  Interactive Intelligent Tutoring Systems (ITSs) enhance traditional ITSs by
promoting effective learning through interactions and problem resolution in
online education. Yet, proactive engagement, prioritizing resource optimization
with planning and assessment capabilities, is often overlooked in current ITS
designs. In this work, we investigate a new task, named Goal-oriented
Intelligent Tutoring Systems (GITS), which aims to enable the student's mastery
of a designated concept by strategically planning a customized sequence of
exercises and assessment. To address the problem of goal-oriented policy
learning in GITS, we propose a novel graph-based reinforcement learning
framework, named Planning-Assessment-Interaction (PAI). Specifically, we first
leverage cognitive structure information to improve state representation
learning and action selection for planning the next action, which can be either
to tutor an exercise or to assess the target concept. Further, we use a
dynamically updated cognitive diagnosis model to simulate student responses to
exercises and concepts. Three benchmark datasets across different subjects are
constructed for enabling offline academic research on GITS. Experimental
results demonstrate the effectiveness and efficiency of PAI and extensive
analyses of various types of students are conducted to showcase the challenges
in this task.

Computers and Society;Artificial Intelligence;Information Retrieval