Multiple-choice questions (MCQs) are ubiquitous in almost all levels of
education since they are easy to administer, grade, and are a reliable format
in both assessments and practices. An important aspect of MCQs is the
distractors, i.e., incorrect options that are designed to target specific
misconceptions or insufficient knowledge among students. To date, the task of
crafting high-quality distractors has largely remained a labor-intensive
process for teachers and learning content designers, which has limited
scalability. In this work, we explore the task of automated distractor and
corresponding feedback message generation in math MCQs using large language
models. We establish a formulation of these two tasks and propose a simple,
in-context learning-based solution. Moreover, we explore using two non-standard
metrics to evaluate the quality of the generated distractors and feedback
messages. We conduct extensive experiments on these tasks using a real-world
MCQ dataset that contains student response information. Our findings suggest
that there is a lot of room for improvement in automated distractor and
feedback generation. We also outline several directions for future work

使用大型语言模型，本研究探索了数学多项选择题中自动生成干扰项和相应反馈信息的任务，并通过非标准指标对生成的干扰项和反馈信息进行了评估，结果显示自动化干扰项和反馈生成方面仍有提升空间，并提出了几个未来研究的方向。