  Large language models exhibit superior capabilities in processing and
understanding language, yet their applications in educational contexts remain
underexplored. Learnersourcing enhances learning by engaging students in
creating their own educational content. When learnersourcing multiple-choice
questions, creating explanations for the solution of a question is a crucial
step; it helps other students understand the solution and promotes a deeper
understanding of related concepts. However, it is often difficult for students
to craft effective solution explanations, due to limited subject understanding.
To help scaffold the task of automated explanation generation, we present and
evaluate a framework called "ILearner-LLM", that iteratively enhances the
generated explanations for the given questions with large language models.
Comprising an explanation generation model and an explanation evaluation model,
the framework generates high-quality student-aligned explanations by
iteratively feeding the quality rating score from the evaluation model back
into the instruction prompt of the explanation generation model. Experimental
results demonstrate the effectiveness of our ILearner-LLM on LLaMA2-13B and
GPT-4 to generate higher quality explanations that are closer to those written
by students on five PeerWise datasets. Our findings represent a promising path
to enrich the learnersourcing experience for students and to enhance the
capabilities of large language models for educational applications.
