In recent years, the demand for intelligent and interactive online education systems has grown significantly, driven by the need for personalized learning experiences and effective student engagement. This study proposes a novel approach that integrates the Dwarf Mongoose Optimization (DMO) algorithm with a Gated Recurrent Unit (GRU) neural network to develop an AI-powered interactive online learning model. The proposed DMO-GRU framework leverages the optimization capability of DMO for feature selection and parameter tuning, while GRU effectively captures temporal learning patterns in student data. A comprehensive literature review was conducted using databases such as IEEE Xplore, ACM, and Google Scholar, focusing on studies from the last decade. The model was evaluated through experimental analysis using key regression and classification metrics, including Mean Absolute Error, Root Mean Square Error, R2 Score, Accuracy, Precision, Recall, Sensitivity, Specificity, and F1-Score with training time. Results indicate that the DMO-GRU outperforms traditional models such as Linear Regression, Random Forest, SVR, and XGBoost, offering higher prediction accuracy and better identification of student learning outcomes. The system also supports interactive modes such as audio, video, and one-to-one sessions, contributing to improved learner engagement. This study demonstrates the potential of AI-driven optimization techniques to enhance the effectiveness, adaptability, and personalization of online education platforms.
Li et al. (Fri,) studied this question.