Publication Type

Journal Article

Version

publishedVersion

Publication Date

11-2025

Abstract

Interactive Intelligent Tutoring Systems (ITSs) enhance the learning experience in online education byfostering effective learning through interactive problem-solving. However, many current ITS models do notfully incorporate proactive engagement strategies that optimize educational resources through thoughtfulplanning and assessment. In this work, we propose a novel and practical task of Goal-oriented IntelligentTutoring Systems (GITS), designed to help students achieve proficiency in specific concepts through a tailoredsequence 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 learningwithin GITS. This framework utilizes cognitive structure information to refine state representation andguide the selection of subsequent actions, whether that involves presenting an exercise or conducting anassessment. Additionally, PAI employs a cognitive diagnosis model that dynamically updates to predict studentreactions to exercises and assessments. We construct three benchmark datasets covering different subjects tofacilitate offline GITS research. Experimental results validate PAI’s effectiveness and efficiency, and we presentcomprehensive analyses of its performance with different student types, highlighting the unique challengespresented by this task.

Keywords

Intelligent Tutoring System, Adaptive Learning, Reinforcement Learning

Discipline

Artificial Intelligence and Robotics | Instructional Media Design

Research Areas

Intelligent Systems and Optimization

Publication

ACM Transactions on Information Systems

Volume

43

Issue

6

First Page

1

Last Page

26

ISSN

1046-8188

Identifier

10.1145/3760401

Publisher

Association for Computing Machinery (ACM)

Copyright Owner and License

Authors-CC-BY

Creative Commons License

Creative Commons Attribution 3.0 License
This work is licensed under a Creative Commons Attribution 3.0 License.

Additional URL

https://doi.org/10.1145/3760401

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