Collections-Based Teaching: AI Resilient or AI Supported?

Instructors are increasingly drawn to collections-based teaching because it appears to be immune to the impact of AI, leaving students with a hands-on engagement with material culture. At the same time, AI has been introduced into scholarly approaches to materiality, specifically in support of traditional research. This workshop invites participants to think through the goals of an AI-resilient model of collections-based teaching while weighing the affordances of technologically inflected approaches to collections, so that they can make their decisions transparent to their students. 

Led by:

• Gina Marie Hurley, PhD, Associate Director of Graduate and Postdoctoral Teaching Development, Poorvu Center for Teaching and Learning
• Hannelore Segers, PhD, Early Materials Cataloger, Beinecke Rare Book and Manuscript Library

Key Questions:

What does this technology allow us to do that we currently can’t? As instructors, how can we guide students toward or away from use of these tools, and a better understanding of their limitations and benefits? Does it interfere with students’ skill-acquisition and understanding of materiality, or does it offer ways to reinforce them?  What are specific contexts in which AI-use can be considered to be useful or detrimental? 

Goals:  

• Explore the idea that collections-based teaching is AI-resilient 
• Consider our goals in introducing material culture to students 
• Through a case study (Gemini), consider whether AI can be used ethically in the space of collection-based teaching, without displacing goals around materiality and textual literacy 
• Revisit the question of the relationship between collections-based teaching and AI resilience with a more nuanced perspective