---
title: "Stepwise Verification and Remediation of Student Reasoning"
description: "LLM-based math tutors can solve word problems well, but they often fail to notice where a student… 48 references, cited by 30 papers here."
authors: ["Nico Daheim", "Jakub Macina", "Manu Kapur", "Iryna Gurevych", "Mrinmaya Sachan"]
year: 2024
venue: "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing"
source: "https://aclanthology.org/2024.emnlp-main.478/"
doi: "10.18653/v1/2024.emnlp-main.478"
arxiv: "2407.09136"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/daheim-2024-stepwise-verification-remediation-student-reasoning/"
---
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# Stepwise Verification and Remediation of Student Reasoning Errors with Large Language Model Tutors

**Authors:** [Nico Daheim](https://md.research.elamur.ai/free/en/authors/nico-daheim/), [Jakub Macina](https://md.research.elamur.ai/free/en/authors/jakub-macina/), [Manu Kapur](https://md.research.elamur.ai/free/en/authors/manu-kapur/), [Iryna Gurevych](https://md.research.elamur.ai/free/en/authors/iryna-gurevych/) and [Mrinmaya Sachan](https://md.research.elamur.ai/free/en/authors/mrinmaya-sachan/) · **Year:** 2024 · **Venue:** [Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing](https://md.research.elamur.ai/free/en/venues/emnlp/) ([EMNLP 2024](https://md.research.elamur.ai/free/en/venues/emnlp/2024/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2024.emnlp-main.478/) · [PDF](https://aclanthology.org/2024.emnlp-main.478.pdf) · [DOI 10.18653/v1/2024.emnlp-main.478](https://doi.org/10.18653/v1/2024.emnlp-main.478) · [arXiv 2407.09136](https://arxiv.org/abs/2407.09136) · [OpenAlex](https://openalex.org/W4404782691)

**Citations:** 5 citations (OpenAlex, 2026-10-14) · [30](https://md.research.elamur.ai/free/en/papers/daheim-2024-stepwise-verification-remediation-student-reasoning/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
LLM-based math tutors can solve word problems well, but they often fail to notice where a student went wrong. As a result, they may praise wrong answers or give feedback that misses the actual mistake. The authors split tutoring into two stages, mirroring how human teachers work. First, a verifier checks the student's solution. Then a separate generator writes the tutor's reply, conditioned on the verifier's output.

To support this, they extend the MathDial dialog tutoring corpus with about 1K stepwise student reasoning chains. In each chain, teachers marked the first erroneous step. They compare several verifiers, using both prompting and finetuning:
- binary and step-level classifiers
- free-text error descriptions
- an alignment between student steps and a reference solution

Locating the first error turns out to be hard for current models. However, finetuning smaller LLMs on the new data lets them beat prompted state-of-the-art models.

In automatic metrics and in teacher-based human evaluation, verification-grounded replies were more targeted, more often correct, less hallucinated, and more actionable. The gains were largest when the verification itself was correct.

## Key points
- **Dataset:** about 1K math reasoning chains with teacher-annotated first error steps, built on MathDial and released under CC-BY-4.0. Code is at github.com/eth-lre/verify-then-generate.
- **Verifier types compared:**
  - Overall Verification (binary)
  - Stepwise Verification (multi-class, with an iterative variant)
  - textual Error Description
  - Step Alignment against a reference solution
- **Baseline:** conditioning on coarse error categories ("Error Reason," Wang et al., 2024b). Error Description and Step Alignment reduced hallucinations most clearly.
- **Finetuning result:** smaller LLMs finetuned on the annotations outperformed prompted state-of-the-art LLMs at verification.
- **Limitations:** English only; reasoning chains are LLM-simulated rather than written by real students; evaluation was done by teachers, not students.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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- [ETH Zurich](https://md.research.elamur.ai/free/en/universities/eth-zurich/) — University, Zurich, Switzerland · 97 works · 9,612 citations
- [Hessian Center for Artificial Intelligence](https://md.research.elamur.ai/free/en/labs/hessian-center-for-artificial-intelligence/) — Research institute, Darmstadt, Germany · 17 works · 787 citations

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## Cite

Cite as: Nico Daheim, Jakub Macina, Manu Kapur, Iryna Gurevych and Mrinmaya Sachan. 2024. Stepwise Verification and Remediation of Student Reasoning Errors with Large Language Model Tutors. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{daheim2024stepwise,
  title = {{Stepwise Verification and Remediation of Student Reasoning Errors with Large Language Model Tutors}},
  author = {Nico Daheim, Jakub Macina, Manu Kapur, Iryna Gurevych and Mrinmaya Sachan},
  booktitle = {Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing},
  year = {2024},
  doi = {10.18653/v1/2024.emnlp-main.478},
  eprint = {2407.09136},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/2024.emnlp-main.478}
}
```

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*Updated 3 November 2026*