The concept of a plug-in recommendation system for task personalization in the educational process

The article discusses the concept of a plug-in recommender system for assignments designed to personalize the educational process. In the context of the digital transformation of education, traditional educational platforms and learning management systems (LMS) often do not provide sufficiently flexible tools to adapt content to the individual needs of students. In response to this issue, the creation of a recommender system is proposed, which integrates with external educational modules, such as simulators, and uses the student's digital profile to analyze their educational needs. The digital profile includes academic data, behavioral patterns, and psycho-physiological indicators, enabling the system to more accurately predict the student's needs and offer appropriate assignments. The article also discusses clustering methods used to group students with similar characteristics and issues related to the system's "cold start." The described system architecture, based on modularity and scalability, allows for the flexible integration of various educational services and ensures personalized interactions with students. The developed system promises to significantly improve the efficiency of the learning process by enhancing the approach to each student's education.