Intelligent Assistive Communication Models for Personalized Learning Environments

Abstract

There is a significant role of intelligent assistive communication as a means of providing personalised learning experiences through dynamically changing the support provided to specific learners. Nonetheless, most current learning systems are based on fixed or programmed forms of communication that are still incapable of responding to the various, and changing learner behaviours. In this paper, a proposal on an intelligent assistive communication system is presented and is motivated by a core personalization engine, which utilises the learning models grounded on data to anticipate learner conditions and provide adaptive communication plans in individualised learning setups. The offered solution incorporates state-of-the-art learning and prediction functions to simulate the pattern of interaction between learners and formulate communication needs to offer individualised and contextual help. Extensive experiments are performed based on the data of learner interaction and communication, and performance is measured against the baseline machine learning models by standard learning and prediction measures, such as accuracy, precision, recall, F1-score, and ROC-AUC measures. The findings prove that intelligent model suggested attains much higher prediction performance and better personalization performance than traditional methods. More specification will indicate the strength of the personalization engine with a heterogeneous group of learners and interaction conditions. These results establish the usefulness of smart assistive communication models to improve the quality of personalization and support of the learners, which provides a scalable and adaptive approach to personalised learning environments in the next generation.