THE CONCEPT OF AI-BASED INFORMATION SYSTEMS FOR THE ANALYSIS OF LEARNING FOREIGN WORDS

In the modern world, information systems based on artificial intelligence (AI) are increasingly used to automate learning and improve the educational process. One of the perspective areas of AI application is foreign language learning, particularly vocabulary acquisition. By integrating AI components, specifically those utilizing machine learning algorithms to analyze large volumes of data and provide automated recommendations to enhance the learning process, users gain constant access to self-assessment tools and automatic adjustment of cognitive workload.This paper examines the key role and significance of information systems for analyzing foreign language vocabulary acquisition with the help of AI. It investigates the working principles of such systems, their advantages, and various strategies used to enhance the efficiency of language learning, aiming for optimal results in acquiring new linguistic knowledge and improving learning outcomes. Learning new foreign terms is often a challenging task for many students, leading to a loss of motivation or slow progress, highlighting the urgent need for solutions that enhance material retention. Adapting to individual users, AI-based information systems have developed a range of services and platforms with global potential for language learning worldwide. These systems function by analyzing user behavior and success, based on specific indicators and metrics, whose numerical values are interpreted to identify patterns and correlations between user behavior and its impact on the system. The advantages of AI-based information systems for language learning are significant, offering an objective, reliable method for assessing learning achievements, eliminating the need for human intervention in many cases. Data collected by these systems serve as a valuable resource for analyzing user productivity, detecting common mistakes, creating effective study plans, and more. However, it's important to note that AI has not yet reached the level of understanding semantics or the cultural and historical nuances of certain words, complicating the implementation of more comprehensive functionality for evaluating and adjusting the learning process. This requires developers to prepare additional data through proprietary sources or gain useful input from user interactions with the system.