AI-University: An LLM-based platform for instructional alignment to scientific classrooms
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909576340701184 |
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| author | Shojaei, Mostafa Faghih Gulati, Rahul Jasperson, Benjamin A. Wang, Shangshang Cimolato, Simone Cao, Dangli Neiswanger, Willie Garikipati, Krishna |
| author_facet | Shojaei, Mostafa Faghih Gulati, Rahul Jasperson, Benjamin A. Wang, Shangshang Cimolato, Simone Cao, Dangli Neiswanger, Willie Garikipati, Krishna |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_08846 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI-University: An LLM-based platform for instructional alignment to scientific classrooms Shojaei, Mostafa Faghih Gulati, Rahul Jasperson, Benjamin A. Wang, Shangshang Cimolato, Simone Cao, Dangli Neiswanger, Willie Garikipati, Krishna Computers and Society Artificial Intelligence Computation and Language Machine Learning 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. |
| title | AI-University: An LLM-based platform for instructional alignment to scientific classrooms |
| topic | Computers and Society Artificial Intelligence Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2504.08846 |