AI Hub
Artificial Intelligence

Innovation & Pilots

Experimentation

Innovation & Pilots

Explore active AI pilots, learn from completed experiments, and propose your own. UMS encourages a culture of experimentation within clear data-security guardrails.

Case Studies

UMA
Active

UMA: $400K Davis Foundation Grant for AI & VR

Project period2026–2028 $400,000

Outcomes & Metrics

  • 20+ courses redesigned with AI/VR integration
  • New VR lab infrastructure
  • Faculty professional development program
  • Student learning outcomes assessment framework
AI Integration VR Instruction Course Redesign Grant-Funded
System-Wide
Completed

Scribehow Process Documentation Pilot

Project period2024–2025 Licensed

Outcomes & Metrics

  • 31 active users across multiple campuses
  • Hundreds of SOPs documented automatically
  • Significant reduction in documentation time
  • Improved onboarding for new staff
Process Documentation Administrative Efficiency SOP Creation
System-Wide
Active

ChatGPT Team Deployment

Project period2024–Present Annual License

Outcomes & Metrics

  • 175 active users (largest tool adoption)
  • Cross-campus deployment model established
  • Data security protocols validated
  • User satisfaction surveys conducted
Enterprise AI Writing Research Code Generation

Lessons Learned

Multi-Tool Strategy Over Single Vendor

Procurement

Adoption is Broad but Shallow

Adoption

Faculty Autonomy Drives Engagement

Pedagogy

Data Classification is a Prerequisite

Security

Video Demos & Spotlights

Active Pilots

UMA AI/VR Course Redesign

Recruiting

Windsurf Code Development

Active

Replit Educational Coding

Active

Copilot M365 Integration

Evaluating

Propose a Pilot

Propose a Pilot

Have an idea for an AI pilot? Submit a lightweight proposal and the AI Task Force will review it for feasibility, alignment, and resource requirements.

Describe the pilot: what AI tool or approach, who would participate, what you hope to learn, and any resource needs.
  • Clear problem statement and measurable goals
  • Defined scope and timeline (typically one semester)
  • Identified participants and stakeholders
  • Data security considerations addressed
  • Alignment with UMS AI strategic priorities
  • Plan for measuring outcomes and sharing results