AI Planning and Activities

Developed in NebulaONE with partner CloudForce, CLIPPY is an AI-assisted support tool designed to strengthen Institutional Review Board (IRB) applications before formal submission. 
Purpose: Provides immediate, UMB-specific feedback to assess and improve the quality, completeness, and submission of applications. 

Audience: UMB investigators, researchers, research coordinators, IRB analysts, and internal research teams.

Taskforce Members: Research & Scholarship AI Task Force members Emilie Ludeman, Christopher Webster, Dustin Lee, Michelle Medeiros, George Unick, Hanae Saito, Sherry Bohn, Casey Jackson, Vondel Mahon; liaisons Emily Hurst.

Current Status: In pilot testing with internal IRB teams to ensure accuracy and workflow fit. Human review remains central and CLIPPY assists, but never replaces, formal IRB decisions. 

Building on the CLIPPY model, UMB and CloudForce are developing a similar AI-assisted tool tailored specifically for Institutional Animal Care and Use Committee (IACUC) workflows. 

Purpose: Helps animal-research teams catch potential issues early, boost submission quality, and cut down on administrative back-and-forth. 

Audience: UMB investigators, researchers, research coordinators, IACUC analysts, and internal research teams. 

Taskforce Members: Research & Scholarship AI Task Force members Emilie Ludeman, Christopher Webster, Dustin Lee, Michelle Medeiros, George Unick, Hanae Saito, Sherry Bohn, Casey Jackson, Vondel Mahon; liaisons Emily Hurst. 
Current Status: Currently in active development with CloudForce. 

An institution-wide advisory tool designed to centralize UMB teaching resources and provide source-linked guidance on instructional practices, academic policies, and faculty development. 

Purpose: Delivers timely, evidence-informed advice on instructional design, active learning, and assessment, while connecting faculty with FCTL experts for specialized consultation. 

Audience: UMB faculty (new, adjunct, clinical, experienced) and Faculty Center for Teaching and Learning (FCTL) support teams. 

Taskforce Members: Teaching and Learning AI Task Force members Benjamin Yelin, Philip Dittmar, Eric Belt, Patricia Tordik, Cheryl Fisher, Shannon Tucker, Sarah Murthi, MJ Bondy, Cory Stephens, and Paul Sacco; liaisons Jenny Owens and Susan Bindon 

Current Status: Pre-planning has started. 

An institution-wide advisory tool designed to centralize UMB teaching resources and provide source-linked guidance on instructional practices, academic policies, and faculty development. 

Purpose: Delivers timely, evidence-informed advice on instructional design, active learning, and assessment, while connecting faculty with FCTL experts for specialized consultation. 

Audience: UMB faculty (new, adjunct, clinical, experienced) and Faculty Center for Teaching and Learning (FCTL) support teams. 

Taskforce Members: Teaching and Learning AI Task Force members Benjamin Yelin, Philip Dittmar, Eric Belt, Patricia Tordik, Cheryl Fisher, Shannon Tucker, Sarah Murthi, MJ Bondy, Cory Stephens, and Paul Sacco; liaisons Jenny Owens and Susan Bindon 

Current Status: Pre-planning has started. 

A coordinated suite of AI-supported tools for minimum-qualification screening, candidate matching and internal talent identification, and hiring-policy and compliance guidance. 

Purpose: Speeds hiring and improves consistency, equity, compliance, and audit readiness while preserving HR and hiring-manager decision-making. 

Audience: HR professionals, recruiters, hiring managers, department administrators, the Office of International Services, applicants, and UMB employees. 

Taskforce Members: Administrative Efficiency AI Task Force members Kevin Donegan, Luke Quell, Ethan Donnelly, Semhar Okbazion, Mark Wozniak, Scott Stefan, Angie Hines, Frank Lancaster, Jussi Isojarvi, Diana Vass, Jon Bratt; liaisons Malika Monger and Peter Murray. 

Current Status: Will begin once HCM goes live. 

A centralized, AI-powered "front door" navigation agent that interprets user intent to seamlessly connect students, faculty, and staff with the right UMB resources, offices, announcements, and school-specific agents across all seven schools. 

Purpose: Streamlines institutional wayfinding and reduces misrouted inquiries by providing 24/7, context-aware routing across decentralized campus systems—improving service visibility while preserving staff capacity for complex, high-touch support. 

Audience: Prospective and current students (including working adults and online learners), faculty, administrative and research staff, and central departments seeking to improve service discoverability. 

Taskforce Members: Student Services AI task force members Isabell May, Margo Stebbins, Heather Moore, Sheena Jackson, Nakiya Schurman, Alexis Guethler, Sam-Saunders-Consroe, James Mszanski, Kayla Kozak, Meghan Fields; liaison Alex Hortis. 

Current Status: To be started. 

A clinical AI governance framework that makes dataset representativeness a required consideration during the selection, development, validation, deployment, and monitoring of AI tools. 

Purpose: Helps ensure clinical AI is fair, safe, reliable, and equitable across diverse patient populations through consistent assessment and monitoring practices. 

udience: Clinicians, clinical informatics and data-science teams, AI governance and compliance teams, and patients who benefit from equitable care. 

Taskforce Members: Clinical Care AI Task Force members Nileshkumar Dubey, Qoot Aklhubaizi, David Bunnell, Michelle Pearce, Veronica Quattrini, Shivakumar Narayanan, Galina Arbitman, Kesha Johnson, Seung Kee Choi; liaisons Yolanda Ogbolu 

Current Status: To be started.