Towards Goal-oriented Intelligent Tutoring Systems in Online Education

Interactive Intelligent Tutoring Systems (ITSs) enhance the learning experience in online education by fostering effective learning through interactive problem-solving. However, many current ITS models do not fully incorporate proactive engagement strategies that optimize educational resources through thoughtful planning and assessment. In this work, we propose a novel and practical task of Goal-oriented Intelligent Tutoring Systems (GITS), designed to help students achieve proficiency in specific concepts through a tailored sequence of exercises and evaluations. We introduce a novel graph-based reinforcement learning framework, named Planning-Assessment-Interaction ( PAI ), to tackle the challenges of goal-oriented policy learning within GITS. This framework utilizes cognitive structure information to refine state representation and guide the selection of subsequent actions, whether that involves presenting an exercise or conducting an assessment. Additionally, PAI employs a cognitive diagnosis model that dynamically updates to predict student reactions to exercises and assessments. We construct three benchmark datasets covering different subjects to facilitate offline GITS research. Experimental results validate PAI ’s effectiveness and efficiency, and we present comprehensive analyses of its performance with different student types, highlighting the unique challenges presented by this task.