Join us for AI and the Student Experience: Leadership Applications and Measurable Impact, part of the EDUCAUSE Leadership Series. In this three-part experience, synchronous sessions and asynchronous content are combined to support deep, applied learning. Through case studies and examples from higher education, participants will gain practical insights into how institutions navigate the intersection of AI and impactful student experiences.
NOTE: Each session builds on the previous one. Participants are encouraged to engage in all three to maximize their learning.
Schedule
- Session 1: July 20, 2026 | 12 noon–1:00pm ET
- Session 2: July 22, 2026 | 12 noon–2:00pm ET
- Session 3: July 27, 2026 | 12 noon–2:00pm ET
Sessions
Session 1 | Understanding the AI Landscape in Student Success
Drawing on EAB’s latest research and a broad view across institutions nationwide, this session offers a “state of the union” on how AI is shaping the student experience in higher education. As adoption accelerates across campuses, many institutions are still working to align around shared strategies and support for student success teams.
Tara Zirkel will share a national scan of how AI is being used, who is driving adoption, and how approaches are evolving. Attendees will leave with a clearer understanding of how to build a culture that supports effective AI use in student success—through intentional strategy, change management, and stronger partnership between leadership and frontline teams.
Learning Outcomes:
- Identify key national trends shaping how institutions are applying AI to enhance the student experience.
- Recognize common gaps and priority areas institutions should address to move from experimentation to meaningful impact.
- Understand what student success teams need from institutional leadership, such as guardrails, guidance, and support to feel confident and effective using AI in their day-to-day work.
Session 2 | Applying AI to Improve the Early Student Experience
The earliest moments of the student journey often carry the highest stakes and the greatest opportunity for impact. From initial engagement through early academic decisions, students navigate complex systems that can either support or stall their progress.
In this session, you’ll explore how institutions use AI to reduce friction, improve clarity, and support stronger momentum, and learn what leaders should consider when prioritizing and implementing these efforts. Through case-study examples and lessons from implementation, presenters will share how they approach the design and rollout of AI-enabled supports in the early student experience, such as advising bots, AI-enabled registration processes, and more, along with key considerations, tradeoffs, and what they are learning along the way.
You’ll leave with a clearer sense of where AI can most effectively improve the early student experience and how to prioritize and align these efforts with broader strategy and operations that influence the student experience.
Learning Outcomes:
- Identify where AI can improve early student experience and decision-making.
- Understand key design considerations for implementing AI in early-stage student support.
- Connect early-stage AI efforts to broader institutional strategy and the student experience.
Session 3 | Scaling AI for Impact Across the Student Experience
As institutions move beyond early experimentation with AI, leaders face a new challenge: determining what is actually improving the student experience over time and contributing to persistence and completion, and how to scale those efforts across systems and teams.
This session focuses on how institutions are evolving AI-enabled approaches to support ongoing student success. Presenters will explore examples such as AI-enabled advising and support tools, personalized outreach, student-facing AI agents, and coordinated support systems to refine their approaches and build the structures and coordination needed to support responsible, long-term use.
You’ll gain clear examples of what it takes to move from isolated use cases to sustained, institution-wide impact on the student experience and the perspective to lead and scale these efforts in your own context.
Learning Outcomes:
- Assess AI initiatives using meaningful indicators of the student experience and related outcomes.
- Evaluate considerations and tradeoffs when scaling AI-enabled support across the student journey.
- Explore approaches to governance, risk, and responsible use.
- Identify strategies for ongoing refinement and continuous improvement.