16 University of Idaho AI for Research Administration
The University of Idaho has an NSF-funded grant project to explore research administration.
Project Overview
Research Administration (RA) plays an essential role in the national research enterprise, yet disparities in supporting infrastructure are widening across institutions. The workload for managing federally funded research has surged due to dramatic increases in compliance requirements, yet administrative cost recovery has been capped since 1991. Most universities, particularly emerging research institutions (ERIs), minority-serving institutions (MSIs), and primarily undergraduate institutions (PUIs), struggle with aging information systems, data silos, and resource constraints.
As a Carnegie R2 institution, the University of Idaho (UI) continues to grow and adapt to the myriad of challenges that face the RA community. Through networking opportunities with Southern Utah University (SUU), a PUI, and other entities including ERIs and MSIs, we have found that they too face similar challenges compounded by variability in data quality, electronic RA and financial systems, and software interfaces. A commonality amongst these research administration entities is that we are often under-resourced and do not have the capabilities to hire more staff, independently build tools and systems, or to pay for an out-of-the-box solution to mitigate these challenges. To address these issues, our proposal team has spent the prior year investigating how to best develop impactful tools using Artificial Intelligence (AI) and data science to increase efficiencies, lower costs, and augment existing staff. These tools bolster RA capabilities that support our growing portfolio and some are ready for deployment. However, our proposed methods will require cultivating a community of practice to achieve sustainable solutions with broader impacts.
To support a community of practice and engage in building more robust RA infrastructure, we have formulated three main objectives for our proposal. These objectives will address common challenges within RA units of varied sizes and abilities. Firstly, we aim to overcome the persistent problems of data accessibility, interoperability, and reusability by creating an open-source data infrastructure. Secondly, we plan to utilize advancements in artificial intelligence (AI) to revolutionize RA practices. This will be achieved by developing a set of open-source AI tools specifically designed for research administration, benefiting the lead applicant entity (UI), our sub-awardee entity (SUU), and other partner organizations involved in this iterative effort. Thirdly, we will ensure that the open-source tools and data infrastructure we create are transferable, scalable, and impactful. This will be accomplished by fostering a community of practice through collaborative development and ensuring the sustainability of our initiatives. To gauge the effectiveness of our efforts, we will implement both quantitative and qualitative impact measurement tools. Additionally, we will seek guidance from an External Advisory Committee (EAC) to ensure that our outcomes are impactful, sustainable, and appropriately scaled to the distinct needs of our partner entities.
