arXiv AI
Aug 26

UCO: A Multi-Turn Interactive Reinforcement Learning Method for Adaptive Teaching with Large Language Models

The paper introduces UCO, a multi‑turn interactive reinforcement learning method designed to improve adaptive teaching with large language models. UCO employs two reward functions—Progress Reward to gauge genuine cognitive advancement and Scaffold Reward to keep instruction within each student’s Zone of Proximal Development. Experiments on BigMath and MathTutorBench show UCO outperforming 11 baseline models and matching advanced closed‑source systems.

By Shouang Wei, Min Zhang, Xin Lin, Bo Jiang, Kun Kuang, Zhongxiang Dai
arXiv AI
Aug 19

Effective Personalized AI Tutors via LLM-Guided Reinforcement Learning

The paper presents a tutoring platform that combines a generative AI chatbot with a reinforcement learning algorithm to adaptively sequence practice problems for students learning Python. In a five‑month field study across ten high schools, the adaptive sequencing improved unassisted final exam performance by 0.15 standard deviations, with mediation analysis indicating that higher engagement drove the gains. The study demonstrates that signals from student‑chatbot interactions can be leveraged to personalize and optimize learning at scale.

By Angel Tsai-Hsuan Chung, Botong Zhang, Ling-Chieh Kung, Hamsa Bastani, Osbert Bastani