Human educators possess an intrinsic ability to anticipate and seek educational explanations from students, which drives them to pose thought-provoking questions when students cannot articulate these explanations independently. We aim to imbue Intelligent Tutoring Systems with this ability using few-shot learning capability of Large Language Models. Our work proposes a novel prompting technique, Assertion Enhanced Few-Shot Learning, to facilitate the generation of accurate, detailed oriented educational explanations. Our central hypothesis is that, in educational domain, few-shot demonstrations are necessary but not a sufficient condition for quality explanation generation. We conducted a study involving 12 in-service teachers, comparing our approach to Traditional Few-Shot Learning. The results show that Assertion Enhanced Few-Shot Learning improves explanation accuracy by 15% and yields higher-quality explanations, as evaluated by teachers. We also conduct a qualitative ablation study to factor the impact of assertions to provide educator-friendly prompting guidelines for generating explanations in their domain of interest.

翻译：人类教育者天生具备预判和寻求学生教育解释的能力，这促使他们在学生无法独立阐述解释时提出启发性的问题。我们旨在利用大型语言模型的小样本学习能力，赋予智能辅导系统这种能力。我们的研究提出了一种新颖的提示技术——断言增强的小样本学习，以促进生成准确、注重细节的教育解释。核心假设是：在教育领域，小样本演示是生成高质量解释的必要条件，但并非充分条件。我们开展了一项涉及12名在职教师的研究，将我们的方法与传统的少样本学习进行了比较。结果表明，断言增强的小样本学习使解释准确性提高了15%，并产生了更高质量的解释（经教师评估）。我们还进行了一项定性的消融研究，以分解断言的影响，从而为教育者在其感兴趣的领域生成解释提供友好的提示指南。