The Learning Lab experience is supported by both asynchronous and synchronous components. Each part includes a set of resources, an asynchronous discussion, and an interactive live session, all of which culminate in the development of a project to apply learning to local and specific contexts in support of the learning objectives.
Schedule
Part 1: From Purpose to Possibilities
July 7, 2025 | 12:00 noon–1:30 p.m. ET
In this session, we will consider generative AI’s potential in higher education. Thinking like a designer, we will explore the principles of AI models and compare various technologies, focusing on their practical applications. We will examine how AI can boost workplace efficiency, and explore tools for creating chatbots, customizable agents, and/or virtual assistants. Through interactive exercises, professionals from diverse higher education roles will gain practical insights into leveraging generative AI to innovate across teaching, research, and administration.
Learning Objectives:
- Describe the underlying principles and techniques used in generative AI models.
- Distinguish between different types of generative AI technologies and their respective strengths and limitations.
- Identify properties of AI that augment or amplify outcomes of the workplace.
Part 2: From Ideas to Prototypes
July 10, 2025 | 12:00 noon–1:30 p.m. ET
In this session, we will focus on designing AI tools around use cases particular to your unit and/or institution. You will identify local challenges and develop ideas for AI solutions to address them. Then, we’ll dive into the practical aspects of creating custom chatbots or AI assistants and integrating them with relevant data sources. Throughout the session, we’ll emphasize hands-on activities, allowing you to gain firsthand experience leveraging these tools. By the session’s end, you’ll have a solid foundation on which to create AI-powered chatbots or assistants ready to enhance your institution’s teaching, research, and/or administrative processes.
Learning Objectives:
- Identify specific challenges or needs within your units or institutions that could be addressed by generative AI solutions.
- Generate and evaluate ideas for how generative AI could be applied to address those challenges or needs.
- Evaluate tools and frameworks for making your own custom chatbot or AI assistant.
- Integrate custom AI solutions with relevant data sources or systems to meet specific unit or institutional needs.
Part 3: From First Draft to Second Draft
July 14, 2025 | 12:00 noon–1:30 p.m. ET
In this Learning Lab session, we’ll refine the custom chatbots or AI assistants we’ve been developing. We will reassess how our AI solutions address unit and/or institutional challenges and enhance workplace outcomes. We will improve interaction patterns and data integration and then evaluate our AI solutions using established benchmarks and institution-specific criteria. We will interpret results to identify enhancements and implement performance-boosting strategies. This hands-on session will prepare you for the final session by equipping you with skills to critically assess and improve your custom chatbots or AI assistants, setting the stage for their optimal deployment in your unit and/or institution.
Learning Objectives:
- Build interaction patterns for a custom chatbot or AI assistant.
- Analyze how custom chatbots or AI assistants can be evaluated based on benchmarks.
- Ideate potential plans for redesign and redeployment.
- Implement strategies and techniques to refine the custom chatbot or AI assistant and improve its performance.
- Interpret evaluation results and user feedback to identify opportunities for enhancement.
Part 4: From Prototype to Path
July 17, 2025 | 12:00 noon–1:30 p.m. ET
In this final session, we’ll reflect on and further refine the custom chatbots or AI assistants we’ve created for our units and/or institutions. The core of our session will focus on comprehensive evaluation: we’ll analyze our chatbots or AI assistants using established benchmarks and develop unique evaluation criteria tailored to our contexts. We’ll assess their effectiveness across multiple dimensions: utility, performance, interaction design, consistency, and potential risks.
Using these insights, we’ll then iterate on our custom chatbots or AI assistants, interpreting evaluation results and user feedback to identify areas for improvement. We’ll implement strategies to enhance performance and align outputs more closely with our organization’s needs, constraints, and visions. The session will culminate in ideating plans for future redesigns and redeployments, ensuring that our AI solutions continue to evolve with our institutions.
Learning Objectives:
- Develop evaluation criteria and metrics unique to your own unit or institution.
- Apply appropriate benchmarks to your own and/or others’ chatbots.
- Assess effectiveness on multiple levels, such as utility, performance, interaction design, consistency, and risk.
- Align custom chatbot or AI assistant output with the organization’s needs, constraints, and vision.
Lab Project/Assignments
You will apply your learning by developing a custom chatbot or AI assistant to address a specific task or challenge within your professional domain in higher education. You will document the development process, implementation strategies, and evaluation metrics, demonstrating your ability to leverage generative AI effectively in your professional contexts.