Personalized Distractor Generation via MCTS-Guided Reasoning Reconstruction
- URL: http://arxiv.org/abs/2508.11184v1
- Date: Fri, 15 Aug 2025 03:20:37 GMT
- Title: Personalized Distractor Generation via MCTS-Guided Reasoning Reconstruction
- Authors: Tao Wu, Jingyuan Chen, Wang Lin, Jian Zhan, Mengze Li, Kun Kuang, Fei Wu,
- Abstract summary: Distractors, incorrect but plausible answer choices in multiple-choice questions (MCQs) play a critical role in educational assessment by diagnosing student misconceptions.<n>Recent work has leveraged large language models (LLMs) to generate shared, group-level distractors.<n>We introduce the task of personalized distractor generation, which aims to generate tailored distractors based on individual misconceptions inferred from each student's past question-answering (QA) records.
- Score: 33.217474795590576
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Distractors, incorrect but plausible answer choices in multiple-choice questions (MCQs), play a critical role in educational assessment by diagnosing student misconceptions. Recent work has leveraged large language models (LLMs) to generate shared, group-level distractors by learning common error patterns across large student populations. However, such distractors often fail to capture the diverse reasoning errors of individual students, limiting their diagnostic effectiveness. To address this limitation, we introduce the task of personalized distractor generation, which aims to generate tailored distractors based on individual misconceptions inferred from each student's past question-answering (QA) records, ensuring every student receives options that effectively exposes their specific reasoning errors. While promising, this task is challenging because each student typically has only a few QA records, which often lack the student's underlying reasoning processes, making training-based group-level approaches infeasible. To overcome this, we propose a training-free two-stage framework. In the first stage, we construct a student-specific misconception prototype by applying Monte Carlo Tree Search (MCTS) to recover the student's reasoning trajectories from past incorrect answers. In the second stage, this prototype guides the simulation of the student's reasoning on new questions, enabling the generation of personalized distractors that align with the student's recurring misconceptions. Experiments show that our approach achieves the best performance in generating plausible, personalized distractors for 140 students, and also effectively generalizes to group-level settings, highlighting its robustness and adaptability.
Related papers
- Learning to Make MISTAKEs: Modeling Incorrect Student Thinking And Key Errors [58.65143578052761]
This paper presents a new method, MISTAKE, that constructs high-quality synthetic examples of reasoning errors.<n>We evaluate MISTAKE on three educational tasks and find that it results in (1) higher accuracy when simulating incorrect student answers.
arXiv Detail & Related papers (2025-10-13T15:10:38Z) - From Correction to Mastery: Reinforced Distillation of Large Language Model Agents [13.982204994247718]
Large Language Model agents excel at solving complex tasks through iterative reasoning and tool use.<n>Existing distillation approaches train smaller students to imitate full teacher trajectories.<n>We propose SCoRe, a student-centered framework in which the student generates training trajectories and the teacher corrects only the earliest error.
arXiv Detail & Related papers (2025-09-12T15:34:07Z) - The Imitation Game for Educational AI [23.71250100390303]
We present a novel evaluation framework based on a two-phase Turing-like test.<n>In Phase 1, students provide open-ended responses to questions, revealing natural misconceptions.<n>In Phase 2, both AI and human experts, conditioned on each student's specific mistakes, generate distractors for new related questions.
arXiv Detail & Related papers (2025-02-21T01:14:55Z) - Generating Plausible Distractors for Multiple-Choice Questions via Student Choice Prediction [1.9949730506194254]
In designing multiple-choice questions (MCQs) in education, creating plausible distractors is crucial for identifying students' misconceptions and gaps in knowledge.<n>This study presents a pipeline for training a model to generate distractors that are more likely to be selected by students.
arXiv Detail & Related papers (2025-01-21T10:20:39Z) - SuperCorrect: Advancing Small LLM Reasoning with Thought Template Distillation and Self-Correction [89.56181323849512]
SuperCorrect is a novel two-stage framework that uses a large teacher model to supervise and correct both the reasoning and reflection processes of a smaller student model.<n>In the first stage, we extract hierarchical high-level and detailed thought templates from the teacher model to guide the student model in eliciting more fine-grained reasoning thoughts.<n>In the second stage, we introduce cross-model collaborative direct preference optimization (DPO) to enhance the self-correction abilities of the student model.
arXiv Detail & Related papers (2024-10-11T17:25:52Z) - Stepwise Verification and Remediation of Student Reasoning Errors with Large Language Model Tutors [78.53699244846285]
Large language models (LLMs) present an opportunity to scale high-quality personalized education to all.
LLMs struggle to precisely detect student's errors and tailor their feedback to these errors.
Inspired by real-world teaching practice where teachers identify student errors and customize their response based on them, we focus on verifying student solutions.
arXiv Detail & Related papers (2024-07-12T10:11:40Z) - Co-Supervised Learning: Improving Weak-to-Strong Generalization with
Hierarchical Mixture of Experts [81.37287967870589]
We propose to harness a diverse set of specialized teachers, instead of a single generalist one, that collectively supervises the strong student.
Our approach resembles the classical hierarchical mixture of experts, with two components tailored for co-supervision.
We validate the proposed method through visual recognition tasks on the OpenAI weak-to-strong benchmark and additional multi-domain datasets.
arXiv Detail & Related papers (2024-02-23T18:56:11Z) - 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) - Distantly-Supervised Named Entity Recognition with Adaptive Teacher
Learning and Fine-grained Student Ensemble [56.705249154629264]
Self-training teacher-student frameworks are proposed to improve the robustness of NER models.
In this paper, we propose an adaptive teacher learning comprised of two teacher-student networks.
Fine-grained student ensemble updates each fragment of the teacher model with a temporal moving average of the corresponding fragment of the student, which enhances consistent predictions on each model fragment against noise.
arXiv Detail & Related papers (2022-12-13T12:14:09Z)
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.