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.

利用大型语言模型的少样本学习能力，我们提出了一种新颖的提示技术，即增强断言的少样本学习，以促进准确、详细的教育解释的生成。通过对 12 名在职教师的比较研究，我们证明了增强断言的少样本学习在解释准确性上提高了 15％，并且得到了教师评价中更高质量的解释。我们还进行了定性消融研究，以考察断言对生成感兴趣领域解释的影响，从而为教育者提供友好的提示指南。