MathEDU: Feedback Generation on Problem-Solving Processes for Mathematical Learning Support
Year: 2025
Type: preprint
Abstract: The increasing reliance on Large Language Models (LLMs) across various domains extends to education, where students progressively use generative AI as a tool for learning. While prior work has examined LLMs' mathematical ability, their reliability in grading authentic student problem-solving processes and delivering effective feedback remains underexplored. This study introduces MathEDU, a dataset consisting of student problem-solving processes in mathematics and corresponding teacher-written feedback. We systematically evaluate the reliability of various models across three hierarchical tasks: answer correctness classification, error identification, and feedback generation. Experimental results show that fine-tuning strategies effectively improve performance in classifying correctness and locating erroneous steps. However, the generated feedback across models shows a considerable gap from teacher-written feedback. Critically, the generated feedback is often verbose and fails to provide targeted explanations for the student's underlying misconceptions. This emphasizes the urgent need for trustworthy and pedagogy-aware AI feedback in education.
Source: arXiv (Cornell University)
Authors: Hsu, Wei-Ling, Tang, Yu-Chien, Yen, An-Zi
Institution: National Yang Ming Chiao Tung University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W6929586339
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