Bridging Borders: Global Faculty Learning Communities on AI for Transforming Teaching and Learning

Educators worldwide are experimenting with AI in their classrooms, but without structured collaboration, valuable insights remain siloed. Faculty learning communities (FLCs) provide an essential framework for cross-institutional knowledge exchange, enabling educators to share practices, co-develop AI-integrated pedagogies, and collectively address the challenges and opportunities AI presents in teaching and learning. This paper examines how global FLCs serve as incubators for AI-enhanced instructional innovation, accelerating AI adoption in higher education while fostering professional growth and pedagogical experimentation. Through an autoethnographically informed qualitative approach, the study explores how this community enabled participants to build fluency, navigate uncertainty, and influence institutional conversations about AI integration. Findings highlight how these peer-driven learning communities create structured, supportive spaces for iterative experimentation, ultimately shortening the research-to-practice cycle and equipping educators with the skills to integrate AI ethically, effectively, and creatively. This paper presents a scalable model for AI-enabled FLCs, outlining strategies for institutions to establish, sustain, and scale these collaborative ecosystems. By investing in global FLCs, institutions can move beyond isolated AI exploration toward a more connected, adaptive, and forward-thinking approach to teaching and learning.