Misconception Diagnosis From Student-Tutor Dialogue: Generate, Retrieve, Rerank

[2602.02414] Misconception Diagnosis From Student-Tutor Dialogue: Generate, Retrieve, Rerank Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computation and Language arXiv:2602.02414 (cs) [Submitted on 2 Feb 2026 ( v1 ), last revised 15 Aug 2026 (this version, v2)] Title: Misconception Diagnosis From Student-Tutor Dialogue: Generate, Retrieve, Rerank Authors: Joshua Mitton , Prarthana Bhattacharyya , Digory Smith , Thomas Christie , Ralph Abboud , Simon Woodhead View a PDF of the paper titled Misconception Diagnosis From Student-Tutor Dialogue: Generate, Retrieve, Rerank, by Joshua Mitton and 5 other authors View PDF HTML (experimental) Abstract: Timely and accurate identification of student misconceptions is key to improving learning outcomes and pre-empting the compounding of student errors. However, this task is highly dependent on the effort and intuition of the teacher. In this work, we present a novel approach for detecting misconceptions from student-tutor dialogues using large language models (LLMs). First, we use a fine-tuned LLM to generate plausible misconceptions, and then retrieve the most promising candidates among these using embedding similarity with the input dialogue. These candidates are then assessed and re-ranked by another fine-tuned LLM to improve misconception relevance. Empirically, we evaluate our system on real dialogues from an educational tutoring platform. We consider multiple base LLM models including LLaMA, Qwen and Claude on zero-shot and fine-tuned settings. We find that our approach improves predictive performance over baseline models and that fine-tuning improves both generated misconception quality and can outperform larger closed-source models. Finally, we conduct ablation studies to both validate the importance of our generation and reranking steps on misconception generation quality. Comments: Published as Oral Paper at Learning at Scale, 2026. Link: this https URL . 21 pages, 8 figures, 8 tables. Joshua Mitton and Prarthana Bhattacharyya contributed equally to this paper Subjects: Computation and Language (cs.CL) ; Machine Learning (cs.LG) Cite as: arXiv:2602.02414 [cs.CL] (or arXiv:2602.02414v2 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2602.02414 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Prarthana Bhattacharyya [ view email ] [v1] Mon, 2 Feb 2026 18:14:35 UTC (547 KB) [v2] Sat, 15 Aug 2026 13:11:33 UTC (547 KB) Full-text links: Access Paper: View a PDF of the paper titled Misconception Diagnosis From Student-Tutor Dialogue: Generate, Retrieve, Rerank, by Joshua Mitton and 5 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CL < prev | next > new | recent | 2026-02 Change to browse by: cs cs.LG References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

Related documents

• Inverse Confounding Analysis: An Exact Method for Quantifying the Significance of Confounding#684581
• Inverse Confounding Analysis: An Exact Method for Quantifying the Significance of Confounding#682804
• An Intelligent Tutoring System for Teaching the 7 Characteristics for Living Things#738692
• Learning to Fail#706193
• An Investigation Into Secondary School Students' Debugging Behaviour in Python#1061622