arXiv:2605.24211v1 Announce Type: new Abstract: 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 con
The continuous struggle of LLMs to generate high-quality, human-like analogies creates a pressing need for modular and systematic approaches to improve their educational capabilities.
Improving LLM's ability to generate educational analogies can significantly enhance AI's role in pedagogy and concept transfer, impacting how knowledge is disseminated and understood.
The focus shifts from monolithic LLM prompting to a modular, pipeline-based approach for complex cognitive tasks, enabling more systematic development and evaluation of AI capabilities.
- · EdTech companies
- · AI researchers specializing in cognitive architectures
- · Learners and educators
- · Developers of AI agentic systems
- · Primitive, single-prompt AI analogy generators
LLMs will become more effective at explaining complex topics by generating contextually relevant and accurate analogies.
The modular approach could be extended to other complex AI tasks, leading to more robust and explainable AI systems beyond analogy generation.
Enhanced AI educational capabilities could accelerate skill acquisition and knowledge diffusion across various sectors, potentially altering traditional learning institutions.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL