AI-大學：一個基於大型語言模型的教學對齊平台，專為科學課堂設計

AI-University: An LLM-based platform for instructional alignment to scientific classrooms

April 11, 2025

作者: Mostafa Faghih Shojaei, Rahul Gulati, Benjamin A. Jasperson, Shangshang Wang, Simone Cimolato, Dangli Cao, Willie Neiswanger, Krishna Garikipati

cs.AI

摘要

我們推出AI大學（AI-U），這是一個靈活的框架，用於AI驅動的課程內容傳遞，能夠適應教師的教學風格。AI-U的核心在於利用檢索增強生成（RAG）技術對大型語言模型（LLM）進行微調，從而從講座視頻、筆記和教科書中生成與教師教學風格一致的響應。以研究生層次的有限元方法（FEM）課程為案例，我們展示了一個可擴展的流程，系統地構建訓練數據，使用低秩適應（LoRA）微調開源LLM，並通過基於RAG的合成優化其響應。我們的評估——結合餘弦相似度、基於LLM的評估和專家評審——顯示出與課程材料的強烈一致性。我們還開發了一個原型網絡應用程序，可在https://my-ai-university.com訪問，該應用通過將AI生成的響應鏈接到相關課程材料的特定部分和開放訪問視頻講座的時間戳實例，增強了可追溯性。我們的專家模型在86%的測試案例中與參考資料具有更高的餘弦相似度。LLM評判者也發現我們的專家模型在大約五分之四的情況下優於基礎Llama 3.2模型。AI-U提供了一種可擴展的AI輔助教育方法，為高等教育中的更廣泛應用鋪平了道路。在此，我們的框架已在FEM課程的背景下展示——這是一門對工程科學博士和碩士生培訓至關重要的學科。然而，這一背景是更廣泛情境中的一個具體實例：微調LLM以適應科學研究內容。

English

We introduce AI University (AI-U), a flexible framework for AI-driven course content delivery that adapts to instructors' teaching styles. At its core, AI-U fine-tunes a large language model (LLM) with retrieval-augmented generation (RAG) to generate instructor-aligned responses from lecture videos, notes, and textbooks. Using a graduate-level finite-element-method (FEM) course as a case study, we present a scalable pipeline to systematically construct training data, fine-tune an open-source LLM with Low-Rank Adaptation (LoRA), and optimize its responses through RAG-based synthesis. Our evaluation - combining cosine similarity, LLM-based assessment, and expert review - demonstrates strong alignment with course materials. We also have developed a prototype web application, available at https://my-ai-university.com, that enhances traceability by linking AI-generated responses to specific sections of the relevant course material and time-stamped instances of the open-access video lectures. Our expert model is found to have greater cosine similarity with a reference on 86% of test cases. An LLM judge also found our expert model to outperform the base Llama 3.2 model approximately four times out of five. AI-U offers a scalable approach to AI-assisted education, paving the way for broader adoption in higher education. Here, our framework has been presented in the setting of a class on FEM - a subject that is central to training PhD and Master students in engineering science. However, this setting is a particular instance of a broader context: fine-tuning LLMs to research content in science.