Analyzing Students' Preferences for LLM-Generated Analogies

Introducing students to new concepts in computer science can often be challenging, as these concepts may differ significantly from their existing knowledge and conceptual understanding. To address this, we employed analogies to help students connect new concepts to familiar ideas. Specifically, we generated analogies using large language models (LLMs), namely ChatGPT, and used them to help students make the necessary connections. In this poster, we present the results of our survey, in which students were provided with two analogies relating to different computing concepts, and were asked to describe the extent to which they were accurate, interesting, and useful. This data was used to determine how effective LLM-generated analogies can be for teaching computer science concepts, as well as how responsive students are to this approach.