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Date of Award
5-6-2026
Document Type
Video Recording
Abstract
Research Information Management Systems (RIMS) house rich, structured data on faculty expertise, yet they remain underutilized for fostering multidisciplinary collaborations in grants, mentoring, and related initiatives. The Office of Scholarly Communications at Texas A&M is piloting an approach that leverages profiles from the Scholars@TAMU RIMS, combined with data from grant calls and institutional funding programs, as inputs to large language models to surface unexpected collaboration opportunities. In partnership with researchers, the team is evaluating and refining this methodology in practice. This presentation shares preliminary findings, along with code and supporting resources to facilitate replication and further study.
Recommended Citation
Creel, James; Meija, Ethel; and Xiao, Daniel, "Multidisciplinary Research Proposal Enhancements through Large Language Models" (2026). 2026 Southeastern Conference (SEC) AI Library Summit. 6.
https://scholarsjunction.msstate.edu/sec-ai-2026/6
