Event Experience



Join us for the Leading AI Adoption event, part of the EDUCAUSE Leadership Series. This three-week experience combines synchronous sessions, asynchronous content, and collaborative activities to support deep, applied learning. Through real-world case studies and examples from higher education, participants will gain practical insights into how institutions are navigating AI adoption—balancing innovation with ethics and alignment to institutional mission. NOTE: Each session builds on the previous one—participants are encouraged to engage in all four to maximize their learning.

Schedule

  • Theme 1: February 2, 12:00 noon–1:30 p.m. ET
  • Theme 2: February 4, 12:00 noon–1:30 p.m. ET
  • Theme 3: February 9, 12:00 noon–1:30 p.m. ET
  • Closing Session: February 11, 12:00 noon–1:30 p.m. ET

Themes

Session 1 | Building Responsible AI Governance and Practice Frameworks

This session will focus on developing scalable and adaptable governance and guidance models that ensure the responsible use of AI. Participants will examine collaborative decision-making structures, cross-functional leadership models, and principles for aligning AI adoption with institutional values.

Learning Outcomes:

  • Evaluate governance frameworks that promote transparency, accountability, and ethical AI implementation.
  • Apply best practice guidelines to guide responsible AI use across institutional functions.
  • Develop strategies for cross-functional collaboration and inclusive stakeholder engagement.

Session 2 | Operationalizing AI Through Professional Development and Infrastructure Planning Part 1

This session will explore how to build internal capacity for AI adoption through professional development, resource planning, and infrastructure design. Case studies will showcase how institutions are fostering AI literacy and aligning operational practices with long-term strategic goals.

Learning Outcomes:

  • Explore professional development approaches that increase AI awareness, skills, and engagement.
  • Design operational strategies that support sustainable, scalable AI integration.
  • Align infrastructure planning with evolving institutional needs and the AI landscape.

Session 3 | Operationalizing AI Through Professional Development and Infrastructure Planning Part 2

This session will explore how to build internal capacity for AI adoption through professional development, resource planning, and infrastructure design. Case studies will showcase how institutions are fostering AI literacy and aligning operational practices with long-term strategic goals.

Learning Outcomes:

  • Explore professional development approaches that increase AI awareness, skills, and engagement.
  • Design operational strategies that support sustainable, scalable AI integration.
  • Align infrastructure planning with evolving institutional needs and the AI landscape.

Session 4: Sustaining AI Innovation: Lifecycle Planning, Communication, and Culture

In the final session, participants will examine the full lifecycle of AI implementation—from pilots to scale—focusing on continuous improvement, communication strategies, and cultural alignment. The session will emphasize building trust and transparency through iterative feedback loops and strategic communication.

Learning Outcomes:

  • Use AI lifecycle frameworks to guide implementation, scaling, and ongoing evaluation.
  • Develop communication strategies that foster transparency, trust, and inclusive participation.
  • Support a responsible AI culture by integrating ethical considerations into daily practices and decision-making processes.