in using AI for educational purposes were taken in the middle of the last century. In 1956, John McCarthy coined the term "artificial intelligence", defining it as "the ability of intelligent systems to perform creative functions that are usually considered the prerogative of humans." In the USSR, AI research began in the 1960s at Moscow State University and the Academy of Sciences. Scientists were already aware of the potential of computers and learning software. However, the first experiments have shown that the development of computer programs for educational purposes is a rather difficult task, requiring significant time and resource costs.
In the following decades, research into the use of AI in education continued, but progress was uneven. In the 1990s, the first educational computer games using AI elements for learning began to appear. However, at that time computers were bulky and expensive, which limited the mass use of these technologies. In recent years, thanks to the development of digital technologies and deep learning, AI in education has become much more accessible and effective [2].
Let's consider the main aspects justifying the relevance of the use of AI in the educational field.
1. Personalization of training
One of the key functions of AI is the ability to personalize learning. AI-based systems can analyze data about students, their successes and preferences, which allows you to create customized curricula. This is especially important in learning foreign languages, where the level of preparation and the speed of learning the material can vary significantly between students. A personalized approach promotes deeper understanding of the language and increases student motivation.
2. Adaptation to individual requests
Artificial intelligence is able to adapt the learning process to the individual needs and interests of students. For example, language bots and online platforms can offer assignments and materials that match students' interests, making the learning process more fun and effective. It also allows students to study at a pace that is convenient for them, which is especially important for learning complex languages [3].
3. Increasing the effectiveness of training
The use of AI technologies such as language bots, machine translation systems and online tests significantly increases the effectiveness of teaching foreign languages. These tools can provide instant feedback, allowing students to quickly correct mistakes and improve their skills. For example, language bots can conduct dialogues in a foreign language, which helps students practice conversational skills in real time.
Neural networks transform language learning at several levels: from automated grammar and vocabulary verification to the creation of virtual interlocutors and personalized training programs [1].
The analysis of the literature has allowed us to obtain the following results of the use of AI technologies in the process of higher education:
1. Automated verification and correction:
Neural network technologies have significantly improved the tools for checking grammar and spelling. Programs based on neural networks are able not only to identify errors, but also to offer correct options, explaining the essence of the problem. This allows the student to quickly correct mistakes and improve their writing skills. Moreover, some programs analyze writing style and offer recommendations for improving vocabulary and sentence structure. This is especially useful for exam preparation or writing professional texts in a foreign language.
2. Translation and contextual analysis:
Neural network translators such as Google Translate or DeepL are becoming more accurate and effective. They can be useful for quick understanding of a text in a foreign language, but it is important to remember that automatic translation is not always perfect and requires critical analysis. Nevertheless, the use of such translators in combination with other teaching methods can significantly facilitate the learning process. Moreover, some neural networks are able to analyze the context and suggest the most appropriate translation options depending on the situation [4].
3. Generation of personalized learning materials:
Neural networks allow you to create personalized curricula and materials adapted to the individual level and needs of the student. They analyze the student's progress and select tasks of appropriate complexity, focusing on weaknesses. This allows you to optimize the learning process and achieve better results in a shorter time. For example, a neural network can generate exercises based on words and grammatical constructions that the student has learned the worst.
4. Virtual interlocutors and communication simulators:
The development of technology has made it possible to create virtual interlocutors capable of maintaining a dialogue in a foreign language. This allows you to practice speaking at any time and in any place, without needing a real interlocutor. Virtual interlocutors can adapt to the user's level and adjust their responses depending on the context. In addition, some programs simulate real communication situations, such as ordering food at a restaurant or buying train tickets. It helps to overcome the language barrier and prepare for real communication.
5. Speech recognition and speech synthesis:
Neural networks have the ability to recognize and synthesize speech, which opens up new opportunities for studying pronunciation and listening. Programs can analyze the user's pronunciation and give feedback, pointing out errors and suggesting ways to correct them. This is especially important for improving pronunciation and accent skills. Speech synthesis can be useful for listening to audio materials, repeating words and phrases, and creating personalized audio tutorials.
6. Interactive games and applications:
Many language learning applications use neural network technologies to create interactive games and exercises that make the learning process more fun and effective [5]. Games can be tailored to the user's level and focus on certain aspects of the language, such as grammar or vocabulary. This allows you to make the learning process more interesting and motivating.
Despite its many advantages, the use of neural networks in language learning has its limitations. The quality of training largely depends on the quality of the data used to train the neural network. Some neural networks may have difficulty understanding the context, especially in complex or ambiguous situations [1]. In addition, it is important to remember that neural networks are just a tool, and they cannot completely replace human communication and interaction with native speakers. There is also a danger of students becoming dependent on technology, which can lead to a decrease in critical thinking and independence.
Even the most powerful and seemingly effective technological systems are unlikely to be able to completely replace traditional forms and methods of teaching. Taking into account the above, there is a need to develop combined approaches to learning. In most educational institutions, teaching subjects boils down to attempts to combine proven classical teaching methods with modern multimedia tools. This may include using AI to automate routine tasks such as checking tests, while teachers can focus on more complex aspects of learning such as developing students' critical thinking and creativity. Conclusion. Neural networks significantly expand the possibilities of language learning by offering new tools and methods to accelerate and optimize the process of learning foreign languages. They allow you to create personalized training programs, automate knowledge testing, and practice communication. However, it is also important to consider the potential risks and challenges associated with using AI and strive to create a balanced approach that combines the best practices of both traditional and innovative learning. The future of language learning is closely linked to the development of artificial intelligence, and we can expect even more advanced and effective tools in the near future. The key remains the balance between technological progress and human interaction in the process of language acquisition.

About the authors

Tatyana Sergeevna Smelyanskaya

Voronezh State Medical University named after N. N. Burdenko

Email: tanechka5145@gmail.com
ORCID iD: 0000-0002-6972-1979

student

Russian Federation, 10 Studencheskaya str., Voronezh, 394036, Russia

Victoria Borisovna Shekhovtsova

Voronezh State Medical University named after N. N. Burdenko

Author for correspondence.
Email: wika21713@gmail.com
ORCID iD: 0000-0002-4192-5515

student

Russian Federation, 10 Studencheskaya str., Voronezh, 394036, Russia

Diana Anatolievna Povalyukhina

Voronezh State Medical University named after N. N. Burdenko

Email: pov-diana@yandex.ru
ORCID iD: 0000-0002-7308-1396
SPIN-code: 6058-7007

Senior Lecturer at the Department of Foreign Languages Department

Russian Federation, 10 Studencheskaya str., Voronezh, 394036, Russia

Polina Andreyevna Tikhonova

Voronezh State Medical University named after N. N. Burdenko

Email: polinka.7.2003@yandex.ru
ORCID iD: 0000-0003-0307-5887

student

Russian Federation, 10 Studencheskaya str., Voronezh, 394036, Russia

References

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