Teaching Through Analogies: A Modular Pipeline for Educational Analogy Generation
Analogies help learners understand unfamiliar concepts by relating them to known concepts. Despite recent advances, large language models (LLMs) continue to struggle to generate analogies of comparable quality to those produced by humans. We present a modular pipeline for educational analogy generation, decomposing the task into four stages: source finding, sub-concept generation, explanation generation, and evaluation. Grounded in Structure Mapping Theory, the pipeline enables systematic, stage-by-stage analysis of how model choice and input configuration affect analogy quality. We evaluate 12 state-of-the-art LLMs across six model families on two datasets with structured sub-concept ann...
| Authors | Mariam Barakat, Ekaterina Kochmar |
|---|---|
| Primary category | cs.CL |
| Published | 22 May 2026 |
| Last updated at source | 22 May 2026 |
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