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 to limited subject understanding. To help scaffold the task of automated explanation generation, we present and evaluate a framework called "ILearner-LLM", that iteratively enhances the generated explanations for the given questions with large language models. Comprising an explanation generation model and an explanation evaluation model, the framework generates high-quality student-aligned explanations by iteratively feeding the quality rating score from the evaluation model back into the instruction prompt of the explanation generation model. Experimental results demonstrate the effectiveness of our ILearner-LLM on LLaMA2-13B and GPT-4 to generate higher quality explanations that are closer to those written by students on five PeerWise datasets. Our findings represent a promising path to enrich the learnersourcing experience for students and to enhance the capabilities of large language models for educational applications.

翻译：大型语言模型在语言处理和理解方面展现出卓越能力，但其在教育领域的应用仍有待深入探索。学习者生成内容通过引导学生自主创建教育材料来增强学习效果。在学生学习生成多项选择题的过程中，为题目解答撰写解释性说明是至关重要的一环——这既有助于其他学生理解解题思路，也能促进对相关概念的深层理解。然而，由于学科知识储备有限，学生往往难以构建有效的解题说明。为辅助自动化解释生成任务，我们提出并评估了一个名为"ILearner-LLM"的框架，该框架通过大型语言模型对给定题目的生成解释进行迭代优化。该框架包含解释生成模型与解释评估模型，通过将评估模型生成的质量评分反馈至解释生成模型的指令提示中，持续生成高质量且符合学生认知水平的解释。实验结果表明，我们的ILearner-LLM框架在LLaMA2-13B和GPT-4模型上均能有效生成更接近学生写作水平的优质解释，并在五个PeerWise数据集上得到验证。本研究为丰富学习者生成内容体验、增强大型语言模型在教育场景中的能力开辟了新的路径。