Automated Feedback Generation for Undergraduate Mathematics: Development and Evaluation of an AI Teaching Assistant

• URL: http://arxiv.org/abs/2601.03458v1
• Date: Tue, 06 Jan 2026 23:02:22 GMT
• Title: Automated Feedback Generation for Undergraduate Mathematics: Development and Evaluation of an AI Teaching Assistant
• Authors: Aron Gohr, Marie-Amelie Lawn, Kevin Gao, Inigo Serjeant, Stephen Heslip,
• Abstract summary: We present a system that processes free-form natural language input, handles a wide range of edge cases, and comments on the technical correctness of submitted proofs.<n>We show that by the metrics we evaluate, the quality of the feedback generated is comparable to that produced by human experts.<n>A version of our tool is deployed on the Imperial mathematics homework platform Lambda.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Intelligent tutoring systems have long enabled automated immediate feedback on student work when it is presented in a tightly structured format and when problems are very constrained, but reliably assessing free-form mathematical reasoning remains challenging. We present a system that processes free-form natural language input, handles a wide range of edge cases, and comments competently not only on the technical correctness of submitted proofs, but also on style and presentation issues. We discuss the advantages and disadvantages of various approaches to the evaluation of such a system, and show that by the metrics we evaluate, the quality of the feedback generated is comparable to that produced by human experts when assessing early undergraduate homework. We stress-test our system with a small set of more advanced and unusual questions, and report both significant gaps and encouraging successes in that more challenging setting. Our system uses large language models in a modular workflow. The workflow configuration is human-readable and editable without programming knowledge, and allows some intermediate steps to be precomputed or injected by the instructor. A version of our tool is deployed on the Imperial mathematics homework platform Lambdafeedback. We report also on the integration of our tool into this platform.

Related papers

• Exploring the Role of Automated Feedback in Programming Education: A Systematic Literature Review [0.08376229126363229]
  This systematic literature review synthesizes 61 empirical studies published by September 2024.<n>Findings reveal that most systems are fully automated, embedded within online platforms.<n>Few systems offer support for higher-order learning processes, interactive components, or learner agency.
  arXiv  Detail & Related papers  (2026-01-23T05:20:37Z)
• Stitch: Step-by-step LLM Guided Tutoring for Scratch [1.8206350996077172]
  We present Stitch, an interactive tutoring system that replaces "showing the answer" with step-by-step scaffolding.<n>We evaluate Stitch in an empirical study, comparing it against a state-of-the-art automated feedback generation tool for Scratch.
  arXiv  Detail & Related papers  (2025-10-30T16:03:56Z)
• MathMistake Checker: A Comprehensive Demonstration for Step-by-Step Math Problem Mistake Finding by Prompt-Guided LLMs [13.756898876556455]
  We propose a novel system, MathMistake Checker, to automate step-by-step mistake finding in mathematical problems with lengthy answers.<n>The system aims to simplify grading, increase efficiency, and enhance learning experiences from a pedagogical perspective.
  arXiv  Detail & Related papers  (2025-03-06T10:19:01Z)
• MathTutorBench: A Benchmark for Measuring Open-ended Pedagogical Capabilities of LLM Tutors [82.91830877219822]
  We present MathTutorBench, an open-source benchmark for holistic tutoring model evaluation.<n>MathTutorBench contains datasets and metrics that broadly cover tutor abilities as defined by learning sciences research in dialog-based teaching.<n>We evaluate a wide set of closed- and open-weight models and find that subject expertise, indicated by solving ability, does not immediately translate to good teaching.
  arXiv  Detail & Related papers  (2025-02-26T08:43:47Z)
• LLM Critics Help Catch Bugs in Mathematics: Towards a Better Mathematical Verifier with Natural Language Feedback [71.95402654982095]
  We propose Math-Minos, a natural language feedback-enhanced verifier. Our experiments reveal that a small set of natural language feedback can significantly boost the performance of the verifier.
  arXiv  Detail & Related papers  (2024-06-20T06:42:27Z)
• Third-Party Language Model Performance Prediction from Instruction [59.574169249307054]
  Language model-based instruction-following systems have lately shown increasing performance on many benchmark tasks. A user may easily prompt a model with an instruction without any idea of whether the responses should be expected to be accurate. We propose a third party performance prediction framework, where a separate model is trained to predict the metric resulting from evaluating an instruction-following system on a task.
  arXiv  Detail & Related papers  (2024-03-19T03:53:47Z)
• UltraFeedback: Boosting Language Models with Scaled AI Feedback [99.4633351133207]
  We present textscUltraFeedback, a large-scale, high-quality, and diversified AI feedback dataset. Our work validates the effectiveness of scaled AI feedback data in constructing strong open-source chat language models.
  arXiv  Detail & Related papers  (2023-10-02T17:40:01Z)
• Automated Distractor and Feedback Generation for Math Multiple-choice Questions via In-context Learning [43.83422798569986]
  Multiple-choice questions (MCQs) are ubiquitous in almost all levels of education since they are easy to administer, grade, and reliable form of assessment. To date, the task of crafting high-quality distractors has largely remained a labor-intensive process for teachers and learning content designers. We propose a simple, in-context learning-based solution for automated distractor and corresponding feedback message generation.
  arXiv  Detail & Related papers  (2023-08-07T01:03:04Z)
• System-Level Natural Language Feedback [83.24259100437965]
  We show how to use feedback to formalize system-level design decisions in a human-in-the-loop-process. We conduct two case studies of this approach for improving search query and dialog response generation. We show the combination of system-level and instance-level feedback brings further gains.
  arXiv  Detail & Related papers  (2023-06-23T16:21:40Z)
• Automated Grading and Feedback Tools for Programming Education: A Systematic Review [7.776434991976473]
  Most papers assess the correctness of assignments in object-oriented languages. Few tools assess the maintainability, readability or documentation of the source code. Most tools offered fully automated assessment to allow for near-instantaneous feedback.
  arXiv  Detail & Related papers  (2023-06-20T17:54:50Z)
• Evaluating Language Models for Mathematics through Interactions [116.67206980096513]
  We introduce CheckMate, a prototype platform for humans to interact with and evaluate large language models (LLMs) We conduct a study with CheckMate to evaluate three language models (InstructGPT, ChatGPT, and GPT-4) as assistants in proving undergraduate-level mathematics. We derive a taxonomy of human behaviours and uncover that despite a generally positive correlation, there are notable instances of divergence between correctness and perceived helpfulness.
  arXiv  Detail & Related papers  (2023-06-02T17:12:25Z)
• Modelling Assessment Rubrics through Bayesian Networks: a Pragmatic Approach [40.06500618820166]
  This paper presents an approach to deriving a learner model directly from an assessment rubric. We illustrate how the approach can be applied to automatize the human assessment of an activity developed for testing computational thinking skills.
  arXiv  Detail & Related papers  (2022-09-07T10:09:12Z)
• ProtoTransformer: A Meta-Learning Approach to Providing Student Feedback [54.142719510638614]
  In this paper, we frame the problem of providing feedback as few-shot classification. A meta-learner adapts to give feedback to student code on a new programming question from just a few examples by instructors. Our approach was successfully deployed to deliver feedback to 16,000 student exam-solutions in a programming course offered by a tier 1 university.
  arXiv  Detail & Related papers  (2021-07-23T22:41:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.