Learning from Long-Term Engagement: Adaptive Tutoring Dialogue Planning for Personalized Education
Zhiang Dong, Zhenlong Dai, Xiangwei Lv, Jingyuan Chen
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
BibTeX
@inproceedings{aaai2026_learningfromlong,
title = {Learning from Long-Term Engagement: Adaptive Tutoring Dialogue Planning for Personalized Education},
author = {Zhiang Dong and Zhenlong Dai and Xiangwei Lv and Jingyuan Chen},
booktitle = {AAAI 2026},
year = {2026}
}