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Proceedings of the AAAI Conference on Artificial Intelligence

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Learning from Long-Term Engagement: Adaptive Tutoring Dia...
Zhiang Dong, · 2026-03-14 · via Proceedings of the AAAI Conference on Artificial Intelligence

Authors

  • Zhiang Dong Zhejiang University
  • Zhenlong Dai Zhejiang University
  • Xiangwei Lv Zhejiang University
  • Jingyuan Chen Zhejiang University

DOI:

https://doi.org/10.1609/aaai.v40i1.36984

Abstract

With the advancements of large language models (LLMs), intelligent tutoring systems have witnessed significant progress. The extensive knowledge and reasoning capabilities of LLMs enable intelligent tutoring systems to generate more helpful tutoring dialogues with scaffolding instructions. However, these systems fail to provide scaffolds that align with the personalized needs of students due to the lack of attention to the long-term learning process of students. Meanwhile, the pursuit of more suitable scaffolds through complex reasoning may result in additional computational overhead. To address these issues, we propose LEAP, a Long-term Educational Adaptive Planning system that can model students' long-term learning process. Specifically, LEAP plans for scaffolds through collaboration of direct planning and thoughtful reasoning to improve efficiency and captures students' long-term learning progress through cognitive state extraction. Then we propose LEAD, a Long-term Educational Archive Dataset to alleviate the lack of data and validate the effectiveness of LEAP, which is constructed through real-world students' reactions and simulation of the teacher-student interactions. Experiments on several datasets demonstrate the effectiveness of LEAP.

How to Cite

Dong, Z., Dai, Z., Lv, X., & Chen, J. (2026). Learning from Long-Term Engagement: Adaptive Tutoring Dialogue Planning for Personalized Education. Proceedings of the AAAI Conference on Artificial Intelligence, 40(1), 237–245. https://doi.org/10.1609/aaai.v40i1.36984

Issue

Section

AAAI Technical Track on Application Domains I

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