title: AI-University: An LLM-based platform for instructional alignment to
    scientific classrooms
summary: 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.
paper_authors._id: 67ff25f33026f8abc4c4a10d
paper_authors.name: Mostafa Faghih Shojaei
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-17T08:05:09.385Z
paper_authors._id: 67a1d948cb80e1b62e62a706
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/67a1d948cb80e1b62e62a706/R-VfYGh-H_vxhuwbHUAHg.png
paper_authors.fullname: Mostafa Shojaei
paper_authors.type: user
paper_authors.user: mostafa-sh
paper_authors._id: 67ff25f33026f8abc4c4a10e
paper_authors.name: Rahul Gulati
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-25T12:27:32.801Z
paper_authors._id: 666b45b0f036ffb1de2b2c4b
paper_authors.avatarUrl: /avatars/ee3db4e7b4cb9ce4915ebdf3d3dc8886.svg
paper_authors.fullname: Rahul Gulati
paper_authors.type: user
paper_authors.user: rahulgulati
paper_authors._id: 67ff25f33026f8abc4c4a10f
paper_authors.name: Benjamin A. Jasperson
paper_authors._id: 67ff25f33026f8abc4c4a110
paper_authors.name: Shangshang Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-23T08:37:43.839Z
paper_authors._id: 67469d6a8407f929491dce06
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/67469d6a8407f929491dce06/7vd7zyApmT4rXe58gqmG1.png
paper_authors.fullname: Shangshang Wang
paper_authors.type: user
paper_authors.user: upup-ashton-wang
paper_authors._id: 67ff25f33026f8abc4c4a111
paper_authors.name: Simone Cimolato
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-16T08:42:55.794Z
paper_authors._id: 6729342804227d5ea3b283c1
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/_GPZiRH3LA39QleNtRqYH.png
paper_authors.fullname: Simone Cimolato
paper_authors.type: user
paper_authors.user: simocimolato
paper_authors._id: 67ff25f33026f8abc4c4a112
paper_authors.name: Dangli Cao
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-16T09:23:14.535Z
paper_authors._id: 672ad19141a93b8e140e8689
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/VRMw7_PIE5QoD_eWP0hOp.png
paper_authors.fullname: Dangli Cao
paper_authors.type: user
paper_authors.user: Dinzhenzhenzhu
paper_authors._id: 67ff25f33026f8abc4c4a113
paper_authors.name: Willie Neiswanger
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-18T09:40:22.985Z
paper_authors._id: 644bf65522d211df6444a7f4
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/644bf65522d211df6444a7f4/k_ddZdQDg2fzhwjI1EXyx.jpeg
paper_authors.fullname: Willie Neiswanger
paper_authors.type: user
paper_authors.user: willieneis
paper_authors._id: 67ff25f33026f8abc4c4a114
paper_authors.name: Krishna Garikipati
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-04-16T05:32:49.978Z
paper_authors._id: 67859d73e670c62966ba5767
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/Mx3fE-YPsjsxpYsBh_DDn.png
paper_authors.fullname: Krishna Garikipati
paper_authors.type: user
paper_authors.user: garikipati
paper_title: AI-University: An LLM-based platform for instructional alignment to
    scientific classrooms
paper_summary: 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.
paper_ai_summary: A flexible AI framework adapts large language models with retrieval-augmented generation for personalized course content delivery, demonstrating strong alignment and improved performance in a graduate-level FEM course.
paper_ai_keywords: large language model retrieval-augmented generation fine-tuning Low-Rank Adaptation LLM cosine similarity AI-generated responses time-stamped instances open-access video lectures expert review LLM judge Llama 3.2 model