AI-University: An LLM-based Platform for Instructional Alignment to Scientific Classrooms

AI-University: An LLM-based Platform for Instructional Alignment to Scientific Classrooms is sponsored by University of Michigan. Develops an AI-driven platform to align instructional content with teaching styles in scientific classrooms.

[2504. 08846] AI-University: An LLM-based platform for instructional alignment to scientific classrooms Computer Science > Computers and Society [Submitted on 11 Apr 2025] Title: AI-University: An LLM-based platform for instructional alignment to scientific classrooms Authors: Mostafa Faghih Shojaei , Rahul Gulati , Benjamin A.

Jasperson , Shangshang Wang , Simone Cimolato , Dangli Cao , Willie Neiswanger , Krishna Garikipati View a PDF of the paper titled AI-University: An LLM-based platform for instructional alignment to scientific classrooms, by Mostafa Faghih Shojaei and 7 other authors Abstract: 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 this https URL , 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. Computers and Society (cs. CY) ; Artificial Intelligence (cs.

AI); Computation and Language (cs. CL); Machine Learning (cs. LG) arXiv:2504.

08846v1 [cs. CY] for this version) https://doi. org/10.

48550/arXiv. 2504. 08846 arXiv-issued DOI via DataCite From: Benjamin Jasperson [ view email ] Fri, 11 Apr 2025 01:26:34 UTC (590 KB) View a PDF of the paper titled AI-University: An LLM-based platform for instructional alignment to scientific classrooms, by Mostafa Faghih Shojaei and 7 other authors BibTeX formatted citation Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer?

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