AI-Powered Platforms for Personalized Language Learning

Abstract

The article is devoted to the analysis of modern platforms for learning foreign languages based on artificial intelligence technologies, with an emphasis on personalizing the learning process and forming individual learning trajectories. The main technological approaches and their corresponding educational platforms are considered, in particular adaptive learning algorithms, speech recognition systems, generative artificial intelligence models that ensure the adaptation of content to the needs of users. Thus, different types of platforms perform different functions within a single AI ecosystem of language learning. The empirical basis of the study was the data of Duolingo Language Reports (2020–2025), which allow you to trace global language preferences in 193 countries of the world and evaluate them in the context of digital learning. English as the first foreign language shows a stable dominant, while the choice of the second language varies significantly, which indicates a significant impact of economic, cultural and geopolitical aspects. On the basis of a two-stage cluster analysis, three main groups of countries with similar linguistic profiles are obtained: Anglo-Spanish, Anglo-globalization and Romano-Francophone cluster. A conceptual model of language clusters in an AI-adaptive learning environment, which is able to interpret It is shown that modern AI platforms are evolving from standard digital courses to intelligent ecosystems capable of predicting users' educational needs, forming personalized learning routes, and generating content in real time. The risks of implementing such systems are also considered, in particular, the issues of algorithmic bias, data privacy, and reducing the role of live communication.