AI-University: An LLM-based platform for instructional alignment to
  scientific classrooms
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.
67ff25f33026f8abc4c4a10d
Mostafa Faghih Shojaei
claimed_verified
2025-04-17T08:05:09.385Z
67a1d948cb80e1b62e62a706
https://cdn-avatars.huggingface.co/v1/production/uploads/67a1d948cb80e1b62e62a706/R-VfYGh-H_vxhuwbHUAHg.png
Mostafa Shojaei
user
mostafa-sh
67ff25f33026f8abc4c4a10e
Rahul Gulati
claimed_verified
2025-04-25T12:27:32.801Z
666b45b0f036ffb1de2b2c4b
/avatars/ee3db4e7b4cb9ce4915ebdf3d3dc8886.svg
Rahul Gulati
user
rahulgulati
67ff25f33026f8abc4c4a10f
Benjamin A. Jasperson
67ff25f33026f8abc4c4a110
Shangshang Wang
claimed_verified
2025-04-23T08:37:43.839Z
67469d6a8407f929491dce06
https://cdn-avatars.huggingface.co/v1/production/uploads/67469d6a8407f929491dce06/7vd7zyApmT4rXe58gqmG1.png
Shangshang Wang
user
upup-ashton-wang
67ff25f33026f8abc4c4a111
Simone Cimolato
claimed_verified
2025-04-16T08:42:55.794Z
6729342804227d5ea3b283c1
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/_GPZiRH3LA39QleNtRqYH.png
Simone Cimolato
user
simocimolato
67ff25f33026f8abc4c4a112
Dangli Cao
claimed_verified
2025-04-16T09:23:14.535Z
672ad19141a93b8e140e8689
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/VRMw7_PIE5QoD_eWP0hOp.png
Dangli Cao
user
Dinzhenzhenzhu
67ff25f33026f8abc4c4a113
Willie Neiswanger
claimed_verified
2025-04-18T09:40:22.985Z
644bf65522d211df6444a7f4
https://cdn-avatars.huggingface.co/v1/production/uploads/644bf65522d211df6444a7f4/k_ddZdQDg2fzhwjI1EXyx.jpeg
Willie Neiswanger
user
willieneis
67ff25f33026f8abc4c4a114
Krishna Garikipati
extracted_confirmed
2025-04-16T05:32:49.978Z
67859d73e670c62966ba5767
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/Mx3fE-YPsjsxpYsBh_DDn.png
Krishna Garikipati
user
garikipati
AI-University: An LLM-based platform for instructional alignment to
  scientific classrooms
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.
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.
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