Graph neural network based intelligent tutoring system: A survey

Abstract

Online education is developing rapidly driven by artificial intelligence technology. The massive learning resources lead to information overload and low resource utilization. Intelligent tutoring system (ITS) plays a vital role in the education platform, providing personalized learning services for students. The data obtained from the online education platform has complex correlations, which can be potentially transformed into multi-level graph structures. In recent years, graph neural networks (GNNs) have been tried to be introduced into intelligent learning services due to their superior performance in processing graph-structured data. This paper aims to provide researchers and engineers with a general overview of modeling processes and techniques for intelligent learning services based on GNNs. Through a careful review of the advanced models published between 2019 and 2023, existing research primarily focuses on four detailed areas within the smart services scenario. The GNN models involved are systematically classified, and the principles, pioneers and variants of various models are summarized in detail. Simultaneously, this paper analyzes the applications, the specific problems to be solved, and the technologies and innovations of graph-based models in the four key areas. In addition, we examine the commonly used datasets and evaluation metrics in the field of education. Finally, the current challenges and future development trends are summarized to provide comprehensive and in-depth guidance for research in related fields.