arXiv AI
Aug 5

EduClaw-Bench: A Long-Horizon Benchmark for Pedagogical LLM Agents with Simulated Learners

arXiv:2608. 03206v1 Announce Type: cross Abstract: Large language models (LLMs) power educational applications from tutoring to essay scoring, but each is a point solution to a single task, and only recently have these point solutions been integrated into agents operating over a learning management system (LMS).

By Unggi Lee, Sookbun Lee, Yeil Jeong, Eunjoo Lee, Minchul Shin, Hoilym Kwon
arXiv Machine Learning
Sep 14

Simulating Disengaged Students to Evaluate LLM-based Tutors

The paper introduces Disengagement-Aware Student Simulators (DAS2), a protocol that models five learner-engagement states—engaged, gaming, wheel-spinning, off-task, and mixed—to evaluate AI tutor performance before deployment. Using annotated tutoring sessions from ASSISTments09, DAS2’s rule-based labels matched human consensus in 81% of cases, and conditioning simulations on intended states narrowed the correctness-rate gap between simulated and authentic sessions for gaming and wheel-spinning behaviors. The study also compares five AI tutors across these states, finding stable relative rankings but state-specific performance differences, and notes that automated evaluation does not fully align with human judgment.

By Xianghui Meng, Jionghao Lin
arXiv Machine Learning
Oct 15

HACHIMI-II: From Role-Play to Learner Behavior Modeling for Faithful Student Simulation

HACHIMI-II redefines student simulation by modeling a learner’s behavior rather than relying on a language model’s role‑play. It builds a student model from the learner’s history, infers their current state, and uses this to constrain the next simulated action. Across item‑response and dialogue datasets, HACHIMI-II achieves markedly smaller ability gaps and more accurate error reproduction than leading baselines.

By Yilin Jiang, Fei Tan, Xuanyu Yin, Jing Leng, Aimin Zhou
arXiv Computation and Language
Sep 28

Recursive Self-Improvement via On-Policy Distillation for Reasoning

The paper introduces a recursive self-improvement framework for language models that replaces an external teacher with a frozen copy of the student, enabling dynamic co-evolution (DCE) and self-refined concise learning (SRCL). DCE allows the privileged teacher to evolve alongside the student, while SRCL trains on shorter, verified rewrites to reduce verbosity. Experiments show that the combined DCE+SRCL approach outperforms traditional on‑policy self‑distillation across multiple model sizes and math benchmarks, achieving significant accuracy gains and shorter outputs.

By Shangjian Yin, Zehao Zhao, Kavosh Asadi, Rui Liu, Yuchen Lu, Shike Mei, Hang Cui, Luke Simon, Zhouxing Shi, Hamed Firooz