提出模块化框架，让教育类大模型问答系统更透明、可调试。
EduMod-LLM: A Modular Approach for Designing Flexible and Transparent Educational Assistants
- 分模块设计函数调用流程，独立评估每一步性能。
- 新检索方法优于传统向量与LLM评分基准。
- 适合教育AI开发者，提升系统可解释性。
随着基于大语言模型（LLM）的问答系统在教育领域应用日益广泛，对其各个组件的性能评估变得至关重要。本文提出 { model}，一种模块化的函数调用式LLM流水线，并从三个关键维度进行全面评估：函数调用策略、检索方法与生成模型。该框架通过隔离各组件实现细粒度分析。我们对比了多种LLM的函数调用表现，将新型结构感知检索方法与基于向量和LLM评分的基线进行比较，并评估不同LLM在答案合成中的效果。该模块化方法揭示了特定失败模式与性能规律，支持开发更具可解释性与教学适配性的教育问答系统。结果表明，模块化函数调用能有效提升系统的透明度与教育契合度。
原文摘要 · Abstract (English)
With the growing use of Large Language Model (LLM)-based Question-Answering (QA) systems in education, it is critical to evaluate their performance across individual pipeline components. In this work, we introduce {\model}, a modular function-calling LLM pipeline, and present a comprehensive evaluation along three key axes: function calling strategies, retrieval methods, and generative language models. Our framework enables fine-grained analysis by isolating and assessing each component. We benchmark function-calling performance across LLMs, compare our novel structure-aware retrieval method to vector-based and LLM-scoring baselines, and evaluate various LLMs for response synthesis. This modular approach reveals specific failure modes and performance patterns, supporting the development of interpretable and effective educational QA systems. Our findings demonstrate the value of modular function calling in improving system transparency and pedagogical alignment. Website and Supplementary Material: https://chancharikmitra.github.io/EduMod-LLM-website/
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