This paper explores if providing students with multiple, diverse explanations from Large Language Models (LLMs) improves their programming understanding more than generic explanations.

In plain terms

This research investigates how Large Language Models (LLMs) can best help students learn programming by providing explanations for code. The problem is that while LLMs can generate explanations, it's unclear if a single generic explanation is the most effective approach compared to human instructors. Researchers hypothesized that multiple, diverse explanations, each highlighting a different aspect (like what a function does, a core concept, or the overall goal), might be more beneficial. They tested this with 971 first-year computing students, randomly assigning them to receive either 'diverse' or 'generic' LLM-generated explanations for programming exercises. The study found that students who received diverse explanations consistently scored about 7.7% higher on open-ended questions, showing improved understanding. Crucially, this improvement did not lead to an increase in their perceived mental effort, known as cognitive load.