Loading...
Description
Carnegie Mellon University Libraries is currently developing an innovative suite of AI-powered tools addressing two critical challenges in research information management: the adoption barrier for public researcher profiles and the balanced distribution of committee service responsibilities among faculty. Our prototype toolset leverages natural language processing and machine learning techniques to extract committee service data from faculty CVs and other unstructured sources, import this information into the Elements/Scholars@CMU platform, and analyze the data to provide recommendations for achieving greater balance in committee service workload. This presentation will share our in-progress development approach, initial prototype functionality, methodological considerations, and preliminary findings. We'll demonstrate how libraries can position themselves as essential curators of institutional data while advancing fairness initiatives through innovative applications of AI in research information management.
Keywords:
Research information management, Artificial intelligence, Faculty workload balance, Committee service, Researcher profiles
Start Date
16-9-2025 3:20 PM
End Date
16-9-2025 3:50 PM
Target Audience
This presentation is intended for RIMS administrators, library leaders, IT professionals, and university administrators. Those involved in implementing RIMS/EFS platforms, particularly where faculty engagement is a challenge, will gain the most value. The presentation will also be valuable for institutions considering innovative approaches to demonstrate ROI of their RIMS through addressing institutional priorities like fair workload distribution initiatives.
Learning Objectives
Learn about emerging approaches to leverage AI technologies for automating data extraction from unstructured sources for research information management systems.Understand strategies being developed for increasing faculty adoption of researcher profiles through automated data population.Discover methods under development for analyzing committee service data to promote more balanced distribution of faculty workloads.Explore the evolving role of libraries in advancing institutional fairness initiatives through research information management.Identify potential applications of similar AI approaches to other areas of research information management.
Recommended Citation
Glenn, Jason and Jebbia, Dom, "An AI-Powered Toolset for Research Information Management: Developing Solutions to Automate Committee Service Data and Promote Balanced Service Distribution" (2025). International Forum on Expert Finder Systems. 8.
https://efsrimsrepository.expertfindersystems.org/efs_forum/2025/fullschedule/8
An AI-Powered Toolset for Research Information Management: Developing Solutions to Automate Committee Service Data and Promote Balanced Service Distribution
Carnegie Mellon University Libraries is currently developing an innovative suite of AI-powered tools addressing two critical challenges in research information management: the adoption barrier for public researcher profiles and the balanced distribution of committee service responsibilities among faculty. Our prototype toolset leverages natural language processing and machine learning techniques to extract committee service data from faculty CVs and other unstructured sources, import this information into the Elements/Scholars@CMU platform, and analyze the data to provide recommendations for achieving greater balance in committee service workload. This presentation will share our in-progress development approach, initial prototype functionality, methodological considerations, and preliminary findings. We'll demonstrate how libraries can position themselves as essential curators of institutional data while advancing fairness initiatives through innovative applications of AI in research information management.