arXiv AI
Simulating Student-Tutor Interactions with LLMs for Multilingual Math Feedback
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Jul 1
What Drives Interactive Improvement from Feedback?
arXiv:2606. 30774v1 Announce Type: new Abstract: We study when natural-language feedback produces improvement beyond the gains obtainable from repeated attempts alone.
arXiv Computation and Language
3d ago
Do LLM Tutors Actually Teach? A Process-Centered Synthesis of the Tutoring Loop
arXiv:2610.15692v2 Announce Type: replace Abstract: Large language models can produce fluent tu- toring dialogue, but whether they actually help students learn remains unclear. We present a process-c...
arXiv Computation and Language
Aug 25
LLM Pedagogical Behavior in AI Tutoring Interactions
arXiv:2608.22993v1 Announce Type: new Abstract: Students increasingly use LLMs as tutors for coursework and problem solving. Little is known about the level of assistance LLMs provide when students u...
arXiv AI
Sep 24
StudentBench: AI and human tutoring yield equivalent GRE learning gains
arXiv:2609.28470v1 Announce Type: new Abstract: Artificial intelligence offers an unprecedented opportunity to augment human capabilities, yet progress at the frontier has focused primarily on advanc...
arXiv AI
2d ago
Multi-sourced AI Tutor for Non-Native English Speaker Students
The paper presents a multi‑source Retrieval‑Augmented Generation (RAG) AI tutor designed to help non‑native English speaker (NNES) students in computing courses. It can process questions in foreign languages—currently Mandarin—and give bilingual answers, generate glossaries of technical terms, and translate queries. In tests on 91 Piazza posts, the tutor answered 83.52 % correctly, translated 76.92 % of questions, and produced glossaries for 64.47 % of them.
arXiv AI
Jul 24
AI Assistants Overassist
arXiv:2607. 21306v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as tutors and thought partners, helping users reason through problems.