Distractor Generation

Distractor generation focuses on automatically creating plausible but incorrect answer choices for multiple-choice questions (MCQs), a crucial task for effective assessment and educational applications. Current research heavily utilizes large language models (LLMs) and transformer-based architectures, often incorporating techniques like retrieval augmentation, knowledge graph integration, and variational error modeling to improve distractor quality and relevance. This field is significant because high-quality distractors enhance the assessment value of MCQs, reducing the reliance on time-consuming manual creation and enabling more efficient and scalable testing across various subjects and languages.