Date
Publisher
arXiv
Generative AI tools are being widely taken up by students in their physics courses and beyond, often before instructors and institutions can develop policies and effective approaches for the use of these tools. Building on a framework for change in the era of AI, we developed and implemented a faculty learning community to help a university physics department address these challenges collectively. Over six biweekly sessions, faculty worked through course policies, classroom conversations about AI, AI-integrated coursework tasks, and assessment. Each session shared a common structure: we presented local data and department-sourced materials, tested them in small groups, and discussed them together, emphasizing durable pedagogical approaches over specific tools and platforms, and leading with evidence of students' own AI use. The series produced a shared, evolving repository of resources for faculty to draw on. This workshop provides an adaptable, theoretically informed model for a faculty learning community that departments can build on.
Study at a glance
- Question: How can a faculty learning community support a university physics department in addressing generative AI?
- Setting and data: University of Colorado Boulder Physics Department, Spring 2026, 19 participating faculty across six workshop sessions, plus student surveys (about 350 undergraduates).
- Method: Case study of a faculty learning community using participant reflections, surveys, and curricular artifacts.
Why it matters
Generative AI tools are entering classrooms before educators have clear guidance, preparation, or an established evidence base. Institutions and departments face the challenge of responding collectively to help instructors adapt teaching and policies.
Key findings
- Faculty most valued collectively airing a shared challenge, reviewing cross-course student data, and surfacing concerns, with all participants expressing interest in continuing the learning community.
- Student surveys across five courses showed most students used AI for supportive tasks like checking solutions rather than cheating, shifting faculty discussions from policing toward collaborative guidance.
- Testing AI on real student work revealed that chatbot feedback was uneven, prompted shallow engagement without explicit structure, and required instructors to actively model productive strategies.
Summary drafted with AI from the full paper and may contain errors. See the full paper for details.
What is the application?
Who age?
Why use AI?
Study design
