Exploring Iterative Enhancement for Improving Learnersourced Multiple-Choice Question Explanations with Large Language Models

arXiv:2309.10444v4 Announce Type: replace-cross
Abstract: Large language models exhibit superior capabilities in processing and understanding language, yet their applications in educational contexts remain underexplored. Learnersourcing enhances learning by engaging students in creating their own educational content. When learnersourcing multiple-choice questions, creating explanations for the solution of a question is a crucial step; it helps other students understand the solution and promotes a deeper understanding of related concepts. However, it is often difficult for students to craft effective solution explanations, due …

abstract applications arxiv capabilities cs.ai cs.cl educational iterative language language models large language large language models multiple processing question questions solution students type understanding