  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.
