让AI学会模拟学生错误，提升教育反馈的精准度。
Learning to Make MISTAKEs: Modeling Incorrect Student Thinking And Key Errors
- 利用错误答案与隐含误解的循环一致性生成真实错误示例。
- 在三项教育任务中，错误模拟准确率显著提升。
- 适合教育AI、智能辅导系统开发者参考。
语言模型的研究主要聚焦于提升输出正确性，但某些重要应用需要建模错误推理模式。例如，能分析和模拟学生错误的自动化系统，可为课堂实时反馈或教师培训提供支持。本文提出MISTAKE方法：(1) 利用错误答案与潜在误解之间的循环一致性，构建高质量的合成错误样本；(2) 使用生成数据训练学生模拟、误解分类和错误答案生成模型。我们在三个教育任务上评估MISTAKE，结果表明：(1) 基于特定误解模拟错误答案的准确率更高；(2) 从观察到的错误答案推断潜在误解的能力增强；(3) 生成的错误答案与专家设计的干扰项更一致（如多项选择题）。
原文摘要 · Abstract (English)
Research on reasoning in language models (LMs) predominantly focuses on improving the correctness of their outputs. But some important applications require modeling reasoning patterns that are incorrect. For example, automated systems that can reason about and simulate student errors are useful for providing real-time feedback in the classroom or offline practice for educators-in-training. This paper presents a new method, MISTAKE, that (1) constructs high-quality synthetic examples of reasoning errors by leveraging cycle consistency between incorrect answers and latent misconceptions; and (2) uses the generated data to learn models for student simulation, misconception classification, and answer generation. We evaluate MISTAKE on three educational tasks and find that it results in (1) higher accuracy when simulating incorrect student answers based on specific misconceptions, (2) increased performance inferring latent misconceptions from observed incorrect answers, and (3) higher alignment with expert-written distractor answers when generating incorrect answers (e.g., for multiple-choice tests).
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。