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Human educators possess an intrinsic ability to anticipate and seekeducational explanations from students, which drives them to posethought-provoking questions when students cannot articulate these explanationsindependently. We aim to imbue Intelligent Tutoring Systems with this abilityusing few-shot learning capability of Large Language Models. Our work proposesa novel prompting technique, Assertion Enhanced Few-Shot Learning, tofacilitate the generation of accurate, detailed oriented educationalexplanations. Our central hypothesis is that, in educational domain, few-shotdemonstrations are necessary but not a sufficient condition for qualityexplanation generation. We conducted a study involving 12 in-service teachers,comparing our approach to Traditional Few-Shot Learning. The results show thatAssertion Enhanced Few-Shot Learning improves explanation accuracy by 15% andyields higher-quality explanations, as evaluated by teachers. We also conduct aqualitative ablation study to factor the impact of assertions to provideeducator-friendly prompting guidelines for generating explanations in theirdomain of interest.

Assertion Enhanced Few-Shot Learning: Instructive Technique for Large Language Models to Generate Educational Explanations