A Unified Response Framework for Questions and Tutoring Dialogues
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at arXiv Machine Learning.
arXiv:2605.30051v2 Announce Type: replace Abstract: A key part of developing large language model (LLM)-powered, automated tutoring tools is student simulation, i.e., using LLMs to role-play as stude...
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...
The study examines the nature of questions students pose to generative AI during two CS2 programming tasks, classifying 830 interactions into 18 categories based on the Graesser taxonomy. Results reveal that a limited set of question types dominates student inquiries and that the distribution of question types shifts significantly as the task progresses.
The paper proposes a response‑free method for estimating difficulty of reading‑comprehension multiple‑choice items by fine‑tuning a transformer on item wording. It introduces two extensions to a baseline joint‑encoding model: a component‑wise variant that encodes passage, question, and options separately, and a multi‑task variant that adds a question‑answering auxiliary task. Experiments on a corpus of nearly 30,000 items show that both extensions outperform the baseline, especially the multi‑task variant across all metrics and the component‑wise variant in rank ordering, even with limited training data.
arXiv:2606. 10736v1 Announce Type: cross Abstract: Large online courses generate thousands of student questions directed at conversational AI teaching assistants, yet these interaction logs remain largely untapped as diagnostic signals.
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.