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Find similar grantsAI-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.
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[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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According to the current listing, eligibility includes: University of Michigan faculty. Confirm the full requirements in the official notice before applying.
AI-University: An LLM-based Platform for Instructional Alignment to Scientific Classrooms is funded by University of Michigan. Verify program details on the funder's official page before applying.
This opportunity targets applicants in Michigan. If your organization operates elsewhere, check the official notice for location requirements.
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UROP Student Success Grant is a fellowship from the University of Michigan's LSA Undergraduate Research Opportunity Program that supports advanced undergraduate students transitioning into Junior Research Leads within their ongoing faculty-mentored research projects. The award provides a $2,000 per semester student stipend (for an average of 10-12 hours of research per week), a $2,000 annual mentor discretionary fund for supplies and software, and access to travel grants for presenting at national conferences. Eligible applicants are U-M undergraduates who have completed at least four semesters of research with their current mentor and are committed to continuing through graduation. Faculty-student pairs must submit a Joint Mentorship Transition Plan. The 2026 application deadline was April 6, 2026.
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Educational Technology, Media, and Materials for Individuals with Disabilities Program (Stepping-up Technology Implementation competition) is sponsored by U.S. Department of Education. This program aims to improve results for students with disabilities by promoting the development, demonstration, and use of technology; supporting educational activities of value in the classroom for students with disabilities; providing captioning and video description; and ens…
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