AI4RA Co-PI Luke Sheneman, director of Research Computing and Data Services at the University of Idaho, recently joined The Vandal Theory podcast to talk about how the university supports AI-powered research. The episode covers how U of I builds and governs its own AI infrastructure, and how that work connects to AI4RA’s mission of streamlining research administration.

Many research administration offices rely on data work every day, often without a clearly defined data role. In this session, AI4RA data scientists Nathan Layman (University of Idaho) and Nathan Wiggins (Southern Utah University) look at what data professionals actually do in research administration, and how offices can get more value from the data they already have. 

With the proliferation of AI across research, it is becoming more important than ever for research administrators to stay up to date on rapidly changing sponsor AI policies. Researchers are finding new ways to incorporate AI into their research, including transforming, summarizing, and extracting text from documents.

Research administrators often lean on a few core professional organizations such as NCURA, SRAI, and NORDP, to educate themselves, network with other RAs, and share their stories. Many of these organizations often provide opportunities for RAs to author materials or promote themselves through published journal articles, magazine articles, and presentations. This week’s tip provides you with their AI policies all in one place to ensure you can remain compliant while you leverage the amazing opportunities they provide. 

MindRouter and the Case for Transparent and Sovereign AI Infrastructure MindRouter is an open-source AI gateway developed by Luke Sheneman, co-PI of the AI4RA project and Director of the University of Idaho’s Research Computing and Data Services. It sits as a middle layer between tools like Vandalizer and the AI models that power them, routing…

This week’s tip introduces a simple framework for thinking about how deeply AI is integrated into your work, and what the tradeoffs are at each level.

There are a number of tasks that can be accomplished using Vandalizer. From summarizing requests for funding, verifying proposal compliance, to extracting data from documents.

Context engineering is the practice of deliberately designing an information environment for an AI to operate within. The key distinction between prompt engineering (PE) and context engineering (CE) is that PE is centered around asking smart questions while CE is about building smarter environments for the AI to work in. 

f you’re receiving this email, you’re an official member of the AI4RA Community of Practice, a listserv connecting research administrators, IT personnel, data scientists, and AI engineers from across the country. With AI evolving as fast as it does, having a community to turn to with questions, ideas, and hard-won lessons makes a real difference. 

One of the most common questions we hear from institutions exploring Vandalizer deployment is “What hardware do we actually need?” and the Cluster Configurator addresses exactly that.