MisEdu‑RAG is a dual‑hypergraph retrieval‑augmented generation framework designed to help novice math teachers diagnose and remediate student misconceptions. It structures pedagogical knowledge as a concept hypergraph and real student mistake cases as an instance hypergraph, performing two‑stage retrieval to ground responses in both layers. On the MisstepMath dataset, MisEdu‑RAG outperforms baseline models, improving token‑F1 by 10.95% and achieving up to 15.3% higher quality across five dimensions, especially in diversity and empowerment.
By Zhihan Guo, Yuting Lu, Jionghao Lin
arXiv:2508. 17092v2 Announce Type: replace-cross Abstract: Knowledge Tracing (KT) aims to predict a student's future performance based on their sequence of interactions with learning content.
By Yahya Badran, Christine Preisach
EduDial is a large-scale multi-turn teacher‑student dialogue corpus covering 345 core knowledge points and 34,250 dialogue sessions, designed around Bloom’s taxonomy and ten questioning strategies such as situational, ZPD, and metacognitive questioning. The dataset includes differentiated teaching strategies for students at varying cognitive levels to provide targeted guidance. Using EduDial, the authors trained EduDial‑LLM 32B and introduced an 11‑dimensional evaluation framework that measures teaching quality and content quality, showing that most mainstream LLMs struggle with student‑centered teaching while EduDial‑LLM outperforms all baselines across all metrics.
By Shouang Wei, Min Zhang, Xin Lin, Bo Jiang, Zhongxiang Dai, Kun Kuang
The paper investigates how large language models (LLMs) generate distractor answers for multiple‑choice questions (MCQs) by modeling student misconceptions. It introduces a learning‑science‑based taxonomy of reasoning strategies and applies it to LLM‑generated reasoning traces in math and science MCQs. The study finds that in math, LLMs often follow a misconception‑based process that can be diagnostically useful, whereas in science they rely more on semantic similarity, with frequent failures when the model cannot produce a correct solution or discards plausible distractors. Providing the correct solution in the prompt improves alignment with human distractors by 6.4%.
"whyItMatters":"The findings show that anchoring distractor generation to the correct solution enhances LLM alignment with human‑authored distractors, underscoring the importance of correct‑answer cues in educational AI."
By Yanick Zengaffinen, Andreas Opedal, Donya Rooein, Kv Aditya Srivatsa, Shashank Sonkar, Mrinmaya Sachan
IDEAlign introduces a new protocol for evaluating the similarity of large language model (LLM) annotations to expert judgments. It uses pick‑the‑odd‑one‑out tasks to capture expert similarity and benchmarks various similarity methods—including text embeddings, topic models, and LLM-as-a-judge—against these human ratings. Applied to educational datasets, the study finds that most metrics miss nuanced expert dimensions, with LLM-as-a-judge performing best yet still insufficient for full expert alignment.
By Hyunji Nam, Lucia Langlois, James Malamut, Mei Tan, Dorottya Demszky
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...
By Zhangqi Duan, Shuyan Huang, Alexander Scarlatos, Jaewook Lee, Simon Woodhead, Andrew Lan
The paper "Limits of LLM Text Detectors in Education" argues that existing LLM‑generated text detectors assume a binary human/LLM distinction, which fails to capture realistic student‑AI collaboration. It introduces a contribution‑aware evaluation framework with eight student contribution levels and presents GEDE, a benchmark of over 900 human‑written and 12,500 generated essays across 886 tasks. Using GEDE, the authors evaluate four detection methods and find that most detectors perform poorly on intermediate contribution levels, especially LLM‑assisted revisions, raising concerns about false accusations.
By Lukas Gehring, Benjamin Paa{\ss}en
The paper introduces a knowledge‑graph‑based evaluation framework, S3KG, to assess whether large language models truly understand context in question answering tasks. S3KG combines structural and semantic signals into a single similarity score and is paired with a diagnostic analysis that pinpoints reasoning errors at the triplet level. Across nine benchmarks, the method outperforms existing baselines, achieving up to +7.6 F1 points and an AUROC of 0.973.
By Subavarshana Arumugam, Mamta Nallaretnam, Kithuni Wickramasinghe, Chamath Gunapala, Pragatheeswaran Vipulanandan, Kamal Premaratne, Uthayasanker Thayasivam
arXiv:2609.09004v1 Announce Type: cross
Abstract: Large Language Models (LLMs) demonstrate impressive performance across diverse NLP tasks, yet their ability to exhibit genuine contextual understandi...
By Subavarshana Arumugam, Mamta Nallaretnam, Kithuni Wickramasinghe, Chamath Gunapala, Pragatheeswaran Vipulanandan, Uthayasanker Thayasivam, Kamal Premaratne
arXiv:2606. 01584v1 Announce Type: cross Abstract: Conversational tutoring agents have been shown to improve learning engagement and student outcomes, and large language models (LLMs) are increasingly used in these systems to provide scalable, personalized feedback.
By Aitor Arronte Alvarez, Naiyi Xie Fincham
arXiv:2605. 15416v2 Announce Type: replace-cross Abstract: Jung et al.
By Gaojie Jin, Yong Tao, Lijia Yu, Tianjin Huang
arXiv:2606. 29049v1 Announce Type: new Abstract: Knowledge Tracing (KT) is important for personalized education but traditionally suffers from two key limitations: a reliance on shallow ID-based representations that neglect semantic depth and a restriction to single-granularity mastery estimation that overlooks hierarchical knowledge dependencies.
By Xinjin Li, Mengyue Wang, Yuzhen Lin, Pengbin Feng, Ziqi Sha, Yeyang Zhou, Yu Ma