Distilling Black-Box Machine Learning into a Small, Self-Explaining Language Model for Learning Analytics

A novel two-stage fine-tuning pipeline is proposed for distilling complex, opaque machine learning (ML) models used in learning analytics into smaller, open-weight large language models (LLMs). This process aims to enable these LLMs to provide individual-level estimates and natural language explanations, addressing challenges of model opacity and deployment burden in educational practice. The approach is designed to be estimator-agnostic and includes a faithfulness-first evaluation framework for narrations.

This research introduces a method to enhance the interpretability and deployability of advanced machine learning in critical domains like education. By making complex models explainable and more accessible, it can foster greater trust and adoption of AI-driven tools, thereby improving decision-making and operational efficiency where data insights are crucial.

A two-stage fine-tuning pipeline distills black-box ML estimators and their post hoc interpretations into small, open-weight LLMs.

Forward consideration, not a verified fact.