As colleges and universities grapple with the opportunities and tensions surrounding artificial intelligence, many institutional conversations occur without substantial student input. At Gettysburg College, we sought to address this gap by inviting students to become co-designers of our emerging AI initiatives through a series of design thinking workshops titled Designing the Future of AI at Gettysburg College. These sessions guided students through empathy-building, problem framing, ideation, and rapid prototyping activities designed to surface their experiences, concerns, hopes, and ideas regarding AI's role in teaching and learning.In this interactive workshop, participants will experience an adapted version of the process used with Gettysburg students while examining insights generated through these sessions. Attendees will leave with practical facilitation strategies, adaptable activity structures, and a replicable model for engaging students as partners in shaping institutional responses to AI. Rather than designing for students, participants will explore what becomes possible when institutions design with them.
Richard Russell is a professor of psychology at Gettysburg College. Trained in cognitive science, he discovered and named super-recognizers, people with exceptional ability to recognize faces. He is now working on humanistic, cybernetic, and cognitive science approaches to understanding... Read More →
Artificial intelligence has intensified a question many educators were already beginning to ask: What evidence truly demonstrates learning? For decades, higher education has relied on products—papers, exams, presentations, and projects—as proxies for student understanding. Generative AI challenges this assumption by making it increasingly difficult to determine whether a finished product reflects a student's own thinking. While many institutional responses have focused on detection, surveillance, or restrictions, this workshop explores a different possibility: What if AI is not simply creating an assessment problem, but revealing one that has existed all along? Drawing on experiences redesigning undergraduate and graduate courses through ungrading, authentic assessment, and AI-integrated pedagogy, participants will examine how process-centered approaches can make learning more visible while preserving student agency. Through examples of self-assessment, reflective learning narratives, revision cycles, and AI transparency practices, attendees will explore ways to shift attention from evaluating products to cultivating evidence of growth, judgment, and engagement. Participants are encouraged to bring an existing assignment or assessment practice to the workshop. Using a guided redesign framework, they will identify opportunities to incorporate reflection, process documentation, feedback loops, and authentic engagement in ways that center human learning in AI-rich environments. Situated within the liberal arts tradition, this workshop embraces the paradox that AI simultaneously challenges and clarifies the value of education. As machines become increasingly capable of generating polished outputs, the most meaningful aspects of learning may be those that are difficult to automate: curiosity, ethical reasoning, self-awareness, intellectual risk-taking, and the ability to make meaning from experience.
This workshop is for any instructor who has sat in front of a paper wondering whether the words on the page truly represent the work of the student who submitted it. How do we preserve the valuable intellectual work fostered by extended writing projects while maintaining confidence that the submissions we receive reflect what students can actually do? Some instructors seek certainty by moving most writing into the classroom; this workshop explores another approach: building confidence in out-of-class work through low-stakes in-class activities that provide meaningful checkpoints for student capabilities. Starting from the question “What evidence would help me determine whether this student wrote this paper?,” participants will identify the evidence they wish they had and work backward to design activities that generate it. Participants will leave with a collaboratively developed collection of activities that offer snapshots of current student abilities while doubling as scaffolding for learning.
In a world where students are increasingly expected to graduate with AI experience, providing equitable, affordable and safe access to AI models is becoming a tricky problem for colleges and universities. This session will cover the creation of ChatGBC, Gettysburg College's OpenWebUI/OpenRouter based AI hub. ChatGBC gives all campus members access to multiple AI models plus a built in RAG knowledge base, maintains control of the data submitted to AI models and is reasonably affordable and supportable for a small liberal arts college. We'll cover initial setup, beta testing, scaling for the entire campus, initial faculty and student feedback and data on costs and support requirements.
