Towards Goal-oriented Intelligent Tutoring Systems in Online Education
This paper proposes Goal-oriented Intelligent Tutoring Systems (GITS) using a graph-based reinforcement learning framework (PAI) that plans exercise sequences and assessments to help students master specific concepts, and introduces three benchmark datasets covering different subjects for offline GITS research.
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 specif