arXiv cs.AI· Junior Cedric Tonga, KV Aditya Srivatsa, Kaushal Kumar Maurya, Fajri Koto, Ekaterina Kochmar·· 3 天前AI 评分22
用 LLM 模拟多语言数学辅导中的学生-导师互动
Simulating Student-Tutor Interactions with LLMs for Multilingual Math Feedback
AI 导读
研究者首次用 LLM 大规模模拟多语言学生-导师互动：强模型扮演导师生成提示，弱模型扮演学生，覆盖 11 种语言、4 个 SOTA LLM 和 352 组实验设置。结果显示，即便对多语言学生 LLM，英文提示通常仍更受偏好，仅少数例外能带来更好的学习效果。
正文 · 原文
Abstract:Large language models (LLMs) have demonstrated the ability to generate formative feedback and instructional hints in English, making them increasingly relevant for AI-assisted education. However, their ability to provide effective instructional support across different languages, especially for mathematically grounded reasoning tasks, remains largely unexamined. In this work, we present the first large-scale simulation of multilingual student-tutor interactions using LLMs. A stronger model plays the role of the tutor, generating feedback in the form of hints, while a weaker model simulates the student. We explore 352 experimental settings across 11 typologically diverse languages, four state-of-the-art LLMs, and multiple prompting strategies to assess whether language-specific feedback leads to measurable learning gains. Our study examines how student input language, tutor feedback language, model choice, and language resource level jointly influence performance. Results show that English hints are generally preferred, even by multilingual student LLMs, with few exceptions that lead to improved student learning outcomes. These findings offer practical insights for developing multilingual, LLM-based educational tools that are both effective and inclusive.
| Comments: | MathNLP EMNLP 2026 |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2506.04920 [cs.CL] |
| (or arXiv:2506.04920v2 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2506.04920 arXiv-issued DOI via DataCite |
Submission history
From: Junior Cedric Tonga [view email]
[v1]
Thu, 5 Jun 2025 11:53:04 UTC (553 KB)
[v2]
Sat, 10 Oct 2026 10:01:20 UTC (540 KB)
来源：arXiv cs.AI · arxiv.org