"Learning by Teaching (LbT)" helps learners deepen their understanding by explaining concepts to others, with questions playing a vital role in identifying knowledge gaps and reinforcing comprehension. However, existing systems for generating such questions often rely on rigid templates and are expensive to build. To overcome these limitations, we developed a system using Large Language Models (LLMs) to create dynamic, contextually relevant questions for LbT. In our English vocabulary learning study, we examined which learner characteristics best leverage the system's benefits. Our results showed improved memory retention over traditional methods at three and seven days of testing, with ten participants. Additionally, we identified traits linked to better learning outcomes, highlighting the potential for tailored approaches. These findings support the development of scalable, cost-effective solutions to enhance LbT methods across various fields.

翻译：“教学相长”（Learning by Teaching, LbT）通过让学习者向他人解释概念来加深理解，其中问题在识别知识漏洞和强化理解方面发挥着关键作用。然而，现有用于生成此类问题的系统往往依赖僵化的模板，且构建成本高昂。为克服这些局限，我们开发了一个基于大语言模型（LLMs）的系统，可为教学相长生成动态且与语境相关的问题。在英语词汇学习研究中，我们检验了哪些学习者特征能最有效地利用该系统的优势。研究结果（包含十名参与者）显示，在三天和七天的测试中，该方法较传统方法能够提升记忆保持率。此外，我们还识别出与更好学习效果相关的特征，凸显了定制化方法的潜力。这些发现支持开发可扩展、低成本方案，以在多个领域增强教学相长方法的效果。