The Learning Lab experience is supported by both asynchronous and synchronous components. Each Learning Lab sequence includes a set of resources, an asynchronous discussion, and an interactive live session, all of which culminate in the development of a project or application to apply learning to local and specific contexts in support of the learning objectives.
Schedule
Part 1: Where Can AI Assistants Actually Help?
July 7, 2026, 12:00–1:30 p.m. ET
Many institutions are experimenting with generative AI. However, identifying meaningful and responsible use cases remains a challenge. In this session, participants will explore how custom AI assistants can support teaching, learning, and operational workflows. Through discussion and examples, we will examine what makes a use case valuable, sustainable, and aligned with institutional priorities. Participants will begin identifying opportunities within their own contexts where an AI assistant could provide real benefit.
Learning Objectives:
- Explore emerging use cases for AI assistants in higher education.
- Identify a real workflow, service, or task where an AI assistant could add value.
- Select a potential use case to explore throughout the Lab.
Part 2: Designing an Assistant That Does Something Useful
July 9, 2026, 12:00–1:30 p.m. ET
With potential use cases identified, participants will learn a framework for the design of their custom AI assistant. We will explore practical design considerations including scope, prompts, knowledge sources, and interaction patterns. Participants will draft a system prompt and either draft or identify existing knowledge files if applicable.
Learning Objectives:
- Define the purpose, audience, and scope of their assistant, along with any relevant guardrails that need to be included in the design.
- Explore how prompts, instructions, and knowledge sources shape assistant behavior.
- Draft initial system prompt.
Part 3: Making Your Assistant More Reliable and Effective
July 14, 2026, 12:00–1:30 p.m. ET
Initial prototypes rarely work perfectly. In this session, participants will learn to test and refine their assistants by improving prompts, adjusting interaction patterns, and evaluating how well the tool supports its intended task. We will introduce practical techniques for improving assistant performance and ensuring responses are useful, consistent, and aligned with the intended purpose.
Learning Objectives:
- Test and evaluate their assistant’s responses and behavior.
- Apply practical techniques to improve reliability and usefulness.
- Refine the assistant to better support its intended task.
Part 4: Preparing Your Assistant for Real-World Use
July 16, 2026, 12:00–1:30 p.m. ET
In the final session, participants will evaluate their assistants through the lens of real-world use. We will consider responsible deployment, guardrails, evaluation criteria, and how assistants might evolve over time. Participants will share their prototypes and identify next steps for expanding, testing, or sharing their assistants within their institutions.
Learning Objectives:
- Consider governance, guardrails, and responsible use.
- Identify criteria for evaluating assistant effectiveness.
- Outline next steps for testing or sharing their assistant concept.
Lab Implementation Project
Throughout the Learning Lab, participants will explore and prototype a custom AI assistant concept that supports a real task, service, or workflow at their institutions.
The final project is a short concept outline and working prototype that includes:
- the problem or task the assistant is designed to support;
- the intended users and value it provides;
- a working prototype or design outline of the assistant;
- key considerations for responsible use and implementation; and
- potential next steps for testing or expanding the assistant.
Participants will leave with a practical assistant concept they can continue developing, testing, or sharing with colleagues.