The distractor generation task focuses on generating incorrect but plausible options for objective questions such as fill-in-the-blank and multiple-choice questions. This task is widely utilized in educational settings across various domains and subjects. The effectiveness of these questions in assessments relies on the quality of the distractors, as they challenge examinees to select the correct answer from a set of misleading options. The evolution of artificial intelligence (AI) has transitioned the task from traditional methods to the use of neural networks and pre-trained language models. This shift has established new benchmarks and expanded the use of advanced deep learning methods in generating distractors. This survey explores distractor generation tasks, datasets, methods, and current evaluation metrics for English objective questions, covering both text-based and multi-modal domains. It also evaluates existing AI models and benchmarks and discusses potential future research directions.

翻译：干扰项生成任务专注于为填空题和多项选择题等客观题生成错误但看似合理的选项。该任务广泛应用于各领域和学科的教育场景中。此类题目在评估中的有效性取决于干扰项的质量，因为它们能挑战应试者从一系列具有误导性的选项中选出正确答案。人工智能的发展使该任务从传统方法转向使用神经网络和预训练语言模型。这一转变建立了新的基准，并扩展了高级深度学习方法在干扰项生成中的应用。本综述探讨了英语客观题的干扰项生成任务、数据集、方法及当前评估指标，涵盖文本和多模态领域。同时评估了现有AI模型与基准，并讨论了未来潜在的研究方向。