A Framework for AI Educator Identity: Bridging Pedagogy, Disciplinary Expertise, and Emerging Technologies

• In Event: Teaching and Learning with AI: Building Literacy, Agency, and Design.

Abstract

This study proposes a six-dimensional framework of teachers’ AI educator identity by extending Carlone and Johnson’s (2007) science identity model and incorporating insights from Galanti and Holincheck’s (2022) work on integrated STEM teacher identity. Drawing on interviews with seven teachers across three schools in three U.S. states, we identified distinct types of AI educator identities shaped by teachers’ competence, performance, recognition, value, interest, and insight. Our findings illustrate the diverse ways teachers engage with AI as both a pedagogical focus and a component of their professional identity. This framework not only advances conceptual understandings of teacher identity in the context of emerging technologies but also offers a foundation for developing instruments to assess and support AI educator identity development at scale.

Authors

• Feiwen Xiao, University of Pennsylvania

• Qiuqing Li, North Carolina State University

• Rebecca Ellis, The Concord Consortium

• Jie Chao, The Concord Consortium

• Carolyn Penstein Rose, Carnegie Mellon University

• Shiyan Jiang, North Carolina State University