Intelligent learning systems for personalizing and adapting language courses

Abstract

Integration of artificial intelligence (AI) into learning takes place at various levels of education. This process includes the design of multimodal didactic material, development of language skills, control and assessment, mining and analysis of educational data, prediction of learning outcomes. It is important not to skip any of these steps so that AI is implemented not only for providing access to information and the execution of routine test tasks, but also for contributing of real transformation in education. In teaching foreign languages today, various technical solutions based on AI are used, such as voice assistants; smart tutors; social and educational bots; automatic systems for assessment and editing of written texts; recommendation systems; intelligent foreign language teaching systems, etc. The objective of this article is to consider innovative approaches to language teaching based on using intelligent learning systems, as well as to analyze the design features of these systems. The concept of personalized learning, which is about adapting the organization, content and pace of learning to the individual needs of students, plays a key role in the design and development of intelligent learning systems. Intelligent learning systems and smart tutors can automatically adapt and create personalized educational material for various disciplines, taking into account the target audience, level of foreign language proficiency, professional and educational goals, intended learning outcomes through multimodal clustering and recommendation systems, automatic text processing and image recognition. The three main components underlying such systems include a domain model, a learner model, and a methodological model. Effective teaching approaches, such as providing feedback, assessment, reflection and recommendations for students, are important components of intelligent learning systems

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