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测和寻求学生教育性解释的能力，当学生无法独立阐述这些解释时，他们会提出发人深省的问题。我们旨在利用大语言模型的小样本学习能力，将这一能力赋予智能辅导系统。本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、注重细节的教育性解释。我们的核心假设是：在教育领域，小样本示范是生成高质量解释的必要条件，但非充分条件。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比较。结果表明，根据教师评估，断言增强的少样本学习可将解释准确性提高15%，并生成更高质量的解释。我们还进行了定性消融研究，以解析断言的影响，从而为教育工作者在其感兴趣领域生成解释提供友好的提示指导原则。