AI 中文总结
本研究提出两阶段微调流水线，将黑盒ML模型蒸馏为小型自解释LLM，可在普通设备上离线预测解释，在教育数据集上表现良好且数据不外泄。
AI 中文摘要
学习分析越来越依赖灵活的机器学习（ML），但模型的不透明性和部署负担阻碍了这些工具应用于教育实践。我们提出一种两阶段微调流水线，将已训练的黑盒估计器及其事后解释（导师模型）蒸馏为小型开放权重大语言模型（LLM；学生模型），该模型返回个体层面的估计值并以自然语言解释。该设计与估计器无关，并搭配以忠实性为核心的评估框架，对每一段叙述与其声称要描述的归因进行审计。我们设计了一项模拟研究，通过对比“理想导师模型”与“现实ML导师模型”，将蒸馏损失与估计器损失分离开来。在理想信号下，使用20亿参数LLM模型进行蒸馏，在恢复效应曲面（r > 0.90）、对重要变量进行完美排序、未引用虚假协变量方面几乎无损失。在现实估计器下，几乎所有剩余误差均源自上游。我们发现，流畅性并非正确性的证据，因为叙述质量与信号质量无关；在严重不平衡的场景中，决策质量会向多数行动靠拢。将该流水线应用于全国代表性数据集，其得出结论：高级数学课程对最不可能就读四年制大学的学生益处最大，且98.8%的叙述通过了审计，无编造的数值。最终得到一个经微调的LLM，可在普通笔记本电脑上离线完成预测与解释，学生数据无需离开设备。
英文摘要
Learning analytics increasingly relies on flexible machine learning (ML), but the model opacity and the burden of deployment prevent these tools from reaching educational practice. We propose a two-stage fine-tuning pipeline that distills a fitted black-box estimator and its post hoc interpretation (the mentor) into a small, open-weight large language model (LLM; the mentee) that returns an individual-level estimate and explains in natural language. The design is estimator-agnostic and paired with a faithfulness-first evaluation framework that audits every narration against the attribution it claims to describe. We design a simulation study that separates distillation loss from estimator loss by comparing an oracle mentor with a realistic ML mentor. Given an oracle signal, distillation with a two-billion-parameter LLM model is nearly lossless in recovering the effect surface (r > .90), perfectly ranking the important variables, and citing no spurious covariate. Under a realistic estimator, almost all remaining error originates upstream. We find that fluency is no evidence of correctness since narration quality is independent of signal quality, and decision quality collapses toward the majority action in severely imbalanced settings. Applied to a nationally representative dataset, the pipeline recovers the finding that advanced mathematics coursework benefits students least likely to enroll in four-year college the most, with 98.8% of narrations passing the audit and no fabricated quantities. The result is a single fine-tuned LLM that predicts and explains offline on a commodity laptop, so student records never leave the machine.
